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Fix projected dip-means #114

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Fix projected dip-means #114

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27 changes: 16 additions & 11 deletions clustpy/partition/projected_dipmeans.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,13 +4,14 @@
"""

import numpy as np
from sklearn.decomposition import PCA
from clustpy.utils import dip_test, dip_pval
from sklearn.base import BaseEstimator, ClusterMixin
from clustpy.partition.xmeans import _initial_kmeans_clusters, _execute_two_means
from clustpy.utils.checks import check_parameters
from sklearn.utils.validation import check_is_fitted
from sklearn.decomposition import PCA
from sklearn.metrics.pairwise import pairwise_distances_argmin_min
from sklearn.utils.validation import check_is_fitted

from clustpy.partition.xmeans import _execute_two_means, _initial_kmeans_clusters
from clustpy.utils import dip_pval, dip_test
from clustpy.utils.checks import check_parameters


def _proj_dipmeans(X: np.ndarray, significance: float, n_random_projections: int, pval_strategy: str, n_boots: int,
Expand Down Expand Up @@ -64,14 +65,18 @@ def _proj_dipmeans(X: np.ndarray, significance: float, n_random_projections: int
cluster_dips = np.array([dip_test(projected_data[:, p], just_dip=True, is_data_sorted=False) for p in
range(projected_data.shape[1])])
# Calculate p-values of maximum dip
pval = dip_pval(np.max(cluster_dips), ids_in_cluster.shape[0], pval_strategy=pval_strategy, n_boots=n_boots,
max_dip = np.max(cluster_dips)
pval = dip_pval(max_dip, ids_in_cluster.shape[0], pval_strategy=pval_strategy, n_boots=n_boots,
random_state=random_state)
# Calculate cluster score
cluster_scores[c] = pval
# Get cluster with minimum pval
cluster_id_to_split = np.argmin(cluster_scores)

if pval < significance:
# Save cluster multimodality score
cluster_scores[c] = max_dip

# Get cluster with maximum multimodality score
cluster_id_to_split = np.argmax(cluster_scores)
# Check if any cluster has to be split
if cluster_scores[cluster_id_to_split] < significance:
if cluster_scores[cluster_id_to_split] > 0:
# Split cluster using bisecting kmeans
labels, centers, _ = _execute_two_means(X, ids_in_each_cluster, cluster_id_to_split, centers,
n_split_trials, random_state)
Expand Down
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