pecok poc on point clustering & sbm

tests/test_pointclustering.py

@@ -0,0 +1,24 @@
+import os
+import sys
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+import numpy as np
+from sklearn import cluster
+from pecok import pecok_clustering
+
+seed = 432
+np.random.seed(seed)
+print("seed is %i" % seed)
+
+n_samples = 10
+n_features = 100
+truth = np.concatenate((np.repeat(0, n_samples//2), np.repeat(1, n_samples//2)))
+X = np.zeros((n_samples, n_features))
+X[:n_samples//2, :] = np.ones(n_features)*0.1 + np.random.normal(scale=1, size=(n_samples//2, n_features))
+X[n_samples//2:, :] = -np.ones(n_features)*0.1 + np.random.normal(scale=0.1, size=(n_samples//2, n_features))
+
+Bhat = pecok_clustering.cluster(X, 2)
+kMeans = cluster.KMeans(n_clusters=2, init='k-means++', n_init=100, copy_x=True)
+print("truth:".ljust(10), truth)
+print("pecok:".ljust(10), kMeans.fit(Bhat).labels_)
+print("kmeans:".ljust(10), kMeans.fit(X).labels_)

tests/test_sbm.py

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+import os
+import sys
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+import numpy as np
+from sklearn import cluster
+from pecok import pecok_clustering
+
+seed = 432
+np.random.seed(seed)
+print("seed is %i" % seed)
+
+n_samples = 10
+p,q = 0.7, 0.3
+truth = np.concatenate((np.repeat(0, n_samples//2), np.repeat(1, n_samples//2)))
+expA = q*np.ones((n_samples, n_samples))
+expA[:n_samples//2,:n_samples//2] += p-q
+expA[n_samples//2:,n_samples//2:] += p-q
+A = np.vectorize(lambda p : np.random.binomial(1, p))(expA)
+A.flat[::n_samples+1] = 10
+i_lower = np.tril_indices(n_samples, -1)
+A[i_lower] = A.T[i_lower]
+print(A)
+
+Bhat = pecok_clustering.cluster(A, 2)
+kMeans = cluster.KMeans(n_clusters=2, init='k-means++', n_init=100, copy_x=True)
+print("truth:".ljust(10), truth)
+print("pecok:".ljust(10), kMeans.fit(Bhat).labels_)
+print("kmeans:".ljust(10), kMeans.fit(A).labels_)