Add animals

animals.py

@@ -0,0 +1,38 @@
+import os
+import numpy as np
+import networkx as nx
+import matplotlib.pyplot as plt
+from sklearn.datasets import make_moons, make_blobs
+from .SGL import LearnGraphTopology
+plots_dir = './plots'
+if not os.path.exists(plots_dir):
+    os.makedirs(plots_dir)
+
+def animals(k, n_iter, alpha, beta):
+    X_animals = np.load('./data/animals_data.npy')
+    animals_names = np.load('./data/animals_name.npy')
+    animals_features = np.load('./data/animals_features.npy')
+    #from sgll.sgl import LearnGraphTopolgy
+    X = np.array(X_animals)
+
+    #SCM = np.dot((X - np.mean(X, axis = 0).reshape(1, -1)), (X - np.mean(X, axis = 0).reshape(1, -1)).T)
+    SCM = np.dot(X, X.T) / X.shape[0]
+    SCM = 1/3 * np.eye(SCM.shape[0]) + SCM
+
+    # estimate underlying graph
+    sgl = LearnGraphTopolgy(SCM, n_iter=n_iter, alpha=alpha, beta = beta)
+    graph = sgl.learn_k_component_graph(k=k)
+
+    A = graph['adjacency']
+    G = nx.from_numpy_matrix(A)
+
+    mapping = {}
+    for i in range(animals_names.shape[0]):
+        mapping[i] = animals_names[i, 0]
+
+    G = nx.relabel_nodes(G, mapping)
+    fig = plt.figure(figsize=(15,15))
+    nx.draw(G, with_labels=True, font_weight='bold')
+    plt.title("Learned graph for the animal dataset" % dataset)
+    filename = os.path.join(plots_dir, 'animals', 'graph_%s_%s_%.3f_%.3f' % (k , n_iter, alpha, beta))
+    fig.savefig(filename)