More tidying of pyMM.py

charlienash · May 19, 2016 · 1a1b3f3 · 1a1b3f3
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diff --git a/__pycache__/__init__.cpython-34.pyc b/__pycache__/__init__.cpython-34.pyc
diff --git a/__pycache__/pyMM.cpython-34.pyc b/__pycache__/pyMM.cpython-34.pyc
diff --git a/__pycache__/util.cpython-34.pyc b/__pycache__/util.cpython-34.pyc
diff --git a/examples/__init__.py b/examples/__init__.py
diff --git a/gmm_overfit_demo.py b/gmm_overfit_demo.py
@@ -0,0 +1,33 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon May  2 11:18:41 2016
+
+@author: charlie
+"""
+
+import numpy as np
+from models.pyMix import GMM, MPPCA, MFA
+from sklearn.cross_validation import KFold
+
+dat_path = '/tmp/virus3.dat'
+data = np.loadtxt(dat_path)
+
+# normalize data
+X = (data - data.mean(axis=0)) / data.std(axis=0)
+
+
+# Get cv splits
+k = 10
+n_examples = X.shape[0]
+kf = KFold(n_examples, k)
+
+# Fit model evaluate with cross-validation
+n_components = 1
+val_score = 0
+for train, test in kf:       
+    gmm = GMM(n_components=n_components)
+    gmm.fit(X[train], init_method='kmeans')
+    print('Val score: {}'.format(gmm.score(X[test])))
+    val_score += gmm.score(X[test])
+mean_val_score = val_score / k
+
diff --git a/models/__init__.py b/models/__init__.py
@@ -0,0 +1,2 @@
+# -*- coding: utf-8 -*-
+
diff --git a/models/__pycache__/__init__.cpython-34.pyc b/models/__pycache__/__init__.cpython-34.pyc
diff --git a/models/__pycache__/pyMix.cpython-34.pyc b/models/__pycache__/pyMix.cpython-34.pyc