# HG changeset patch
# User peterf
# Date 1437484618 -3600
# Node ID 39258b87522811e9a71f39bec0e129c7f540ee11
# Parent  0147bf388eb80312e92564bf9f63ff6bcd7b9c58
Disable sampling when estimating GMMs -- use entire set of training samples.

diff -r 0147bf388eb8 -r 39258b875228 gmm_baseline_experiments/figures/predictionperformanceGMM_Baseline_EXPER005.pdf
Binary file gmm_baseline_experiments/figures/predictionperformanceGMM_Baseline_EXPER005.pdf has changed
diff -r 0147bf388eb8 -r 39258b875228 gmm_baseline_experiments/gmm/GMM_methods.py
--- a/gmm_baseline_experiments/gmm/GMM_methods.py	Sun Jul 19 22:11:19 2015 +0100
+++ b/gmm_baseline_experiments/gmm/GMM_methods.py	Tue Jul 21 14:16:58 2015 +0100
@@ -33,7 +33,8 @@
 	# scale data
 	[X,trainMean,trainStd] = scale(X)
 
-	numTrainingExamplesOfEachType = 20000
+	#No sampling; use entire set of frames
+	#numTrainingExamplesOfEachType = 20000
 	
 	X = X.tolist()
 	Y = Y.tolist()
@@ -48,15 +49,15 @@
 	#Sample data
 	for label in Labels:
 	    DataByLabel[label] = np.array(DataByLabel[label])
-	    I = np.random.choice(DataByLabel[label].shape[0],numTrainingExamplesOfEachType)
-	    DataByLabel[label] = DataByLabel[label][I]	    
+	    #I = np.random.choice(DataByLabel[label].shape[0],numTrainingExamplesOfEachType)
+	    #DataByLabel[label] = DataByLabel[label][I]	    
 
-	#print "Training..."
+	print "Training..."
 	GMMS = {}
 	for label in Labels:
 	    GMMS[label] = mixture.GMM(n_components=numComponents, covariance_type=covarianceType)
 	    GMMS[label].fit(DataByLabel[label])
-	#print "Done!"
+	print "Done!"
 
 	startTime = time.time()