'''
print(features_scaled.shape)
print(features_scaled.min(axis=0))
print(features_scaled.max(axis=0))
scatter(features_scaled[:,0],features_scaled[:,1])
'''
#labels=model.fit_predict(features_scaled)
#print(labels)
print("checking for the song", file_name[2])
print("original class", class_name1[2])
model = GMM(n_components=7)
model.fit(data)
print("EM predict class", model.predict(data[2]))
print("EM predict class", model.predict_proba(data[2]))
#print("EM score",model.score(data, class_name1))
model = SVC(probability=True)
#model.fit(data,class_name1)
#print("SVM predict class",model.predict(data[2]))
#print("SVM predict class",model.predict_proba(data[2]))
#print("SVM score",model.score(data, class_name1))
model = GaussianNB()
#model.fit(data,class_name1)
#print("Gausian Naive predict class",model.predict(data[2]))
#print("Gausian Naive predict class",model.predict_proba(data[2]))
#print("GNB score",model.score(data, class_name1))
model = KMeans(n_clusters=7)
print(features_scaled.max(axis=0))
scatter(features_scaled[:,0],features_scaled[:,1])
'''
#labels=model.fit_predict(features_scaled)
#print(labels)
print("checking for the song",file_name[2])
print("original class",class_name1[2])
model=GMM(n_components=7)
model.fit(data)
print("EM predict class",model.predict(data[2]))
print("EM predict class",model.predict_proba(data[2]))
#print("EM score",model.score(data, class_name1))
model=SVC(probability=True)
#model.fit(data,class_name1)
#print("SVM predict class",model.predict(data[2]))
#print("SVM predict class",model.predict_proba(data[2]))
#print("SVM score",model.score(data, class_name1))
model=GaussianNB()
#model.fit(data,class_name1)
#print("Gausian Naive predict class",model.predict(data[2]))
#print("Gausian Naive predict class",model.predict_proba(data[2]))
#print("GNB score",model.score(data, class_name1))
model=KMeans(n_clusters=7)
