--- a
+++ b/svm_clas.py
@@ -0,0 +1,55 @@
+from sklearn import svm
+import numpy as np
+train_y = []
+train_a = []
+train_x = np.genfromtxt('train.csv',delimiter=',')
+f = open("labels_0.dat","r")
+for i in f:
+	train_y.append(i)
+train_y = np.array(train_y).astype(np.float)
+train_y = train_y.astype(np.int)
+train_x = np.array(train_x)
+#print "valence",train_y
+#print train_x
+#print "train_x",train_x
+clf = svm.SVC()
+clf.fit(train_x, train_y)
+
+
+f = open("labels_1.dat","r")
+for i in f:
+	train_a.append(i)
+train_a = np.array(train_a).astype(np.float)
+train_a = train_a.astype(np.int)
+#print "arousal",train_a[1040:1280]
+#print "train_x",len(train_x[0:26])
+clf1 = svm.SVC()
+clf1.fit(train_x, train_a)
+#print test_a
+predict_al = clf1.predict(train_x)
+#print "alrosal",predict_al
+predict_val = clf.predict(train_x) 
+#print "valence",predict_val 
+val_count = al_count = 0
+for i in range(len(train_y)):
+	if train_y[i] == predict_val[i]:
+		val_count = val_count+1
+	if train_a[i] == predict_al[i]:
+		al_count = al_count+1
+print "predicted valence",(float(val_count)/len(train_y))*100
+print "predicted arousal",(float(al_count)/len(train_y))*100
+# classifier efficiency
+'''
+predicted valence 98.046875 percentage
+predicted arousal 97.890625 percentage
+
+predicted valence 95.0
+predicted arousal 96.09375 
+'''
+# output
+'''
+predicted valence 17.9166666667
+predicted arousal 13.3333333333
+'''
+#chan = ['Fp1','AF3','F3','F7','FC5','FC1','C3','T7','CP5','CP1','P3','P7','PO3','O1','Oz','Pz','Fp2','AF4','Fz','F4','F8','FC6','FC2','Cz','C4','T8','CP6','CP2','P4','P8','PO4','O2']
+