HiranmaiGaddam
104 linhas
2.3 KiB
Python
104 linhas
2.3 KiB
Python
from sklearn import svm
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import numpy as np
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train_y = []
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train_a = []
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test_y = []
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test_a = []
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z=[]
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z1=[]
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train_x = np.genfromtxt('train.csv',delimiter=',')
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test_x=np.genfromtxt('test.csv',delimiter=',')
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train_x = np.array(train_x)
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test_x = np.array(test_x)
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with open('labels_0.txt') as my_file:
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p=my_file.readlines()
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for i in p:
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q= i.split()
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if(q!=[]):
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z= float(q[0])
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train_y.append(z)
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train_y = np.array(train_y).astype(np.float)
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train_y = train_y.astype(np.int)
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with open('labelst_0.txt') as myfile:
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p1=myfile.readlines()
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for j in p1:
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q1= j.split()
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if(q1!=[]):
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z1= float(q1[0])
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test_y.append(z1)
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test_y = np.array(test_y).astype(np.float)
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test_y = test_y.astype(np.int)
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#print "valence",train_y
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#print train_x
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#print "train_x",train_x
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clf = svm.SVC()
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clf.fit(train_x, train_y)
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with open('labels_1.txt') as my_file1:
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p2=my_file1.readlines()
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for k in p2:
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q2= k.split()
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if(q2!=[]):
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z2= float(q2[0])
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train_a.append(z2)
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train_a = np.array(train_a).astype(np.float)
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train_a = train_a.astype(np.int)
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with open('labelst_1.txt') as myfile1:
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p3=myfile1.readlines()
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for l in p3:
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q3= l.split()
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if(q3!=[]):
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z3= float(q3[0])
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test_a.append(z3)
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test_a = np.array(test_a).astype(np.float)
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test_a = test_a.astype(np.int)
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test_a = np.array(test_y).astype(np.float)
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test_a = test_y.astype(np.int)
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#print "arousal",train_a[1040:1280]
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#print "train_x",len(train_x[0:26])
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clf1 = svm.SVC()
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clf1.fit(train_x, train_a)
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predict_al = clf1.predict(test_x)
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#print "alrosal",predict_al
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predict_val = clf.predict(test_x)
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#print "valence",predict_val
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val_count = al_count = 0
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for i in range(len(test_y)):
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if test_y[i] == predict_val[i]:
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val_count = val_count+1
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if test_a[i] == predict_al[i]:
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al_count = al_count+1
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print "predicted valence",(float(val_count)/len(test_y))*100
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print "predicted arousal",(float(al_count)/len(test_a))*100
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# classifier efficiency
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'''
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predicted valence 98.046875 percentage
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predicted arousal 97.890625 percentage
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predicted valence 95.0
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predicted arousal 96.09375
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'''
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# output
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'''
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predicted valence 17.9166666667
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predicted arousal 13.3333333333
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'''
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#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']
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