from sklearn.neighbors import NearestNeighbors  
import numpy as np
from sklearn.svm import SVC
from sklearn.externals import joblib

p = np.load("p_softmax.npy")
classifier = SVC()
x = p[:, :8, :].reshape((-1, 1000))
y = np.array([])
for i in range(50):
	for j in range(8):
		y = np.append(y, np.array([i + 1]))


classifier.fit(x, y)
joblib.dump(classifier, 'svm_p_softmax.pkl')

for i in range(50):
	for j in range(2):
		x = p[i, 8 + j, :]
		print(classifier.predict(x))

0e19a2e21234e47dc62dabd6046fda706c244a56
<mask token>

<mask token>
for i in range(50):
    for j in range(8):
        y = np.append(y, np.array([i + 1]))
classifier.fit(x, y)
joblib.dump(classifier, 'svm_p_softmax.pkl')
for i in range(50):
    for j in range(2):
        x = p[i, 8 + j, :]
        print(classifier.predict(x))

<mask token>
p = np.load('p_softmax.npy')
classifier = SVC()
x = p[:, :8, :].reshape((-1, 1000))
y = np.array([])
for i in range(50):
    for j in range(8):
        y = np.append(y, np.array([i + 1]))
classifier.fit(x, y)
joblib.dump(classifier, 'svm_p_softmax.pkl')
for i in range(50):
    for j in range(2):
        x = p[i, 8 + j, :]
        print(classifier.predict(x))

from sklearn.neighbors import NearestNeighbors
import numpy as np
from sklearn.svm import SVC
from sklearn.externals import joblib
p = np.load('p_softmax.npy')
classifier = SVC()
x = p[:, :8, :].reshape((-1, 1000))
y = np.array([])
for i in range(50):
    for j in range(8):
        y = np.append(y, np.array([i + 1]))
classifier.fit(x, y)
joblib.dump(classifier, 'svm_p_softmax.pkl')
for i in range(50):
    for j in range(2):
        x = p[i, 8 + j, :]
        print(classifier.predict(x))

from sklearn.neighbors import NearestNeighbors  
import numpy as np
from sklearn.svm import SVC
from sklearn.externals import joblib

p = np.load("p_softmax.npy")
classifier = SVC()
x = p[:, :8, :].reshape((-1, 1000))
y = np.array([])
for i in range(50):
	for j in range(8):
		y = np.append(y, np.array([i + 1]))


classifier.fit(x, y)
joblib.dump(classifier, 'svm_p_softmax.pkl')

for i in range(50):
	for j in range(2):
		x = p[i, 8 + j, :]
		print(classifier.predict(x))
