def model_train(Save = False, modelname = None):
X1_left = ReadCSV('datasets_csv/feature_0630_1700/X1_offline_0630_1700.csv','float64')
X1_midle = ReadCSV('datasets_csv/feature_0701_1100/X1_offline_0701_1100.csv','float64')
X1_right = ReadCSV('datasets_csv/feature_0701_1400/X1_offline_0701_1400.csv','float64')
Y1 = ReadCSV('datasets_csv/feature_0701_1100/Y1_offline_0701_1100.csv','float64')
X1 = np.c_[X1_left,X1_midle,X1_right]
# X1 = np.c_[X1_left]
X1 = list(X1)
Y1 = list(Y1)
X3_left = ReadCSV('datasets_csv/feature_0630_1700/X3_offline_0630_1700.csv','float64')
X3_midle = ReadCSV('datasets_csv/feature_0701_1100/X3_offline_0701_1100.csv','float64')
X3_right = ReadCSV('datasets_csv/feature_0701_1400/X3_offline_0701_1400.csv','float64')
Y3 = ReadCSV('datasets_csv/feature_0701_1100/Y3_offline_0701_1100.csv','float64')
X3 = np.c_[X3_left,X3_midle,X3_right]
# X3 = np.c_[X3_left]
X3 = list(X3)
Y3 = list(Y3)
X1.extend(X3)
Y1.extend(Y3)
del X3,Y3
X4_left = ReadCSV('datasets_csv/feature_0630_1700/X4_offline_0630_1700.csv','float64')
X4_midle = ReadCSV('datasets_csv/feature_0701_1100/X4_offline_0701_1100.csv','float64')
X4_right = ReadCSV('datasets_csv/feature_0701_1400/X4_offline_0701_1400.csv','float64')
Y4 = ReadCSV('datasets_csv/feature_0701_1100/Y4_offline_0701_1100.csv','float64')
X4 = np.c_[X4_left,X4_midle,X4_right]
# X4 = np.c_[X4_left]
X4 = list(X4)
Y4 = list(Y4)
X1.extend(X4)
Y1.extend(Y4)
del X4,Y4
X5_left = ReadCSV('datasets_csv/feature_0630_1700/X5_offline_0630_1700.csv','float64')
X5_midle = ReadCSV('datasets_csv/feature_0701_1100/X5_offline_0701_1100.csv','float64')
X5_right = ReadCSV('datasets_csv/feature_0701_1400/X5_offline_0701_1400.csv','float64')
Y5 = ReadCSV('datasets_csv/feature_0701_1100/Y5_offline_0701_1100.csv','float64')
X5 = np.c_[X5_left,X5_midle,X5_right]
X5 = list(X5)
Y5 = list(Y5)
X1.extend(X5)
Y1.extend(Y5)
del X5,Y5
X6_left = ReadCSV('datasets_csv/feature_0630_1700/X6_offline_0630_1700.csv','float64')
X6_midle = ReadCSV('datasets_csv/feature_0701_1100/X6_offline_0701_1100.csv','float64')
X6_right = ReadCSV('datasets_csv/feature_0701_1400/X6_offline_0701_1400.csv','float64')
Y6 = ReadCSV('datasets_csv/feature_0701_1100/Y6_offline_0701_1100.csv','float64')
X6 = np.c_[X6_left,X6_midle,X6_right]
X6 = list(X6)
Y6 = list(Y6)
X1.extend(X6)
Y1.extend(Y6)
del X6,Y6
X7_left = ReadCSV('datasets_csv/feature_0630_1700/X7_offline_0630_1700.csv','float64')
X7_midle = ReadCSV('datasets_csv/feature_0701_1100/X7_offline_0701_1100.csv','float64')
X7_right = ReadCSV('datasets_csv/feature_0701_1400/X7_offline_0701_1400.csv','float64')
Y7 = ReadCSV('datasets_csv/feature_0701_1100/Y7_offline_0701_1100.csv','float64')
X7 = np.c_[X7_left,X7_midle,X7_right]
X7 = list(X7)
Y7 = list(Y7)
X1.extend(X7)
Y1.extend(Y7)
del X7,Y7
X8_left = ReadCSV('datasets_csv/feature_0630_1700/X8_offline_0630_1700.csv','float64')
X8_midle = ReadCSV('datasets_csv/feature_0701_1100/X8_offline_0701_1100.csv','float64')
X8_right = ReadCSV('datasets_csv/feature_0701_1400/X8_offline_0701_1400.csv','float64')
Y8 = ReadCSV('datasets_csv/feature_0701_1100/Y8_offline_0701_1100.csv','float64')
X8 = np.c_[X8_left,X8_midle,X8_right]
X8 = list(X8)
Y8 = list(Y8)
X1.extend(X8)
Y1.extend(Y8)
del X8,Y8
X1 = np.array(X1)
Y1 = np.array(Y1)
X1,Y1 = DowmSample(X1,Y1,1)
model = RandomForestClassifier(n_estimators=100,random_state=1)
# model = GradientBoostingClassifier(n_estimators=100,max_leaf_nodes=5, subsample=0.8, random_state=1)
# model = LogisticRegression('l2')
# model = RVC()
y0 = []
for y in Y1:
y0.append(y[0])
y1 = np.array(y0)
# y1 = y0.reshape(1,len(y0))
print 'size of X1',np.shape(X1)
print 'size of y1',np.shape(y1)
model.fit(X1, y1.ravel())
model.fit(X1, y1)
# 保存模型
if Save == True:
f = open(modelname,'w')
pickle.dump(model, f)
f.close()
print '\n -------------- Training is over ----------------------'
return model
def model_train(Save = False, modelname = None):
X1_left = ReadCSV('datasets_csv/feature_0630_1700/X1_offline_0630_1700.csv','float64')
X1_midle = ReadCSV('datasets_csv/feature_0701_1100/X1_offline_0701_1100.csv','float64')
X1_right = ReadCSV('datasets_csv/feature_0701_1400/X1_offline_0701_1400.csv','float64')
Y1 = ReadCSV('datasets_csv/feature_0701_1100/Y1_offline_0701_1100.csv','float64')
X1 = np.c_[X1_left,X1_midle,X1_right]
# X1 = np.c_[X1_left]
X1 = list(X1)
Y1 = list(Y1)
X2_left = ReadCSV('datasets_csv/feature_0630_1700/X2_offline_0630_1700.csv','float64')
X2_midle = ReadCSV('datasets_csv/feature_0701_1100/X2_offline_0701_1100.csv','float64')
X2_right = ReadCSV('datasets_csv/feature_0701_1400/X2_offline_0701_1400.csv','float64')
Y2 = ReadCSV('datasets_csv/feature_0701_1100/Y2_offline_0701_1100.csv','float64')
X2 = np.c_[X2_left,X2_midle,X2_right]
# X2 = np.c_[X2_left]
X2 = list(X2)
Y2 = list(Y2)
X1.extend(X2)
Y1.extend(Y2)
del X2,Y2
X3_left = ReadCSV('datasets_csv/feature_0630_1700/X3_offline_0630_1700.csv','float64')
X3_midle = ReadCSV('datasets_csv/feature_0701_1100/X3_offline_0701_1100.csv','float64')
X3_right = ReadCSV('datasets_csv/feature_0701_1400/X3_offline_0701_1400.csv','float64')
Y3 = ReadCSV('datasets_csv/feature_0701_1100/Y3_offline_0701_1100.csv','float64')
X3 = np.c_[X3_left,X3_midle,X3_right]
# X3 = np.c_[X3_left]
X3 = list(X3)
Y3 = list(Y3)
X1.extend(X3)
Y1.extend(Y3)
del X3,Y3
X4_left = ReadCSV('datasets_csv/feature_0630_1700/X4_offline_0630_1700.csv','float64')
X4_midle = ReadCSV('datasets_csv/feature_0701_1100/X4_offline_0701_1100.csv','float64')
X4_right = ReadCSV('datasets_csv/feature_0701_1400/X4_offline_0701_1400.csv','float64')
Y4 = ReadCSV('datasets_csv/feature_0701_1100/Y4_offline_0701_1100.csv','float64')
X4 = np.c_[X4_left,X4_midle,X4_right]
# X4 = np.c_[X4_left]
X4 = list(X4)
Y4 = list(Y4)
X1.extend(X4)
Y1.extend(Y4)
del X4,Y4
X5_left = ReadCSV('datasets_csv/feature_0630_1700/X5_offline_0630_1700.csv','float64')
X5_midle = ReadCSV('datasets_csv/feature_0701_1100/X5_offline_0701_1100.csv','float64')
X5_right = ReadCSV('datasets_csv/feature_0701_1400/X5_offline_0701_1400.csv','float64')
Y5 = ReadCSV('datasets_csv/feature_0701_1100/Y5_offline_0701_1100.csv','float64')
X5 = np.c_[X5_left,X5_midle,X5_right]
X5 = list(X5)
Y5 = list(Y5)
X1.extend(X5)
Y1.extend(Y5)
del X5,Y5
X6_left = ReadCSV('datasets_csv/feature_0630_1700/X6_offline_0630_1700.csv','float64')
X6_midle = ReadCSV('datasets_csv/feature_0701_1100/X6_offline_0701_1100.csv','float64')
X6_right = ReadCSV('datasets_csv/feature_0701_1400/X6_offline_0701_1400.csv','float64')
Y6 = ReadCSV('datasets_csv/feature_0701_1100/Y6_offline_0701_1100.csv','float64')
X6 = np.c_[X6_left,X6_midle,X6_right]
X6 = list(X6)
Y6 = list(Y6)
X1.extend(X6)
Y1.extend(Y6)
del X6,Y6
X7_left = ReadCSV('datasets_csv/feature_0630_1700/X7_offline_0630_1700.csv','float64')
X7_midle = ReadCSV('datasets_csv/feature_0701_1100/X7_offline_0701_1100.csv','float64')
X7_right = ReadCSV('datasets_csv/feature_0701_1400/X7_offline_0701_1400.csv','float64')
Y7 = ReadCSV('datasets_csv/feature_0701_1100/Y7_offline_0701_1100.csv','float64')
X7 = np.c_[X7_left,X7_midle,X7_right]
X7 = list(X7)
Y7 = list(Y7)
X1.extend(X7)
Y1.extend(Y7)
del X7,Y7
X8_left = ReadCSV('datasets_csv/feature_0630_1700/X8_offline_0630_1700.csv','float64')
X8_midle = ReadCSV('datasets_csv/feature_0701_1100/X8_offline_0701_1100.csv','float64')
X8_right = ReadCSV('datasets_csv/feature_0701_1400/X8_offline_0701_1400.csv','float64')
Y8 = ReadCSV('datasets_csv/feature_0701_1100/Y8_offline_0701_1100.csv','float64')
X8 = np.c_[X8_left,X8_midle,X8_right]
X8 = list(X8)
Y8 = list(Y8)
X1.extend(X8)
Y1.extend(Y8)
del X8,Y8
X1 = np.array(X1)
Y1 = np.array(Y1)
X1,Y1 = DowmSample(X1,Y1,8)
model = GradientBoostingClassifier(n_estimators=100,max_leaf_nodes=5, learning_rate=0.1, subsample=0.8, random_state=1)
model.fit(X1, Y1.ravel())
# 保存模型
if Save == True:
f = open(modelname,'w')
pickle.dump(model, f)
f.close()
print '\n ------------- Training is over ----------------------'
return model