def crossValidate(train_y, train_X):
""" Cross validate to get optimal parameters """
# scale data
min_max_scaler = preprocessing.MinMaxScaler(feature_range=(-1, 1))
X_scaledArr = min_max_scaler.fit_transform(train_X)
X_scaled = X_scaledArr.tolist()
# write to svm format file
outputPath = '.\\cv'
fileName = 'train_data'
svmTool = SvmTool()
svmTool.write2SVMFormat(outputPath, fileName, X_scaled, train_y)
def crossValidate(train_y, train_X):
""" Cross validate to get optimal parameters """
# scale data
min_max_scaler = preprocessing.MinMaxScaler(feature_range=(-1, 1))
X_scaledArr = min_max_scaler.fit_transform(train_X)
X_scaled = X_scaledArr.tolist()
# write to svm format file
outputPath = '.\\cv'
fileName = 'train_data'
svmTool = SvmTool()
svmTool.write2SVMFormat(outputPath, fileName, X_scaled, train_y)
