# -*- coding:utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
from sklearn import ensemble
from sklearn.utils import shuffle
from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error
from CrawlerUtil import read_csv
from sklearn.model_selection import train_test_split
# #############################################################################
# 导入数据
# 加载数据集
file_dir = 'C:/Users/yuzhe/Desktop/OptionAnalysis/files/'
csv_file = read_csv(file_dir + 'TestUSDIndex.csv')
dataNum = len(csv_file)
featureNum = len(csv_file[0])-2
print("特征的维度", featureNum)
dataMat = np.array(csv_file)
X = dataMat[1:, 1: featureNum].astype(float)
y = dataMat[1:, featureNum].astype(float)
'''
# 将y标签的增长率转化为增、跌、不变三种标签
for i in range(dataNum-1):
if y[i] == 0: y[i] = 0
elif y[i]<0: y[i] = -1
else:y[i]= 1
'''
# 数据集分
from sklearn.grid_search import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
import numpy as np
from sklearn.model_selection import train_test_split
from CrawlerUtil import read_csv
# 加载样本数据集
if __name__=='__main__':
n = 180
csv_file = read_csv('C:/Users/yuzhe/Desktop/OptionAnalysis/files/files_' + n.__str__() + 'min/REDUCED_FEATURE_VECTOR_' + n.__str__() + '.csv')
dataNum = len(csv_file)
featureNum = len(csv_file[0])-1
print("Dimension of feature", featureNum)
dataMat = np.array(csv_file)
X = dataMat[1:, 0:featureNum].astype(float)
y = dataMat[1:, featureNum].astype(float)
print(dataNum)
for i in range(dataNum-1):
if y[i] == 0: y[i] = 0
elif y[i]<0: y[i] = -1
else:y[i]= 1
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) # 数据集分割
pipe_scv = Pipeline([('scl', StandardScaler()), ('clf', SVC(random_state=1))])
param_range = [0.0001, 0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0]
param_grid = [{'clf_C': param_range, 'clf_kernel': ['linear']},
{'clf_C': param_range, 'clf_gamma': param_range, 'clf_kernel': ['rbf']}]
gs = GridSearchCV(estimator = pipe_scv ,
print(dataNum)
for i in range(dataNum - 1):
if y[i] == 0:
y[i] = 0
elif y[i] < 0:
y[i] = -1
else:
y[i] = 1
return y
if __name__ == "__main__":
# 加载样本数据集
n = 120
csv_file = read_csv('C:/Users/yuzhe/Desktop/OptionAnalysis/files/files_' +
n.__str__() + 'min/REDUCED_FEATURE_VECTOR_' +
n.__str__() + '.csv')
dataNum = len(csv_file)
featureNum = len(csv_file[0]) - 1
print("Dimension of feature", featureNum)
dataMat = np.array(csv_file)
X = dataMat[1:, 0:featureNum].astype(float)
y = dataMat[1:, featureNum].astype(float)
y = convert2class(y, dataNum) #转换为类别
# 神经网络对数据尺度敏感，所以最好在训练前标准化，或者归一化，或者缩放到[-1,1]
scaler = StandardScaler() # 标准化转换
scaler.fit(X) # 训练标准化对象
X = scaler.transform(X) # 转换数据集
# solver='lbfgs', MLP的求解方法：L-BFGS 在小数据上表现较好，Adam 较为鲁棒，SGD在参数调整较优时会有最佳表现（分类效果与迭代次数）；SGD标识随机梯度下降。
# alpha:L2的参数：MLP是可以支持正则化的，默认为L2，具体参数需要调整