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prediction_algo.py
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import pandas as pd
import numpy as np
import matplotlib.pylab as plt
import xlrd
import openpyxl
from sklearn.datasets import load_boston
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVR
from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
from Data_interaction import DataInteraction
class Prediction():
def __init__(self)：
DI = DataInteraction()
def _train_prediction(self,timeStep,col,startId,num = 16002):
#导入数据？
table = DI.getData(timeStep,col,startId,num)
# #数据manipulation
# nrows = table.nrows #行数？
# #c1=arange(0,nrows,1) #0到行数的list
table.reverse()
table = np.array(table)[:,col]
# cols_list =table.col_values(x)
# cols_array=np.array(cols)# 把list转换为矩阵进行矩阵操作
# datamatrix[:,x]=cols_array# 把数据进行存储
# temData = datamatrix[:,0] #？？？
# humData = datamatrix[:,1] #？？？
x_predcit = table[-4:]
x = np.zeros((num-4,4)) #数据行数-4
for i in range(num-4):
x[i][0:4] = table[i:i+4]
x_target_tem = table[4:]
x_train, x_test, y_train, y_test = train_test_split(x, x_target_tem, test_size=0, random_state=33)
#训练数据和测试数据进行标准化处理
ss_x = StandardScaler()
x_train = ss_x.fit_transform(x_train)
x_predict =ss_x.fit_transform(x_predict)
# x_test = ss_x.transform(x_test)
ss_y = StandardScaler()
y_train = ss_y.fit_transform(y_train.reshape(-1, 1))
# y_test = ss_y.transform(y_test.reshape(-1, 1))
# 4.1 支持向量机模型进行学习和预测
# 线性核函数配置支持向量机
linear_svr = SVR(kernel="linear")
# 训练
linear_svr.fit(x_train, y_train)
# 预测 保存预测结果
linear_svr_y_predict = linear_svr.predict(x_predict)
linear_svr_y_predict = ss_y.inverse_transform(linear_svr_y_predict)
return linear_svr_y_predict
# 多项式核函数配置支持向量机
# poly_svr = SVR(kernel="poly")
# # 训练
# poly_svr.fit(x_train, y_train)
# # 预测 保存预测结果
# poly_svr_y_predict = linear_svr.predict(x_test)