Question

我正在尝试使用6种不同的温度输入来计算Pewma，但不了解如何训练模型。我成功读取了数据，创建了2个不同的数据框，一个包含输入，一个是包含要为其生成Pewma的值的列。但是，我得到的最接近的结果是在熊猫中一起使用ewm() + mean()函数，但是除了您要测试的数据之外，它不需要任何东西。

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

T = 20
t = 0
alpha_0 = 0.95
beta = 0.5
threshold = 0.05
#newpewma = 0 #first initialization of
oldpewma = 0
pewma = 0
alpha = 0

def calc_alpha(dataset, t, T, col, beta, alpha_calc):
    if t < T:
        alpha = 1-1.0/t
        print("EWMA calc in progress (initialization of MA) - ")
    else:
        alpha = (1 - beta*dataset[col])*alpha_calc
        print("EWMA calc in progress - ")
    return alpha

def calc_pewma(oldpewma, alpha_0, beta, fulldata, testingdata, T, prob, col):

    firstcalc = 1-beta*prob
    secondcalc = alpha_0*firstcalc*oldpewma
    thirdcalc = (1-alpha_0*firstcalc)*testingdata[col]
    newpewma = secondcalc + thirdcalc
    return newpewma

#calculate zero-mean
def calc_zed(std, dataset, lastpewma, col):
    zed = (dataset[col] - lastpewma)/std
    return zed

#calculate probability
def calc_prob(zed):
    partone = 1/m.sqrt(2*m.pi)
    parttwo = m.pow(zed, 2)
    partthree = -(parttwo/2)
    prob = partone*m.pow(partthree, 1)
    prob = m.fabs(prob)
    return prob

def calc_std(t, y, column):
    deviationPrediction = np.std(y.iloc[1:t, 0])
    return deviationPrediction


# Read Excel file, limit to temp columns
print("Reading in sensor data")
fullDataTemp = pd.read_excel('tempData.xlsx')
fullDataTemp = fullDataTemp[["TempLR", "TempK", "TempNW", "TempSW", "TempW", "TempTV", "TempO"]]

# data_cols in lambda function
predictThisTemp = "TempK"
print(predictThisTemp + " Chosen for prediction")

#Split data into 2 numpy arrays
X = np.array(fullDataTemp.drop([predictThisTemp], 1))
y = np.array(fullDataTemp[predictThisTemp])
arraylength = len(y)

#split data reformatted to pandas
df_predict = pd.DataFrame(y)
df_fulldata = pd.DataFrame(X)

#rolling averages of the data
df_predict = df_predict.rolling(T).mean()
df_fulldata = df_fulldata.rolling(T).mean()

pewmaArray = []
for col in y:
    t= 3
    #t = len(y[col])
    #print(t)
    alpha = calc_alpha(df_predict, t, T, col, beta, alpha)
    std = calc_std(t, df_predict, predictThisTemp)
    zed = calc_zed(std, df_predict, oldpewma, col)
    prob = calc_prob(zed)
    pewma = calc_pewma(pewma, alpha, beta, df_fulldata, df_predict, T, prob)
    pewmaArray.append(pewma)


plt.plot(pewmaArray, 'r--')
plt.show()

多个输入，如何计算输出的pewma？

0 个答案: