4_1.py

import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path

from HelperClass.DataReader_1_0 import *

file_name="Data/ch04.npz"


def method1(X,Y,m):
    x_mean=X.mean()
    p=sum(Y*(X-x_mean))
    q=sum(X*X)-sum(X)*sum(X)/m
    w=p/q
    return w

def method2(X,Y,m):
    x_mean=X.mean()
    y_mean=Y.mean()
    p=sum(X*(Y-y_mean))
    q=sum(X*X)-x_mean*sum(X)
    w=p/q
    return w

def method3(X,Y,m):
    p=m*sum(X*Y)-sum(X)*sum(Y)
    q=m*sum(X*X)-sum(X)**2
    w=p/q
    return w

def calculate_b_1(X,Y,w,m):
    b=sum(Y-w*X)/m
    return b

def calculate_b_2(X,Y,w,m):
    b=Y.mean()-w*X.mean()
    return b

def leastSquare():
    reader=DataReader_1_0(file_name)
    reader.ReadData()
    X,Y=reader.GetWholeTrainSamples()
    m=X.shape[0]
    w1=method1(X,Y,m)
    b1=calculate_b_1(X,Y,w1,m)

    w2=method2(X,Y,m)
    b2=calculate_b_2(X,Y,w2,m)

    w3=method3(X,Y,m)
    b3=calculate_b_1(X,Y,w3,m)

    print("w1=%f, b1=%f" % (w1,b1))
    print("w2=%f, b2=%f" % (w2,b2))
    print("w3=%f, b3=%f" % (w3,b3))

def gradientDescent():
    reader=DataReader_1_0(file_name)
    reader.ReadData()
    X,Y=reader.GetWholeTrainSamples()

    eta=0.1
    w,b=0.0,0.0

    for i in range(reader.num_train):
        xi=X[i]
        yi=Y[i]
        zi=xi*w+b

        dz=zi-yi
        dw=dz*xi
        db=dz
        w=w-eta*dw
        b=b-eta*db

    print("w=%f"%w)
    print("b=%f"%b)

if __name__=='__main__':
    gradientDescent()