Machine learning 逻辑回归无法找到θ的值
我在csv文件中有100个条目Machine learning 逻辑回归无法找到θ的值,machine-learning,Machine Learning,我在csv文件中有100个条目 Physics,Maths,Status_class0or1 30,40,0 90,70,1 df = pd.read_csv("LogisticRegressionFirstBinaryClassifier.csv", header=None) df.columns = ["Maths", "Physics", "AdmissionStatus"] X = np.array(df[["Maths", "Physics"]]) y = np.array(df[[
Physics,Maths,Status_class0or1
30,40,0
90,70,1
df = pd.read_csv("LogisticRegressionFirstBinaryClassifier.csv", header=None)
df.columns = ["Maths", "Physics", "AdmissionStatus"]
X = np.array(df[["Maths", "Physics"]])
y = np.array(df[["AdmissionStatus"]])
X = preprocessing.normalize(X)
X = np.c_[np.ones(X.shape[0]), X]
theta = np.ones((X.shape[1], 1))
print(X.shape) # (100, 3)
print(y.shape) # (100, 1)
print(theta.shape) # (3, 1)
利用以上数据,我试图建立逻辑(二进制)分类器。
请告诉我哪里做错了?为什么我在3*3矩阵中得到答案(θ的9个值,其中应仅为3)
以下是代码:
导入库
import numpy as np
import pandas as pd
from sklearn import preprocessing
从csv文件读取数据
Physics,Maths,Status_class0or1
30,40,0
90,70,1
df = pd.read_csv("LogisticRegressionFirstBinaryClassifier.csv", header=None)
df.columns = ["Maths", "Physics", "AdmissionStatus"]
X = np.array(df[["Maths", "Physics"]])
y = np.array(df[["AdmissionStatus"]])
X = preprocessing.normalize(X)
X = np.c_[np.ones(X.shape[0]), X]
theta = np.ones((X.shape[1], 1))
print(X.shape) # (100, 3)
print(y.shape) # (100, 1)
print(theta.shape) # (3, 1)
计算X和θ的点积
def calc_z(X,theta):
return np.dot(X,theta)
S形函数
def sigmoid(z):
return 1 / (1 + np.exp(-z))
成本函数
def cost_function(X, y, theta):
z = calc_z(X,theta)
h = sigmoid(z)
return (-y * np.log(h) - (1 - y) * np.log(1 - h)).mean()
print("cost_function =" , cost_function(X, y, theta))
def derivativeofcostfunction(X, y, theta):
z = calc_z(X,theta)
h = sigmoid(z)
calculation = np.dot((h - y).T,X)
return calculation
print("derivativeofcostfunction=", derivativeofcostfunction(X, y, theta))
def grad_desc(X, y, theta, lr=.001, converge_change=.001):
cost = cost_function(X, y, theta)
change_cost = 1
num_iter = 1
while(change_cost > converge_change):
old_cost = cost
print(theta)
print (derivativeofcostfunction(X, y, theta))
theta = theta - lr*(derivativeofcostfunction(X, y, theta))
cost = cost_function(X, y, theta)
change_cost = old_cost - cost
num_iter += 1
return theta, num_iter
以下是输出:
[[ 0.4185146 -0.56877556 0.63999433]
[15.39722864 9.73995197 11.07882445]
[12.77277463 7.93485324 9.24909626]]
[[0.33944777 0.58199037 0.52493407]
[0.02106587 0.36300629 0.30297278]
[0.07040604 0.3969297 0.33737757]]
[[-0.05856159 -0.89826735 0.30849185]
[15.18035041 9.59004868 10.92827046]
[12.4804775 7.73302024 9.04599788]]
[[0.33950634 0.58288863 0.52462558]
[0.00588552 0.35341624 0.29204451]
[0.05792556 0.38919668 0.32833157]]
[[-5.17526527e-01 -1.21534937e+00 -1.03387571e-02]
[ 1.49729502e+01 9.44663458e+00 1.07843504e+01]
[ 1.21978140e+01 7.53778010e+00 8.84964495e+00]]
(array([[ 0.34002386, 0.58410398, 0.52463592],
[-0.00908743, 0.34396961, 0.28126016],
[ 0.04572775, 0.3816589 , 0.31948193]]), 46)
我修改了这段代码,只是在返回矩阵时添加了转置,它解决了我的问题
def derivativeofcostfunction(X, y, theta):
z = calc_z(X,theta)
h = sigmoid(z)
calculation = np.dot((h - y).T,X)
return calculation.T