import numpy as np
import matplotlib.pyplot as plt
data = np.genfromtxt("data.csv",delimiter=",")
x_data = data[:,0]
y_data = data[:,1]
print(x_data.shape)
print(y_data.shape)
plt.scatter(x_data,y_data)
plt.xlabel('this is x')
plt.ylabel('this is y')
plt.title('this is a demo')
plt.show()
最小二乘法及梯度下降實現:
#learning rate
lr = 0.0001
#截距
b = 0
#斜率
k = 0
#最大迭代次數
epochs = 50
#最小二乘法
def compute_error(b,k,x_data,y_data):
totalError = 0
for i in range(0,len(x_data)):
totalError += (y_data[i]-(k*x_data[i]+b))**2
return totalError/float(len(x_data))/2.0
#梯度下降
def gradient_descent_runner(x_data,y_data,b,k,lr,epochs):
m = float(len(x_data))
for i in range(epochs):
b_grad = 0
k_grad = 0
for j in range(0,len(x_data)):
b_grad += (1/m)*(((k*x_data[i])+b)-y_data[j])
k_grad += (1/m) * x_data[j] * (((k * x_data[j]) + b) - y_data[j])
b = b-(lr*b_grad)
k = k-(lr*k_grad)
return b,k
開啓訓練
print("Starting b = {0}, k = {1}, error = {2}".format(b, k, compute_error(b, k, x_data, y_data)))
print("Running...")
b, k = gradient_descent_runner(x_data, y_data, b, k, lr, epochs)
print("After {0} iterations b = {1}, k = {2}, error = {3}".format(epochs, b, k, compute_error(b, k, x_data, y_data)))
#畫圖
plt.plot(x_data, y_data, 'b.')
plt.plot(x_data, k*x_data + b, 'r')
plt.show()
用sklearn實現使一元線性迴歸
from sklearn.linear_model import LinearRegression
x_data2 = data[:,0,np.newaxis]
y_data2 = data[:,1,np.newaxis]
# 創建並擬合模型
model = LinearRegression()
model.fit(x_data2, y_data2)
# 畫圖
plt.plot(x_data2, y_data2, 'b.')
plt.plot(x_data2, model.predict(x_data2), 'r')
plt.show()
多元線性迴歸sklearn實現
#!/usr/bin/env python
# coding: utf-8
import numpy as np
from numpy import genfromtxt
from sklearn import linear_model
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# 讀入數據
data = genfromtxt(r"Delivery.csv",delimiter=',')
print(data)
# 切分數據
x_data = data[:,:-1]
y_data = data[:,-1]
print(x_data)
print(y_data)
# 創建模型
model = linear_model.LinearRegression()
model.fit(x_data, y_data)
# 係數
print("coefficients:",model.coef_)
# 截距
print("intercept:",model.intercept_)
# 測試
x_test = [[102,4]]
predict = model.predict(x_test)
print("predict:",predict)
ax = plt.figure().add_subplot(111, projection = '3d')
ax.scatter(x_data[:,0], x_data[:,1], y_data, c = 'r', marker = 'o', s = 100) #點爲紅色三角形
x0 = x_data[:,0]
x1 = x_data[:,1]
# 生成網格矩陣
x0, x1 = np.meshgrid(x0, x1)
#特徵值 特徵0 特徵1
z = model.intercept_ + x0*model.coef_[0] + x1*model.coef_[1]
# 畫3D圖
ax.plot_surface(x0, x1, z)
#設置座標軸
ax.set_xlabel('Miles')
ax.set_ylabel('Num of Deliveries')
ax.set_zlabel('Time')
#顯示圖像
plt.show()