# _*_ coding : utf-8 _*_
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
class LinearRegression(object):
def __init__(self,learning_rate=0.01,max_iter=100,seed=None):
np.random.seed(seed)
self.lr = learning_rate
self.max_iter = max_iter
self.w = np.random.normal(1,0.1)
self.b = np.random.normal(1,0.1)
self.loss_arr = []
def fit(self,x,y):
self.x = x
self.y = y
for i in range(self.max_iter):
self._train_step()
self.loss_arr.append(self.loss())
def model(self,x,w,b):
return x * w + b
def predict(self,x=None):
if x is None:
x = self.x
y_pred = self.model(x,self.w,self.b)
return y_pred
def loss(self,y_true=None,y_pred=None):
if y_true is None or y_pred is None:
y_true = self.y
y_pred = self.predict(self.x)
return np.mean((y_true - y_pred)**2)
def _calc_gradient(self):
d_w = np.mean((self.x * self.w + self.b - self.y) * self.x)
d_b = np.mean((self.x * self.w + self.b - self.y))
return d_w,d_b
def _train_step(self):
d_w,d_b = self._calc_gradient()
self.w = self.w - self.lr * d_w
self.b = self.b - self.lr * d_b
return self.w,self.b
def generate_data():
np.random.seed(272)
data_size = 100
X = np.random.uniform(low=1.0,high=10.0,size=data_size)
y = X * 20 + 10 + np.random.normal(loc=0.0,scale=10.0,size=data_size)
return pd.DataFrame({"X":X,"y":y})
if __name__ == '__main__':
data = np.array(generate_data())
x = data[:,0]
y = data[:,1]
regr = LinearRegression(learning_rate=0.01,max_iter=10,seed=111)
regr.fit(x,y)
def show_data(x, y, w=None, b=None):
plt.scatter(x, y, marker='.')
if w is not None and b is not None:
plt.plot(x, w * x + b, c='red')
plt.show()
show_data(x, y, regr.w, regr.b)
plt.scatter(np.arange(len(regr.loss_arr)), regr.loss_arr, marker='o', c='green')
plt.show()
