“regresión lineal” Código de respuesta

regresión lineal

from sklearn.linear_model import LinearRegression
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
y = np.dot(X, np.array([1, 2])) + 3
reg = LinearRegression().fit(X, y)
reg.score(X, y)
reg.coef_
reg.intercept_
reg.predict(np.array([[3, 5]]))
Zany Zebra

regresión lineal

# Implementation of gradient descent in linear regression
import numpy as np
import matplotlib.pyplot as plt
 
class Linear_Regression:
    def __init__(self, X, Y):
        self.X = X
        self.Y = Y
        self.b = [0, 0]
     
    def update_coeffs(self, learning_rate):
        Y_pred = self.predict()
        Y = self.Y
        m = len(Y)
        self.b[0] = self.b[0] - (learning_rate * ((1/m) *
                                np.sum(Y_pred - Y)))
 
        self.b[1] = self.b[1] - (learning_rate * ((1/m) *
                                np.sum((Y_pred - Y) * self.X)))
 
    def predict(self, X=[]):
        Y_pred = np.array([])
        if not X: X = self.X
        b = self.b
        for x in X:
            Y_pred = np.append(Y_pred, b[0] + (b[1] * x))
 
        return Y_pred
     
    def get_current_accuracy(self, Y_pred):
        p, e = Y_pred, self.Y
        n = len(Y_pred)
        return 1-sum(
            [
                abs(p[i]-e[i])/e[i]
                for i in range(n)
                if e[i] != 0]
        )/n
    #def predict(self, b, yi):
 
    def compute_cost(self, Y_pred):
        m = len(self.Y)
        J = (1 / 2*m) * (np.sum(Y_pred - self.Y)**2)
        return J
 
    def plot_best_fit(self, Y_pred, fig):
                f = plt.figure(fig)
                plt.scatter(self.X, self.Y, color='b')
                plt.plot(self.X, Y_pred, color='g')
                f.show()
 
 
def main():
    X = np.array([i for i in range(11)])
    Y = np.array([2*i for i in range(11)])
 
    regressor = Linear_Regression(X, Y)
 
    iterations = 0
    steps = 100
    learning_rate = 0.01
    costs = []
     
    #original best-fit line
    Y_pred = regressor.predict()
    regressor.plot_best_fit(Y_pred, 'Initial Best Fit Line')
     
 
    while 1:
        Y_pred = regressor.predict()
        cost = regressor.compute_cost(Y_pred)
        costs.append(cost)
        regressor.update_coeffs(learning_rate)
         
        iterations += 1
        if iterations % steps == 0:
            print(iterations, "epochs elapsed")
            print("Current accuracy is :",
                regressor.get_current_accuracy(Y_pred))
 
            stop = input("Do you want to stop (y/*)??")
            if stop == "y":
                break
 
    #final best-fit line
    regressor.plot_best_fit(Y_pred, 'Final Best Fit Line')
 
    #plot to verify cost function decreases
    h = plt.figure('Verification')
    plt.plot(range(iterations), costs, color='b')
    h.show()
 
    # if user wants to predict using the regressor:
    regressor.predict([i for i in range(10)])
 
if __name__ == '__main__':
    main()
Alvin Saini

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