Python gradient-descent multi-regression - cost increases to infinity

问题

Writing this algorithm for my final year project. Used gradient descent to find the minimum, but instead getting the cost as high as infinity.

I have checked the gradientDescent function. I believe that's correct.

The csv I am importing and its formatting is causing some error. The data in the CSV is of below format.

Each quad before '|' is a row.

First 3 columns are independent variables x. 4th column is dependent y.

600 20 0.5 0.63 | 600 20 1 1.5 | 800 20 0.5 0.9

import numpy as np
import random
import pandas as pd

def gradientDescent(x, y, theta, alpha, m, numIterations):
    xTrans = x.transpose()
    for i in range(0, numIterations):
        hypothesis = np.dot(x, theta)
        loss = hypothesis - y
        # avg cost per example (the 2 in 2*m doesn't really matter here.
        # But to be consistent with the gradient, I include it)
        cost = np.sum(loss ** 2) / (2 * m)
        print("Iteration %d | Cost: %f" % (i, cost))
        # avg gradient per example
        gradient = np.dot(xTrans, loss) / m
        # update
        theta = theta - alpha * gradient
    return theta

df = pd.read_csv(r'C:\Users\WELCOME\Desktop\FinalYearPaper\ConferencePaper\NewTrain.csv', 'rU', delimiter=",",header=None)

x = df.loc[:,'0':'2'].as_matrix()
y = df[3].as_matrix()

print(x)
print(y)

m, n = np.shape(x)
numIterations= 100
alpha = 0.001
theta = np.ones(n)
theta = gradientDescent(x, y, theta, alpha, m, numIterations)
print(theta)

回答1:

As forayer mentioned in the comments, the problem is in the line where you read the csv. You are setting delimiter=",", which means that python expects each column in your data to be separated by a comma. However, in your data, columns are apparently separated by a whitespace.

Just substitute the line with

df = pd.read_csv(r'C:\Users\WELCOME\Desktop\FinalYearPaper\ConferencePaper\NewTrain.csv', 'rU', delimiter=" ",header=None)

来源：https://stackoverflow.com/questions/49640823/python-gradient-descent-multi-regression-cost-increases-to-infinity

标签

python

pandas

numpy

machine-learning

gradient-descent