我是机器学习新手,正在尝试实现梯度下降。我编写的代码如下所示,并为所有参数生成了NaN值:
def compute_error_for_line_given_points(b,m,points):
totalError = 0 #sum of square error formula
for i in range (0, len(points)):
x = points[i, 0]
y = points[i, 1]
totalError += (y-(m*x + b)) ** 2
return totalError/ float(len(points))
def step_gradient(b_current, m_current, points, learning_rate):
#gradient descent
b_gradient = 0
m_gradient = 0
N = float(len(points))
for i in range(0, len(points)):
x = points[i, 0]
y = points[i, 1]
b_gradient += -(2/N) * (y - (m_current * x + b_current))
m_gradient += -(2/N) * x * (y - (m_current * x + b_current))
new_b = b_current - (learning_rate * b_gradient)
new_m = m_current - (learning_rate * m_gradient)
return [new_b,new_m]
def gradient_descent_runner(points, starting_b, starting_m, learning_rate, num_iterations):
b = starting_b
m = starting_m
for i in range(num_iterations):
b,m = step_gradient(b, m, array(points), learning_rate)
return [b,m]
def run():
#Step 1: Collect the data
points = genfromtxt("C:/Users/mishruti/Downloads/For Linear Regression.csv", delimiter = ",")
#Step 2: Define our Hyperparameters
learning_rate = 0.0000001 #how fast the data converge
#y=mx+b (Slope formule)
initial_b = 0 # initial y-intercept guess
initial_m = 0 # initial slope guess
num_iterations = 4
print("Starting gradient descent at b = {0}, m = {1}, error = {2}".format(initial_b, initial_m, compute_error_for_line_given_points(initial_b, initial_m, points)))
print("Running...")
[b, m] = gradient_descent_runner(points, initial_b, initial_m, learning_rate, num_iterations)
print("After {0} iterations b = {1}, m = {2}, error = {3}".format(num_iterations, b, m, compute_error_for_line_given_points(b, m, points)))
# main function
if __name__ == "__main__":
run()
附上我的数据集样本。有人能帮我弄清楚吗?谢谢
目前没有回答
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