为什么我的python代码比R代码慢很多

Question

Update

Finally I found the mistake... The bug is in my full code. I am a beginner in py, so... Thanks @mrdomoboto to point one error. Thanks @Spacedman let me to create a reproducible example, such that I can go back to look at my full code.

So sorry for the my noise... I will more carefully check my code before ask question. Should I delete it?

#

I am just doing some experiments on perceptron algorithm, and try to learn python with this practice. So I both implemented it in R and python. However I found my python code is around 10 times slower than my R code. Actually, I almost directly translate my R code into python. I really want to know what is the reason. Please point out the problem from my poor code.

R code:

def Perceptron(X,y,ini=(0,0,0)):
    w = np.array(ini)
    N = X.shape[0]
    # add ones as the first columns of X
    X = np.hstack((np.ones(N).reshape(N,1), X))
    go_next = True
    while go_next:
        cont = 0
        for i in range(N):
            if np.sign(X[i,:].dot(w)*y[i]) == 1:
                cont = cont + 1
            else: w = w + y[i]*X[i,:]
            if cont==N: go_next=False
    return w

My py code:

 def Perceptron(X,y,ini=(0,0,0)): w = np.array(ini) N = X.shape[0] # add ones as the first columns of X X = np.hstack((np.ones(N).reshape(N,1), X)) go_next = True while go_next: cont = 0 for i in range(N): if np.sign(X[i,:].dot(w)*y[i]) == 1: cont = cont + 1 else: w = w + y[i]*X[i,:] if cont==N: go_next=False return w 

Answer 1

Any computations inside the inner most loops of algorithms will slow programs by an order of magnitude greater than if they were inside the outer closest lexical scope.

if cont==N: go_next=False should be moved outside of the inner most for loop ( as it is in your R program ).

Take a look at computational analysis .