Perceptron Algorithm Confusion-CodePudding

I have this problem that I'm trying to solve:

def phi(x):
    # DO NOTHING ON THIS FUNCTION
    if x<=0:
        return -1.0
    else:
        return 1.0
    
phi = np.vectorize(phi)

This is where I need to implement the function:

def predictOne(x, w):
    z =  
    return phi(z)

But when I try to use my formula but with my input code:

def predictOne(x, w):
    z =  0   x * w
    return phi(z)

And run my asserts:

assert predictOne(np.array([0.0,0.0]) , np.array([0.1,3.2,7.4])) == 1.0
assert predictOne(np.array([0.0,0.0]), np.array([-0.1,3.2,7.4])) == -1.0
assert predictOne(np.array([0.3,-0.7]), np.array([0.1,3.2,7.4])) == -1.0
assert predictOne(np.array([0.3,0.7]), np.array([0.1,3.2,7.4])) == 1.0

I get an (operands could not be broadcast together with shapes (2,) (3,)

Apparently, the assertions are correct so I'm doing somethin wrong in my predictOne function. Can anyone help?

CodePudding user response：

The problem is in the formula:

def predictOne(x, w):
    z = w[0]   np.sum(x * w[1:])
    return phi(z)

w[0] is bias. Bias of zero, as pointed out by @mkrieger, doesn't make a lot of sense.
perceptron essentially calculates weighted sum of inputs, so the result should be summed up.