I am trying to undersand how the Perceptron's weights are calculated, for example, with this method fit:
def fit(self, X,y):
self.w_ = np.zeros(1 + X.shape[1])
self.errors_ = []
for _ in range(self.n_iter):
errors = 0
for xi, target in zip(X, y):
update = self.eta * (target - self.predict(xi))
self.w_[1:] += update * xi
self.w_[0] += update
errors += int(update !=0.0)
self.errors_.append(errors)
return self
Let's imagine that on the first iteration of the for loop we have:
xi = array([5.1, 1.4])
target = int(-1)
self.eta = float(0.01)
self.w_=array([0., 0., 0.])
Then self.predict(xi) occurs to get update:
def predict(self,X):
return np.where(self.net_input(X) >= 0.0, 1, -1)
And it calls self.net_input(X):
def net_input(self,X):
return np.dot(X, self.w_[1:]) + self.w_[0]
Then we have these calculations:
np.where(X, self.w_[1:]) + self.w_[0] equals ([5.1, 1.4]*[0.,0.]) + 0 = 0
np.where(self.net_input(X) >= 0.0, 1, -1) equals 1 (because *self.net_input(X)* = 0)
update = self.eta * (target - self.predict(xi)) equals update = 0.01 * (-1-1) = -0.02
self.w_[1:] += update * xi equals [0.,0.] += -0.02 * 0.01 = [-0.0002, -0.0002]
self.w_[0] = update(-0.02)
And that's what we 'have':
self.w_ = array([-0.02 , -0.0002, -0.0002])
However, what I see after first the first iteration on the breakpoint is:
self.w_ = array([-0.02 , -0.102, -0.028])
I started learning ML 2 days ago, so maybe I'm missing something important?
P.S. Code is working well
hello bloowy im here to tell you to use sigmoid which equals to softmaxx**2e andl ike this you can find a solution to your problem