kernel_other_opt_function.py

def liklihood_loss(x,y,w):
    hypothesis = x.dot(w)
    hypothesis = 1/(1+np.exp(-1*hypothesis))
    hypothesis[hypothesis<=0.00001] = 0.00001
    loss = -1*((1-y).T.dot(np.log(1-hypothesis)) + y.T.dot(np.log(hypothesis)))/len(y)
    return loss.flatten()[0]

def sin_least_sqs_loss(x,y,w):
    hypothesis = w[0]*x[:,0:1] + np.cos(x[:,1:2]*w[1]-w[2])*w[3]
    loss = hypothesis-y
    return np.sum(loss**2)/len(y)
  
def complex_sin_least_sqs_loss(x,y,w):
    hypothesis = w[0]*x[:,0:1] + w[6]*(np.cos(x[:,1:2]*w[1]-w[2])*w[3] + np.sin(x[:,1:2]*w[4]-w[5])*w[3])
    loss = hypothesis-y
    return np.sum(loss**2)/len(y)