human
I am working on the file "Paper/SGD algorithm paper.ipynb".
The first task is:
The purpose of SVM is to find a weight vector $w$ and intercept $b$ such that the line described by $w_0X_1+w_0X_2+b=0$ best separates the two classes of points. Stochastic gradient descent will obtain the optimal $w$ and $b$ from the data. 
Implementation in Python
Can you write Python program for it?
assistant
#Here is Python program to find a weight vector $w$ and intercept $b$ such that the line described by $w_0X_1+w_0X_2+b=0$ best separates the two classes of points.

class Lossfunction:
    def loss(self, p, y):
        return 0
    def dloss(self, p, y):
        return 0

class Hinge(Lossfunction):
    def __init__(self, threshold=1):
        self.threshold=threshold
    def loss(self, p, y):
        z=p*y
        
        if z<=self.threshold:
            return (self.threshold-z)
        return 0
    def _dloss(self, p ,y):
        z= p*y
        if z<=self.threshold:
            return -y
        return 0

import numpy as np
class sgddata:
    def __init__(self, X, Y, sample_weights=0,seed=None):
        if len(X)!=len(Y):
            raise IndexError('X, Y not same length')
        self.X = np.array(X)
        self.Y = np.array(Y)
        self.sample_weights = sample_weights
        self.n_samples = self.X.shape[0]
        self.n_features = self.X.shape[1]
        self.current_index = -1
    def __next__(self):
        self.current_index += 1
        return self.X[self.current_index], self.Y[self.current_index]
    def _reset(self):
        self.current_index = -1
    def shuffle(self ,seed=None):
        np.random.seed(seed)
        idx = np.random.permutation(self.n_samples)
        self.X = self.X[idx]
        self.Y = self.Y[idx]
    def __str__(self):
        return '{},{}'.format(str(self.X), str(self.Y))

from time import time
def sgd( weights,
               intercept,
               loss,
               penalty_type,
               alpha,  
               dataset,
               n_iter, fit_intercept=True,
               verbose=False, shuffle=True, seed=None,
               weight_pos=1, weight_neg=1,
               eta0=0,
               t=1.0,
               intercept_decay=1.0):
    MAX_DLOSS = 1e12
    eta = eta0
    l1_ratio = 0.0
    sumlosslist = []
    n_samples=dataset.n_samples
    typw = np.sqrt(1.0 / np.sqrt(alpha))
    # computing eta0, the initial learning rate
    initial_eta0 = typw / max(1.0, loss.dloss(-typw, 1.0))
    # initialize t such that eta at first sample equals eta0
    optimal_init = 1.0 / (initial_eta0 * alpha)
    t_start =...