emilemathieu

class Module(object):
    """ Base class for neural network's layers
    """
    def forward(self, X):
        """ Apply the layer function to the input data
        Parameters
        ----------
        X : array-like, shape = [n_samples, depth_in, height_in, width_in]
        Returns
        -------

emilemathieu

def _compute_intercept(self, alpha, yg):
    indices = (alpha < self.C) * (alpha > 0)
    return np.mean(yg[indices])
        
def _compute_weights(self, X, y):
    iteration = 0
    n_samples = X.shape[0]
    alpha = np.zeros(n_samples) # Initialise coefficients to 0  w
    g = np.ones(n_samples) # Initialise gradients to 1

emilemathieu

import numpy as np

class Kernel(object):
    """Collection of usual kernels"""

    @staticmethod
    def linear():
        def f(x, y):
            return np.inner(x, y)
        return f

emilemathieu

class binary_classification(object):
    def __init__(self, kernel, C=1.0, max_iter=1000, tol=0.001):
        self.kernel = kernel # K(x_i, x_j) = <phi(x_i), phi(x_j)>
        self.C = C # penalty coefficient
        self.max_iter = max_iter # maximum number of iterations for solver
        self.tol = tol # tolerance for the solver

    def fit(self, X, y):
        # Compute coefficients of the dual problem
        lagrange_multipliers, intercept = self._compute_weights(X, y)