[PYTHON] Linear Discriminator-Fisher's Linear Discriminant Function, Simple Perceptron, IRLS

For those who want to quickly know what kind of classification hyperplane the Fisher's linear discriminator, simple perceptron, and IRLS (repetitive reweighting least squares), which are famous as linear discriminators, draw.

The following two classes are generated from the Gaussian distribution and their boundaries are calculated.

*Class 1: 80 piecesN(x | \mu_{1}, \Sigma_{1})From, 20 piecesN(x|\mu_{1}', \Sigma_{1}')Generated from

• Class 2: Generate 100 from $ N (x | \ mu_ {2}, \ Sigma_ {2}) $

\Sigma_{1} = \Sigma_{1}' = \Sigma_{2} = ((30, 10), (10, 15))^{-1}

\mu_{1} = (0, 0)^{T}

\mu_{1}' = (-2, -2)^{T}

\mu_{2} = (1, 1)^{T}

Implementation

import numpy as np
import matplotlib.pyplot as plt

def generate_data(mu, cov, num_data):
    cls1 = np.random.multivariate_normal(mu[0], cov, num_data[0])
    cls1_ = np.random.multivariate_normal(mu[1], cov, num_data[1])
    cls2 = np.random.multivariate_normal(mu[2], cov, num_data[2])

    return np.r_[cls1, cls1_], cls2

def plot(filename, cls1, cls2, spr=None):
    x1, x2 = cls1.T
    plt.plot(x1, x2, "bo")
    x1, x2 = cls2.T
    plt.plot(x1, x2, "ro")
    
    if not spr is None:
        plt.plot(spr[0], spr[1], "g-")

    plt.xlim(-3, 3)
    plt.ylim(-3, 3)
    plt.savefig(filename)
    plt.clf()

def step(out):
    out = out >= 0.
    out = out.astype(float)
    for i in range(len(out[0])):
        if out[0][i] == 0.:
            out[0][i] = -1.

    return out

def sigmoid(x):
    return 1./(1.+np.exp(-x))

def fisher(cls1, cls2):
    m1 = np.mean(cls1, axis=0)
    m2 = np.mean(cls2, axis=0)

    dim = len(m1)

    Sw = np.zeros((dim, dim))
    for i in range(len(cls1)):
        xi = np.array(cls1[i]).reshape(dim, 1)
        m1 = np.array(m1).reshape(dim, 1)
        Sw += np.dot((xi - m1), (xi - m1).T)
    for i in range(len(cls2)):
        xi = np.array(cls2[i]).reshape(dim, 1)
        m2 = np.array(m2).reshape(dim, 1)
        Sw += np.dot((xi - m2), (xi - m2).T)
    Sw_inv = np.linalg.inv(Sw)
    w = np.dot(Sw_inv, (m2 - m1))

    m = (m1 + m2) / 2.
    b = -sum(w*m)
    
    x = np.linspace(-3, 3, 1000)
    y = [(w[0][0]*xs+b)/(-w[1][0]) for xs in x]
    plot("fisher.png ", cls1, cls2, (x, y))

def perceptron(cls1, cls2, lr=0.5, loop=1000):
    cls1_ = np.c_[cls1, np.ones((len(cls1))), np.ones((len(cls1)))]
    cls2_ = np.c_[cls2, np.ones((len(cls2))), -1*np.ones((len(cls2)))]

    data = np.r_[cls1_, cls2_]
    np.random.shuffle(data)
    data, label = np.hsplit(data, [len(data[0])-1])
    w = np.random.uniform(-1., 1., size=(1, len(data[0])))

    for i in range(loop):
        out = np.dot(w, data.T)
        out = step(out)
        dw = lr * (label - out.T) * data
        w += np.mean(dw, axis=0)
    
    x = np.linspace(-3, 3, 1000)
    y = [(w[0][0]*xs+w[0][2])/(-w[0][1]) for xs in x]
    plot("perceptron.png ", cls1, cls2, (x, y))

def IRLS(cls1, cls2, tol=1e-5, maxits=100):
    cls1_ = np.c_[cls1, np.ones((len(cls1))), np.ones((len(cls1)))]
    cls2_ = np.c_[cls2, np.ones((len(cls2))), np.zeros((len(cls2)))]

    data = np.r_[cls1_, cls2_]
    np.random.shuffle(data)
    data, label = np.hsplit(data, [len(data[0])-1])
    w = np.zeros((1, len(data[0])))
    
    itr=0
    while(itr < maxits):
        y = sigmoid(np.dot(w, data.T)).T
        g = np.dot(data.T, (y - label))
        rn = y.T*(1-y.T)
        r = np.diag(rn[0])
        hesse = np.dot(np.dot(data.T, r), data)
        diff = np.dot(np.dot(np.linalg.inv(hesse), data.T), (y - label))
        w -= diff.T
        if np.sum(g**2) <= tol:
            print(itr)
            break
        itr += 1
    
    x = np.linspace(-3, 3, 1000)
    y = [(w[0][0]*xs+w[0][2])/(-w[0][1]) for xs in x]
    plot("IRLS.png ", cls1, cls2, (x, y))

if __name__ == "__main__":
    mu = [[0., 0.], [-2., -2.], [1. ,1.]]
    cov = np.linalg.inv([[30., 10.], [10., 15.]])
    num_data = [80, 20, 100]

    cls1, cls2 = generate_data(mu, cov, num_data)
    plot("data.png ", cls1, cls2)
    fisher(cls1, cls2)
    perceptron(cls1, cls2)
    IRLS(cls1, cls2)

result

First of all, Fisher's linear classifier You can see that it is greatly pulled by the outliers.

Then simple perceptron Regardless of whether the generalization performance is good, it is a classification hyperplane that can classify training data. Also, it is not pulled by outliers.

Finally IRLS It is not affected by outliers and seems to have good generalization performance.