SVM : Python

# Importing modules

# shows comments

# code is straight forward

%matplotlib inline

import numpy as np

import matplotlib.pyplot as plt

from scipy import stats


# use seaborn plotting defaults

import seaborn as sns; sns.set()

from sklearn.datasets.samples_generator import make_blobs

X,y = make_blobs(n_samples=50,centers=2,random_state=0, cluster_std=0.60)

plt.scatter(X[:,0],X[:,1],c=y,s=50,cmap='autumn')

xfit = np.linspace(-1, 3.5)

plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')

plt.plot([0.6], [2.1], 'x', color='red', markeredgewidth=2, markersize=10)

for m, b in [(1, 0.65), (0.5, 1.6), (-0.2, 2.9)]:

    plt.plot(xfit, m * xfit + b, '-k')

plt.xlim(-1, 3.5);

xfit = np.linspace(-1, 3.5)

plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')

for m, b, d in [(1, 0.65, 0.33), (0.5, 1.6, 0.55), (-0.2, 2.9, 0.2)]:

    yfit = m * xfit + b

    plt.plot(xfit, yfit, '-k')

    plt.fill_between(xfit, yfit - d, yfit + d, edgecolor='none', color='#AAAAAA',alpha=0.4)

plt.xlim(-1, 3.5);

  
from sklearn.svm import SVC # "Support vector classifier"

model = SVC(kernel='linear', C=1E10)

model.fit(X, y)



# SVC(C=10000000000.0, break_ties=False, cache_size=200, class_weight=None,

    coef0=0.0, decision_function_shape='ovr', degree=3, gamma='scale',
    kernel='linear', max_iter=-1, probability=False, random_state=None,
    shrinking=True, tol=0.001, verbose=False)

def plot_svc_decision_function(model, ax=None, plot_support=True):

    """Plot the decision function for a two-dimensional SVC"""

    if ax is None:

        ax = plt.gca()

    xlim = ax.get_xlim()

    ylim = ax.get_ylim()

   

    # create grid to evaluate model

    x = np.linspace(xlim[0], xlim[1], 30)

    y = np.linspace(ylim[0], ylim[1], 30)

    Y, X = np.meshgrid(y, x)

    xy = np.vstack([X.ravel(), Y.ravel()]).T

    P = model.decision_function(xy).reshape(X.shape)

   

    # plot decision boundary and margins

    ax.contour(X, Y, P, colors='k',levels=[-1, 0, 1], alpha=0.5,linestyles=['--', '-', '--'])



    # plot support vectors

    if plot_support:

        ax.scatter(model.support_vectors_[:, 0],model.support_vectors_[:, 1],s=300, linewidth=1, facecolors='none');

    ax.set_xlim(xlim)

    ax.set_ylim(ylim)





plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')

plot_svc_decision_function(model);

model.support_vectors_



# array([[0.44359863, 3.11530945], [2.33812285, 3.43116792],[2.06156753, 1.96918596]])

def plot_svm(N=10, ax=None):

    X, y = make_blobs(n_samples=200, centers=2,random_state=0, cluster_std=0.60)

    X = X[:N]

    y = y[:N]

    model = SVC(kernel='linear', C=1E10)

    model.fit(X, y)

   

    ax = ax or plt.gca()

    ax.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')

    ax.set_xlim(-1, 4)

    ax.set_ylim(-1, 6)

    plot_svc_decision_function(model, ax)



fig, ax = plt.subplots(1, 2, figsize=(16, 6))

fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)

for axi, N in zip(ax, [60, 120]):

    plot_svm(N, axi)

    axi.set_title('N = {0}'.format(N))

from ipywidgets import interact, fixed

interact(plot_svm, N=[10, 200], ax=fixed(None));