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This repository was archived by the owner on May 13, 2023. It is now read-only.

Commit 3f0e8b7

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Create svm_smo.py
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‎svm_smo.py‎

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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.datasets import make_blobs, make_circles, make_moons
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from sklearn.preprocessing import StandardScaler
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from scipy.misc import imresize
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# Set tolerances
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tol = 0.01 # error tolerance
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eps = 0.01 # alpha tolerance
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class SMOModel:
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"""Container object for the model used for sequential minimal optimization."""
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def __init__(self, X, y, C, kernel, alphas, b, errors):
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self.X = X # training data vector
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self.y = y # class label vector
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self.C = C # regularization parameter
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self.kernel = kernel # kernel function
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self.alphas = alphas # lagrange multiplier vector
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self.b = b # scalar bias term
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self.errors = errors # error cache
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self._obj = [] # record of objective function value
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self.m = len(self.X) # store size of training set
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def linear(x, y, b=1):
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"""
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Computes the linear kernel between x and y
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Args:
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b: Bias (a scalar)
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x: array
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y: array
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Returns:
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Linear kernel between x and y
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"""
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result = None
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#######################################################################
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# TODO: #
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# Compute the linear kernel between x and y #
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#######################################################################
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result = np.dot(x, y.T)+b
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pass
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#######################################################################
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# END OF YOUR CODE #
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#######################################################################
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return result
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def gaussian(x, y, sigma=1):
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"""
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Computes the gaussian kernel between x and y
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Args:
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x: array
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y: array
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sigma: scalar
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Returns:
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Gaussian similarity
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"""
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result = None
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#######################################################################
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# TODO: #
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# Compute the Gaussian kernel between x and y #
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#######################################################################
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#result = np.exp(-(np.absolute(x-y)**2)/2*(sigma**2))
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if np.ndim(x) == 1 and np.ndim(y) == 1:
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result = np.exp(- np.linalg.norm(x - y) / (2 * sigma ** 2))
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elif (np.ndim(x) > 1 and np.ndim(y) == 1) or (np.ndim(x) == 1 and np.ndim(y) > 1):
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result = np.exp(- np.linalg.norm(x - y, axis=1) / (2 * sigma ** 2))
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elif np.ndim(x) > 1 and np.ndim(y) > 1:
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result = np.exp(- np.linalg.norm(x[:, np.newaxis] - y[np.newaxis, :], axis=2) / (2 * sigma ** 2))
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pass
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#######################################################################
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# END OF YOUR CODE #
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#######################################################################
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return result
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def objective_function(alphas, y,kernel, X):
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"""
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Computes the objective function
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Args:
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alphas: Lagrangian multipliers
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y: class labels -1 or 1
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X: training data
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Returns:
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Value of the objective function
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"""
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result = None
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#######################################################################
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# TODO: #
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# Compute the objective function #
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#######################################################################
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result = np.sum(alphas) - 0.5 * np.sum(y * y * kernel(X, X) * alphas * alphas) #correct
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pass
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#######################################################################
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# END OF YOUR CODE #
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#######################################################################
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return result
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# Decision function
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def decision_function(alphas, target, kernel, X_train, x_test, b):
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"""
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Compute the decision function
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Args:
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alphas: Lagrangian multipliers
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y: class labels -1 or 1
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X: training/test data
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Returns:
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Output of decision function
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"""
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result = None
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#######################################################################
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# TODO: #
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# Compute the decision function #
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#######################################################################
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#result = (alphas * target) @ kernel(X_train, x_test) - b #correct
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result = np.dot((alphas * target), kernel(X_train, x_test)) - b
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pass
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#######################################################################
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# END OF YOUR CODE #
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#######################################################################
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return result
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def plot_decision_boundary(model, ax, resolution=100, colors=('b', 'k', 'r')):
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"""Plots the model's decision boundary on the input axes object.
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Range of decision boundary grid is determined by the training data.
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Returns decision boundary grid and axes object (`grid`, `ax`)."""
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# Generate coordinate grid of shape [resolution x resolution]
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# and evaluate the model over the entire space
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xrange = np.linspace(model.X[:,0].min(), model.X[:,0].max(), resolution)
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yrange = np.linspace(model.X[:,1].min(), model.X[:,1].max(), resolution)
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grid = [[decision_function(model.alphas, model.y,
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model.kernel, model.X,
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np.array([xr, yr]), model.b) for yr in yrange] for xr in xrange]
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grid = np.array(grid).reshape(len(xrange), len(yrange))
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# Plot decision contours using grid and
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# make a scatter plot of training data
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ax.contour(xrange, yrange, grid, (-1, 0, 1), linewidths=(1, 1, 1),
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linestyles=('--', '-', '--'), colors=colors)
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ax.scatter(model.X[:,0], model.X[:,1],
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c=model.y, cmap=plt.cm.viridis, lw=0, alpha=0.5)
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# Plot support vectors (non-zero alphas)
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# as circled points (linewidth > 0)
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mask = model.alphas != 0.0
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ax.scatter(model.X[:,0][mask], model.X[:,1][mask],
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c=model.y[mask], cmap=plt.cm.viridis)
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return grid, ax
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def take_step(i1, i2, model):
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# Skip if chosen alphas are the same
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if i1 == i2:
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return 0, model
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alph1 = model.alphas[i1]
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alph2 = model.alphas[i2]
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y1 = model.y[i1]
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y2 = model.y[i2]
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E1 = model.errors[i1]
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E2 = model.errors[i2]
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s = y1 * y2
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# Compute L & H, the bounds on new possible alpha values
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if (y1 != y2):
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L = max(0, alph2 - alph1)
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H = min(model.C, model.C + alph2 - alph1)
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elif (y1 == y2):
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L = max(0, alph1 + alph2 - model.C)
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H = min(model.C, alph1 + alph2)
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if (L == H):
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return 0, model
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# Compute kernel & 2nd derivative eta
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k11 = model.kernel(model.X[i1], model.X[i1])
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k12 = model.kernel(model.X[i1], model.X[i2])
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k22 = model.kernel(model.X[i2], model.X[i2])
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eta = 2 * k12 - k11 - k22
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# Compute new alpha 2 (a2) if eta is negative
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if (eta < 0):
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a2 = alph2 - y2 * (E1 - E2) / eta
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# Clip a2 based on bounds L & H
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#######################################################################
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# TODO: #
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# Clip a2 based on the last equation in the notes #
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#######################################################################
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if L < a2 < H:
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a2 = a2
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elif (a2 <= L):
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a2 = L
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elif (a2 >= H):
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a2 = H
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pass
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#######################################################################
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# END OF YOUR CODE #
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#######################################################################
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# If eta is non-negative, move new a2 to bound with greater objective function value
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else:
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alphas_adj = model.alphas.copy()
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alphas_adj[i2] = L
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# objective function output with a2 = L
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Lobj = objective_function(alphas_adj, model.y, model.kernel, model.X)
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alphas_adj[i2] = H
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# objective function output with a2 = H
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Hobj = objective_function(alphas_adj, model.y, model.kernel, model.X)
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if Lobj > (Hobj + eps):
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a2 = L
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elif Lobj < (Hobj - eps):
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a2 = H
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else:
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a2 = alph2
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# Push a2 to 0 or C if very close
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if a2 < 1e-8:
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a2 = 0.0
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elif a2 > (model.C - 1e-8):
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a2 = model.C
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# If examples can't be optimized within epsilon (eps), skip this pair
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if (np.abs(a2 - alph2) < eps * (a2 + alph2 + eps)):
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return 0, model
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# Calculate new alpha 1 (a1)
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a1 = alph1 + s * (alph2 - a2)
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# Update threshold b to reflect newly calculated alphas
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# Calculate both possible thresholds
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b1 = E1 + y1 * (a1 - alph1) * k11 + y2 * (a2 - alph2) * k12 + model.b
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b2 = E2 + y1 * (a1 - alph1) * k12 + y2 * (a2 - alph2) * k22 + model.b
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# Set new threshold based on if a1 or a2 is bound by L and/or H
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if 0 < a1 and a1 < model.C:
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b_new = b1
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elif 0 < a2 and a2 < model.C:
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b_new = b2
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# Average thresholds if both are bound
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else:
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b_new = (b1 + b2) * 0.5
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# Update model object with new alphas & threshold
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model.alphas[i1] = a1
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model.alphas[i2] = a2
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# Update error cache
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# Error cache for optimized alphas is set to 0 if they're unbound
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for index, alph in zip([i1, i2], [a1, a2]):
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if 0.0 < alph < model.C:
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model.errors[index] = 0.0
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# Set non-optimized errors based on equation 12.11 in Platt's book
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non_opt = [n for n in range(model.m) if (n != i1 and n != i2)]
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model.errors[non_opt] = model.errors[non_opt] + \
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y1*(a1 - alph1)*model.kernel(model.X[i1], model.X[non_opt]) + \
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y2*(a2 - alph2)*model.kernel(model.X[i2], model.X[non_opt]) + model.b - b_new
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# Update model threshold
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model.b = b_new
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return 1, model
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def examine_example(i2, model):
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y2 = model.y[i2]
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alph2 = model.alphas[i2]
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E2 = model.errors[i2]
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r2 = E2 * y2
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# Proceed if error is within specified tolerance (tol)
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if ((r2 < -tol and alph2 < model.C) or (r2 > tol and alph2 > 0)):
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if len(model.alphas[(model.alphas != 0) & (model.alphas != model.C)]) > 1:
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# Use 2nd choice heuristic is choose max difference in error
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if model.errors[i2] > 0:
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i1 = np.argmin(model.errors)
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elif model.errors[i2] <= 0:
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i1 = np.argmax(model.errors)
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step_result, model = take_step(i1, i2, model)
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if step_result:
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return 1, model
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# Loop through non-zero and non-C alphas, starting at a random point
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for i1 in np.roll(np.where((model.alphas != 0) & (model.alphas != model.C))[0],
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np.random.choice(np.arange(model.m))):
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step_result, model = take_step(i1, i2, model)
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if step_result:
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return 1, model
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# loop through all alphas, starting at a random point
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for i1 in np.roll(np.arange(model.m), np.random.choice(np.arange(model.m))):
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step_result, model = take_step(i1, i2, model)
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if step_result:
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return 1, model
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return 0, model
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def train(model):
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numChanged = 0
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examineAll = 1
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while(numChanged > 0) or (examineAll):
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numChanged = 0
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if examineAll:
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# loop over all training examples
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for i in range(model.alphas.shape[0]):
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examine_result, model = examine_example(i, model)
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numChanged += examine_result
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if examine_result:
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obj_result = objective_function(model.alphas, model.y, model.kernel, model.X)
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model._obj.append(obj_result)
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else:
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# loop over examples where alphas are not already at their limits
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for i in np.where((model.alphas != 0) & (model.alphas != model.C))[0]:
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examine_result, model = examine_example(i, model)
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numChanged += examine_result
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if examine_result:
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obj_result = objective_function(model.alphas, model.y, model.kernel, model.X)
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model._obj.append(obj_result)
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if examineAll == 1:
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examineAll = 0
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elif numChanged == 0:
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examineAll = 1
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return model
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