I was working on an simplified action descriptors for action detection using the bounded Dense trajectory, where my final step was to predict the action by training the action descriptors with SVM. Since my whole code was in python, I wanted a python based implementation of libsvm which I found in Scikit learns.
The problem with the Scikit was it had a few default kernels.
but I wanted chi square kernel as this is the most used kernel for histogram data and I was unable to construct custom kernel . After a brief amount of browsing and going through the Scikit Documentation. I found there are several ways to include the other kinds of kernels
First way was to use Scikit's Pairwise Metrics. Apart from the above mentioned kernels, we have two new kernels
metrics.pairwise.additive_chi2_kernel (X[, Y])Computes the additive chi-squared kernel between observations in X and Y
metrics.pairwise.chi2_kernel (X[, Y, gamma])Computes the exponential chi-squared kernel X and Y.
Usage:
from metrics.pairwise.additive import chi2_kernel
>>> Y = [0, 1, 2, 3]
>>> clf = svm.SVC(kernel=chi2_kernel)
>>> clf.fit(X, Y) SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0, kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False)
>>> test=[[0],[1],[1].[2]]
>>> clf.predict(test)
Second way is to use the approximations
kernel_approximation.SkewedChi2Sampler ( [...])Approximates feature map of the “skewed chi-squared” kernel by Monte
Usage:
>>> from sklearn.kernel_approximation import SkewedChi2Sampler
>>> X = [[0, 0], [1, 1], [1, 0], [0, 1]]
>>> y = [0, 0, 1, 1]
>>> chi2_feature = SkewedChi2Sampler(sample_step=1. sample_interval=3)
>>> X_features = rbf_feature.fit_transform(X)
>>> clf = SGDClassifier()
>>> clf.fit(X_features, y)
SGDClassifier(alpha=0.0001, class_weight=None, epsilon=0.1, eta0=0.0, fit_intercept=True, l1_ratio=0.15, learning_rate='optimal', loss='hinge', n_iter=5, n_jobs=1, penalty='l2', power_t=0.5, random_state=None, rho=None, shuffle=False, verbose=0, warm_)
>>> clf.predict([[1.0],[1,1]])
you can see how these kernels can be implemented in one of my projects here
The problem with the Scikit was it had a few default kernels.
- linear:
- polynomial:
- rbf:
- sigmoid:
but I wanted chi square kernel as this is the most used kernel for histogram data and I was unable to construct custom kernel . After a brief amount of browsing and going through the Scikit Documentation. I found there are several ways to include the other kinds of kernels
First way was to use Scikit's Pairwise Metrics. Apart from the above mentioned kernels, we have two new kernels
metrics.pairwise.additive_chi2_kernel (X[, Y])Computes the additive chi-squared kernel between observations in X and Y
metrics.pairwise.chi2_kernel (X[, Y, gamma])Computes the exponential chi-squared kernel X and Y.
Usage:
from metrics.pairwise.additive import chi2_kernel
>>> Y = [0, 1, 2, 3]
>>> clf = svm.SVC(kernel=chi2_kernel)
>>> clf.fit(X, Y) SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0, kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False)
>>> test=[[0],[1],[1].[2]]
>>> clf.predict(test)
Second way is to use the approximations
kernel_approximation.SkewedChi2Sampler ( [...])Approximates feature map of the “skewed chi-squared” kernel by Monte
Usage:
>>> from sklearn.kernel_approximation import SkewedChi2Sampler
>>> X = [[0, 0], [1, 1], [1, 0], [0, 1]]
>>> y = [0, 0, 1, 1]
>>> chi2_feature = SkewedChi2Sampler(sample_step=1. sample_interval=3)
>>> X_features = rbf_feature.fit_transform(X)
>>> clf = SGDClassifier()
>>> clf.fit(X_features, y)
SGDClassifier(alpha=0.0001, class_weight=None, epsilon=0.1, eta0=0.0, fit_intercept=True, l1_ratio=0.15, learning_rate='optimal', loss='hinge', n_iter=5, n_jobs=1, penalty='l2', power_t=0.5, random_state=None, rho=None, shuffle=False, verbose=0, warm_)
>>> clf.predict([[1.0],[1,1]])
you can see how these kernels can be implemented in one of my projects here
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