.. currentmodule:: mlpy

Support Vector Machines (SVMs)
==============================

Support Vector Machines from [LIBSVM]_
--------------------------------------

.. class:: mlpy.LibSvm(svm_type='c_svc', kernel_type='linear', degree=3, gamma=0.001, coef0=0, C=1, nu=0.5, eps=0.001, p=0.1, cache_size=100, shrinking=True, probability=False, weight={})
        
	LibSvm.
        
   	:Parameters:

           svm_type : string
               SVM type, can be one of: 'c_svc', 'nu_svc', 
               'one_class', 'epsilon_svr', 'nu_svr'
           kernel_type : string
               kernel type, can be one of: 'linear' (uT*v),
               'poly' ((gamma*uT*v + coef0)^degree), 'rbf' 
               (exp(-gamma*|u-v|^2)), 'sigmoid'
               (tanh(gamma*uT*v + coef0))
           degree : int (for 'poly' kernel_type)
               degree in kernel
           gamma : float (for 'poly', 'rbf', 'sigmoid' kernel_type)
               gamma in kernel (e.g. 1 / number of features)
           coef0 : float (for 'poly', 'sigmoid' kernel_type)
               coef0 in kernel
           C : float (for 'c_svc', 'epsilon_svr', 'nu_svr')
               cost of constraints violation
           nu : float (for 'nu_svc', 'one_class', 'nu_svr')
               nu parameter
           eps : float
               stopping criterion, usually 0.00001 in nu-SVC,
               0.001 in others
           p : float (for 'epsilon_svr')
               p is the epsilon in epsilon-insensitive loss function
               of epsilon-SVM regression
           cache_size : float [MB]
               size of the kernel cache, specified in megabytes
           shrinking : bool
               use the shrinking heuristics
           probability : bool
               predict probability estimates
           weight : dict 
               changes the penalty for some classes (if the weight for a
               class is not changed, it is set to 1). For example, to
               change penalty for classes 1 and 2 to 0.5 and 0.8
               respectively set weight={1:0.5, 2:0.8}
	 
	.. automethod:: learn(x, y)
        .. automethod:: pred(t)
	.. automethod:: pred_probability(t)
	.. automethod:: pred_values(t)
	.. automethod:: labels()
	.. automethod:: nclasses()
	.. automethod:: nsv()
	.. automethod:: label_nsv()
	.. automethod:: load_model(filename)
	.. automethod:: save_model(filename)


Example on :download:`spiral dataset <data/spiral.data>`:

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> import mlpy
>>> f = np.loadtxt("spiral.data")
>>> x, y = f[:, :2], f[:, 2]
>>> svm = mlpy.LibSvm(svm_type='c_svc', kernel_type='rbf', gamma=100)
>>> svm.learn(x, y)
>>> xmin, xmax = x[:,0].min()-0.1, x[:,0].max()+0.1
>>> ymin, ymax = x[:,1].min()-0.1, x[:,1].max()+0.1
>>> xx, yy = np.meshgrid(np.arange(xmin, xmax, 0.01), np.arange(ymin, ymax, 0.01))
>>> xnew = np.c_[xx.ravel(), yy.ravel()]
>>> ynew = svm.pred(xnew).reshape(xx.shape)
>>> fig = plt.figure(1)
>>> plt.set_cmap(plt.cm.Paired)
>>> plt.pcolormesh(xx, yy, ynew)
>>> plt.scatter(x[:,0], x[:,1], c=y)
>>> plt.show()

.. image:: images/svm_spiral.png

.. [LIBSVM] Chih-Chung Chang and Chih-Jen Lin. LIBSVM: a library for support vector machines. 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
.. [Cristianini] N Cristianini and J Shawe-Taylor. An introduction to support vector machines. Cambridge University Press.
.. [Vapnik95] V Vapnik. The Nature of Statistical Learning Theory. Springer-Verlag, 1995.


Kernel Adatron
--------------

.. class:: mlpy.KernelAdatron(C=1000, maxsteps=1000, eps=0.01)
   
   Kernel Adatron algorithm without-bias-term (binary classifier).
    
   The algoritm handles a version of the 1-norm soft margin
   support vector machine. If C is very high the algoritm 
   handles a version of the hard margin SVM.

   Use positive definite kernels (such as Gaussian
   and Polynomial kernels)
   
   :Parameters:
      C : float
         upper bound on the value of alpha
      maxsteps : integer (> 0)
         maximum number of steps
      eps : float (>=0)
         the algoritm stops when abs(1 - margin) < eps
	 
   .. automethod:: learn(K, y)
   .. automethod:: pred(Kt)
   .. automethod:: margin()
   .. automethod:: steps()
   .. automethod:: alpha()

Example:

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> import mlpy
>>> np.random.seed(0)
>>> mean1, cov1, n1 = [1, 4.5], [[1,1],[1,2]], 20  # 20 samples of class 1
>>> x1 = np.random.multivariate_normal(mean1, cov1, n1)
>>> y1 = np.ones(n1, dtype=int)
>>> mean2, cov2, n2 = [2.5, 2.5], [[1,1],[1,2]], 30 # 30 samples of class 2
>>> x2 = np.random.multivariate_normal(mean2, cov2, n2)
>>> y2 = 2 * np.ones(n2, dtype=int)
>>> x = np.concatenate((x1, x2), axis=0) # concatenate the samples
>>> y = np.concatenate((y1, y2))
>>> K = mlpy.kernel_gaussian(x, x, sigma=2) # kernel matrix
>>> xmin, xmax = x[:,0].min()-1, x[:,0].max()+1
>>> ymin, ymax = x[:,1].min()-1, x[:,1].max()+1
>>> xx, yy = np.meshgrid(np.arange(xmin, xmax, 0.02), np.arange(ymin, ymax, 0.02))
>>> xt = np.c_[xx.ravel(), yy.ravel()] # test points
>>> Kt = mlpy.kernel_gaussian(xt, x, sigma=2) # test kernel matrix
>>> fig = plt.figure(1)
>>> cmap = plt.set_cmap(plt.cm.Paired)
>>> for i, c in enumerate([1, 10, 100, 1000]):
...     ka = mlpy.KernelAdatron(C=c)
...     ax = plt.subplot(2, 2, i+1)
...     ka.learn(K, y)
...     ytest = ka.pred(Kt).reshape(xx.shape)
...     title = ax.set_title('C: %s; margin: %.3f; steps: %s;' % (c, ka.margin(), ka.steps()))
...     plot1 = plt.pcolormesh(xx, yy, ytest)
...     plot2 = plt.scatter(x[:,0], x[:,1], c=y)
>>> plt.show()

.. image:: images/kernel_adatron.png


.. [Friess] Friess, Cristianini, Campbell. The Kernel-Adatron Algorithm: a Fast and Simple Learning Procedure for Support Vector Machines.
.. [Kecman03] Kecman, Vogt, Huang. On the Equality of Kernel AdaTron and Sequential Minimal Optimization in Classification and Regression Tasks and Alike Algorithms for Kernel Machines. ESANN'2003 proceedings - European Symposium on Artificial Neural Networks, ISBN 2-930307-03-X, pp. 215-222.
