svm.py

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#!/usr/bin/env python

from ctypes import *
from ctypes.util import find_library
import sys
import subprocess as sb

# For unix the prefix 'lib' is not considered.
if find_library('svm'):
        libsvm = CDLL(find_library('svm'))
elif find_library('libsvm'):
        libsvm = CDLL(find_library('libsvm'))
else:
        if sys.platform == 'win32':
                libsvm = CDLL('../windows/libsvm.dll')
        else:
                libsvm = CDLL(sb.Popen(['rospack', 'find', 'libsvm3'], stdout=sb.PIPE).communicate()[0].rstrip() + '/libsvm.so.2')
                #libsvm = CDLL('../libsvm.so.2')

# Construct constants
SVM_TYPE = ['C_SVC', 'NU_SVC', 'ONE_CLASS', 'EPSILON_SVR', 'NU_SVR' ]
KERNEL_TYPE = ['LINEAR', 'POLY', 'RBF', 'SIGMOID', 'PRECOMPUTED']
for i, s in enumerate(SVM_TYPE): exec("%s = %d" % (s , i))
for i, s in enumerate(KERNEL_TYPE): exec("%s = %d" % (s , i))

PRINT_STRING_FUN = CFUNCTYPE(None, c_char_p)
def print_null(s): 
        return 

def genFields(names, types): 
        return list(zip(names, types))

def fillprototype(f, restype, argtypes): 
        f.restype = restype
        f.argtypes = argtypes

class svm_node(Structure):
        _names = ["index", "value"]
        _types = [c_int, c_double]
        _fields_ = genFields(_names, _types)

def gen_svm_nodearray(xi, feature_max=None, issparse=None):
        if isinstance(xi, dict):
                index_range = xi.keys()
        elif isinstance(xi, (list, tuple)):
                index_range = range(len(xi))
        else:
                raise TypeError('xi should be a dictionary, list or tuple')

        if feature_max:
                assert(isinstance(feature_max, int))
                index_range = filter(lambda j: j <= feature_max, index_range)
        if issparse: 
                index_range = filter(lambda j:xi[j] != 0, index_range)

        index_range = sorted(index_range)
        ret = (svm_node * (len(index_range)+1))()
        ret[-1].index = -1
        for idx, j in enumerate(index_range):
                ret[idx].index = j
                ret[idx].value = xi[j]
        max_idx = 0
        if index_range: 
                max_idx = index_range[-1]
        return ret, max_idx

class svm_problem(Structure):
        _names = ["l", "y", "x"]
        _types = [c_int, POINTER(c_double), POINTER(POINTER(svm_node))]
        _fields_ = genFields(_names, _types)

        def __init__(self, y, x):
                if len(y) != len(x):
                        raise ValueError("len(y) != len(x)")
                self.l = l = len(y)

                max_idx = 0
                x_space = self.x_space = []
                for i, xi in enumerate(x):
                        tmp_xi, tmp_idx = gen_svm_nodearray(xi)
                        x_space += [tmp_xi]
                        max_idx = max(max_idx, tmp_idx)
                self.n = max_idx

                self.y = (c_double * l)()
                for i, yi in enumerate(y): self.y[i] = yi

                self.x = (POINTER(svm_node) * l)() 
                for i, xi in enumerate(self.x_space): self.x[i] = xi

class svm_parameter(Structure):
        _names = ["svm_type", "kernel_type", "degree", "gamma", "coef0",
                        "cache_size", "eps", "C", "nr_weight", "weight_label", "weight", 
                        "nu", "p", "shrinking", "probability"]
        _types = [c_int, c_int, c_int, c_double, c_double, 
                        c_double, c_double, c_double, c_int, POINTER(c_int), POINTER(c_double),
                        c_double, c_double, c_int, c_int]
        _fields_ = genFields(_names, _types)

        def __init__(self, options = None):
                if options == None:
                        options = ''
                self.parse_options(options)

        def show(self):
                attrs = svm_parameter._names + self.__dict__.keys()
                values = map(lambda attr: getattr(self, attr), attrs) 
                for attr, val in zip(attrs, values):
                        print(' %s: %s' % (attr, val))

        def set_to_default_values(self):
                self.svm_type = C_SVC;
                self.kernel_type = RBF
                self.degree = 3
                self.gamma = 0
                self.coef0 = 0
                self.nu = 0.5
                self.cache_size = 100
                self.C = 1
                self.eps = 0.001
                self.p = 0.1
                self.shrinking = 1
                self.probability = 0
                self.nr_weight = 0
                self.weight_label = (c_int*0)()
                self.weight = (c_double*0)()
                self.cross_validation = False
                self.nr_fold = 0
                self.print_func = None

        def parse_options(self, options):
                argv = options.split()
                self.set_to_default_values()
                self.print_func = cast(None, PRINT_STRING_FUN)
                weight_label = []
                weight = []

                i = 0
                while i < len(argv):
                        if argv[i] == "-s":
                                i = i + 1
                                self.svm_type = int(argv[i])
                        elif argv[i] == "-t":
                                i = i + 1
                                self.kernel_type = int(argv[i])
                        elif argv[i] == "-d":
                                i = i + 1
                                self.degree = int(argv[i])
                        elif argv[i] == "-g":
                                i = i + 1
                                self.gamma = float(argv[i])
                        elif argv[i] == "-r":
                                i = i + 1
                                self.coef0 = float(argv[i])
                        elif argv[i] == "-n":
                                i = i + 1
                                self.nu = float(argv[i])
                        elif argv[i] == "-m":
                                i = i + 1
                                self.cache_size = float(argv[i])
                        elif argv[i] == "-c":
                                i = i + 1
                                self.C = float(argv[i])
                        elif argv[i] == "-e":
                                i = i + 1
                                self.eps = float(argv[i])
                        elif argv[i] == "-p":
                                i = i + 1
                                self.p = float(argv[i])
                        elif argv[i] == "-h":
                                i = i + 1
                                self.shrinking = int(argv[i])
                        elif argv[i] == "-b":
                                i = i + 1
                                self.probability = int(argv[i])
                        elif argv[i] == "-q":
                                self.print_func = PRINT_STRING_FUN(print_null)
                        elif argv[i] == "-v":
                                i = i + 1
                                self.cross_validation = 1
                                self.nr_fold = int(argv[i])
                                if self.nr_fold < 2:
                                        raise ValueError("n-fold cross validation: n must >= 2")
                        elif argv[i].startswith("-w"):
                                i = i + 1
                                self.nr_weight += 1
                                nr_weight = self.nr_weight
                                weight_label += [int(argv[i-1][2:])]
                                weight += [float(argv[i])]
                        else:
                                raise ValueError("Wrong options")
                        i += 1

                libsvm.svm_set_print_string_function(self.print_func)
                self.weight_label = (c_int*self.nr_weight)()
                self.weight = (c_double*self.nr_weight)()
                for i in range(self.nr_weight): 
                        self.weight[i] = weight[i]
                        self.weight_label[i] = weight_label[i]

class svm_model(Structure):
        #_names = ['param', 'nr_class', 'l', 'SV', 'sv_coef', 
    #          'rho', 'probA', 'probB', 'label', 'nSV', 'free_sv', 'SVidx', 'nx']
        #_types = [POINTER(svm_parameter), c_int, c_int, POINTER(POINTER(svm_node)), POINTER(POINTER(c_double)), 
    #          POINTER(c_double), POINTER(c_double), POINTER(c_double), POINTER(c_int), POINTER(c_int), c_int, 
    #          POINTER(c_byte), c_int]
        #_fields_ = genFields(_names, _types)

        def __init__(self):
                self.__createfrom__ = 'python'

        def __del__(self):
                # free memory created by C to avoid memory leak
                if hasattr(self, '__createfrom__') and self.__createfrom__ == 'C':
                        libsvm.svm_free_and_destroy_model(pointer(self))

        def get_svm_type(self):
                return libsvm.svm_get_svm_type(self)

        def get_nr_class(self):
                return libsvm.svm_get_nr_class(self)

        def get_svr_probability(self):
                return libsvm.svm_get_svr_probability(self)

        def get_labels(self):
                nr_class = self.get_nr_class()
                labels = (c_int * nr_class)()
                libsvm.svm_get_labels(self, labels)
                return labels[:nr_class]

        def is_probability_model(self):
                return (libsvm.svm_check_probability_model(self) == 1)

        #def predict(self, sample):
        #       return libsvm.svm_predict(self, gen_svm_nodearray(sample)[0])

        #def predict_values(self, sample):
        #       nc = self.get_nr_class()
        #       nvalues = (nc * (nc-1))/2
        #       dists = (c_double*nvalues)()
        #       p = libsvm.svm_predict_values(self, gen_svm_nodearray(sample)[0], dists)
        #       ddict = {}
        #       ii = 0
        #       for i in range(nc):
        #               for j in range(i+1, nc):
        #                       ddict[(i,j)] = dists[ii]
        #                       ii = ii+1

        #       nr_classifier = nc*(nc-1)//2
        #       dec_values = (c_double * nr_classifier)()
        #       xi = sample
        #       xi, idx = gen_svm_nodearray(xi)
        #       label = libsvm.svm_predict_values(self, xi, dec_values)
        #       values = dec_values[:nr_classifier]
        #       print 'got label', label

        #       return p, ddict

        def get_support_vectors(self, samples_len):
                svs = (c_int * samples_len)()
                libsvm.svm_get_support_vectors(self, samples_len, svs)
                return svs[:samples_len]




def toPyModel(model_ptr):
        """
        toPyModel(model_ptr) -> svm_model

        Convert a ctypes POINTER(svm_model) to a Python svm_model
        """
        if bool(model_ptr) == False:
                raise ValueError("Null pointer")
        m = model_ptr.contents
        m.__createfrom__ = 'C'
        return m

fillprototype(libsvm.svm_train, POINTER(svm_model), [POINTER(svm_problem), POINTER(svm_parameter)])
fillprototype(libsvm.svm_cross_validation, None, [POINTER(svm_problem), POINTER(svm_parameter), c_int, POINTER(c_double)])

fillprototype(libsvm.svm_save_model, c_int, [c_char_p, POINTER(svm_model)])
fillprototype(libsvm.svm_load_model, POINTER(svm_model), [c_char_p])

fillprototype(libsvm.svm_get_svm_type, c_int, [POINTER(svm_model)])
fillprototype(libsvm.svm_get_nr_class, c_int, [POINTER(svm_model)])
fillprototype(libsvm.svm_get_labels, None, [POINTER(svm_model), POINTER(c_int)])
fillprototype(libsvm.svm_get_support_vectors, None, [POINTER(svm_model), c_int, POINTER(c_int)])
fillprototype(libsvm.svm_get_svr_probability, c_double, [POINTER(svm_model)])

fillprototype(libsvm.svm_predict_values, c_double, [POINTER(svm_model), POINTER(svm_node), POINTER(c_double)])
fillprototype(libsvm.svm_predict, c_double, [POINTER(svm_model), POINTER(svm_node)])
fillprototype(libsvm.svm_predict_probability, c_double, [POINTER(svm_model), POINTER(svm_node), POINTER(c_double)])

fillprototype(libsvm.svm_free_model_content, None, [POINTER(svm_model)])
fillprototype(libsvm.svm_free_and_destroy_model, None, [POINTER(POINTER(svm_model))])
fillprototype(libsvm.svm_destroy_param, None, [POINTER(svm_parameter)])

fillprototype(libsvm.svm_check_parameter, c_char_p, [POINTER(svm_problem), POINTER(svm_parameter)])
fillprototype(libsvm.svm_check_probability_model, c_int, [POINTER(svm_model)])
fillprototype(libsvm.svm_set_print_string_function, None, [PRINT_STRING_FUN])