#!/usr/bin/env python

from ctypes import *
from ctypes.util import find_library
from os import path
import sys

__all__ = ['libsvm', 'svm_problem', 'svm_parameter',
           'toPyModel', 'gen_svm_nodearray', 'print_null', 'svm_node', 'C_SVC',
           'EPSILON_SVR', 'LINEAR', 'NU_SVC', 'NU_SVR', 'ONE_CLASS',
           'POLY', 'PRECOMPUTED', 'PRINT_STRING_FUN', 'RBF',
           'SIGMOID', 'c_double', 'svm_model']

try:

    dirname = path.dirname(path.abspath(__file__))
    if sys.platform == 'win32':
        libsvm = CDLL(path.join(dirname, r'..\windows\libsvm.dll'))
    else:
        libsvm = CDLL(path.join(dirname, '../libsvm.so.2'))

except:

    # 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:
        raise Exception('LIBSVM library not found.')

C_SVC = 0
NU_SVC = 1
ONE_CLASS = 2
EPSILON_SVR = 3
NU_SVR = 4

LINEAR = 0
POLY = 1
RBF = 2
SIGMOID = 3
PRECOMPUTED = 4

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 __str__(self):
        return '%d:%g' % (self.index, self.value)


def gen_svm_nodearray(xi, feature_max=None, isKernel=None):
    if isinstance(xi, dict):
        index_range = xi.keys()
    elif isinstance(xi, (list, tuple)):
        if not isKernel:
            xi = [0] + xi  # idx should start from 1
        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 not isKernel:
        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, isKernel=None):
        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, isKernel=isKernel)

            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 __str__(self):
        s = ''
        attrs = svm_parameter._names + list(self.__dict__.keys())
        values = map(lambda attr: getattr(self, attr), attrs)
        for attr, val in zip(attrs, values):
            s += (' %s: %s\n' % (attr, val))
        s = s.strip()

        return s

    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 = cast(None, PRINT_STRING_FUN)

    def parse_options(self, options):
        if isinstance(options, list):
            argv = options
        elif isinstance(options, str):
            argv = options.split()
        else:
            raise TypeError("arg 1 should be a list or a str.")
        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', 'sv_indices', 'label', 'nSV', 'free_sv']
    _types = [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), POINTER(c_int), 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 get_sv_indices(self):
        total_sv = self.get_nr_sv()
        sv_indices = (c_int * total_sv)()
        libsvm.svm_get_sv_indices(self, sv_indices)
        return sv_indices[:total_sv]

    def get_nr_sv(self):
        return libsvm.svm_get_nr_sv(self)

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

    def get_sv_coef(self):
        return [tuple(self.sv_coef[j][i] for j in xrange(self.nr_class - 1))
                for i in xrange(self.l)]

    def get_SV(self):
        result = []
        for sparse_sv in self.SV[:self.l]:
            row = dict()

            i = 0
            while True:

                row[sparse_sv[i].index] = sparse_sv[i].value
                if sparse_sv[i].index == -1:

                    break

                i += 1

            result.append(row)
        return result


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_sv_indices, None, [POINTER(svm_model), POINTER(c_int)])
fillprototype(libsvm.svm_get_nr_sv, c_int, [POINTER(svm_model)])
fillprototype(libsvm.svm_get_svr_probability, c_double, [POINTER(svm_model)])