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

__all__ = ['liblinear', 'feature_node', 'gen_feature_nodearray', 'problem',
           'parameter', 'model', 'toPyModel', 'L2R_LR', 'L2R_L2LOSS_SVC_DUAL',
           'L2R_L2LOSS_SVC', 'L2R_L1LOSS_SVC_DUAL', 'MCSVM_CS',
           'L1R_L2LOSS_SVC', 'L1R_LR', 'L2R_LR_DUAL', 'L2R_L2LOSS_SVR',
           'L2R_L2LOSS_SVR_DUAL', 'L2R_L1LOSS_SVR_DUAL', 'print_null']

try:
    dirname = path.dirname(path.abspath(__file__))
    if sys.platform == 'win32':
        liblinear = CDLL(path.join(dirname, r'..\windows\liblinear.dll'))
    else:
        liblinear = CDLL(path.join(dirname, '../liblinear.so.2'))
except:
    # For unix the prefix 'lib' is not considered.
    if find_library('linear'):
        liblinear = CDLL(find_library('linear'))
    elif find_library('liblinear'):
        liblinear = CDLL(find_library('liblinear'))
    else:
        raise Exception('LIBLINEAR library not found.')

L2R_LR = 0
L2R_L2LOSS_SVC_DUAL = 1
L2R_L2LOSS_SVC = 2
L2R_L1LOSS_SVC_DUAL = 3
MCSVM_CS = 4
L1R_L2LOSS_SVC = 5
L1R_LR = 6
L2R_LR_DUAL = 7
L2R_L2LOSS_SVR = 11
L2R_L2LOSS_SVR_DUAL = 12
L2R_L1LOSS_SVR_DUAL = 13

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 feature_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_feature_nodearray(xi, feature_max=None, issparse=True):
    if isinstance(xi, dict):
        index_range = xi.keys()
    elif isinstance(xi, (list, tuple)):
        xi = [0] + xi  # idx should start from 1
        index_range = range(1, 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 = (feature_node * (len(index_range) + 2))()
    ret[-1].index = -1  # for bias term
    ret[-2].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 problem(Structure):
    _names = ["l", "n", "y", "x", "bias"]
    _types = [c_int, c_int, POINTER(c_double), POINTER(POINTER(feature_node)), c_double]
    _fields_ = genFields(_names, _types)

    def __init__(self, y, x, bias=-1):
        if len(y) != len(x):
            raise ValueError("len(y) != len(x)")
        self.l = l = len(y)
        self.bias = -1

        max_idx = 0
        x_space = self.x_space = []
        for i, xi in enumerate(x):
            tmp_xi, tmp_idx = gen_feature_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] = y[i]

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

        self.set_bias(bias)

    def set_bias(self, bias):
        if self.bias == bias:
            return
        if bias >= 0 and self.bias < 0:
            self.n += 1
            node = feature_node(self.n, bias)
        if bias < 0 and self.bias >= 0:
            self.n -= 1
            node = feature_node(-1, bias)

        for xi in self.x_space:
            xi[-2] = node
        self.bias = bias


class parameter(Structure):
    _names = ["solver_type", "eps", "C", "nr_weight", "weight_label", "weight", "p"]
    _types = [c_int, c_double, c_double, c_int, POINTER(c_int), POINTER(c_double), c_double]
    _fields_ = genFields(_names, _types)

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

    def __str__(self):
        s = ''
        attrs = 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.solver_type = L2R_L2LOSS_SVC_DUAL
        self.eps = float('inf')
        self.C = 1
        self.p = 0.1
        self.nr_weight = 0
        self.weight_label = (c_int * 0)()
        self.weight = (c_double * 0)()
        self.bias = -1
        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.solver_type = int(argv[i])
            elif argv[i] == "-c":
                i = i + 1
                self.C = float(argv[i])
            elif argv[i] == "-p":
                i = i + 1
                self.p = float(argv[i])
            elif argv[i] == "-e":
                i = i + 1
                self.eps = float(argv[i])
            elif argv[i] == "-B":
                i = i + 1
                self.bias = float(argv[i])
            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])]
            elif argv[i] == "-q":
                self.print_func = PRINT_STRING_FUN(print_null)
            else:
                raise ValueError("Wrong options")
            i += 1

        liblinear.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]

        if self.eps == float('inf'):
            if self.solver_type in [L2R_LR, L2R_L2LOSS_SVC]:
                self.eps = 0.01
            elif self.solver_type in [L2R_L2LOSS_SVR]:
                self.eps = 0.001
            elif self.solver_type in [L2R_L2LOSS_SVC_DUAL, L2R_L1LOSS_SVC_DUAL, MCSVM_CS, L2R_LR_DUAL]:
                self.eps = 0.1
            elif self.solver_type in [L1R_L2LOSS_SVC, L1R_LR]:
                self.eps = 0.01
            elif self.solver_type in [L2R_L2LOSS_SVR_DUAL, L2R_L1LOSS_SVR_DUAL]:
                self.eps = 0.1


class model(Structure):
    _names = ["param", "nr_class", "nr_feature", "w", "label", "bias"]
    _types = [parameter, c_int, c_int, POINTER(c_double), POINTER(c_int), c_double]
    _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':
            liblinear.free_and_destroy_model(pointer(self))

    def get_nr_feature(self):
        return liblinear.get_nr_feature(self)

    def get_nr_class(self):
        return liblinear.get_nr_class(self)

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

    def get_decfun_coef(self, feat_idx, label_idx=0):
        return liblinear.get_decfun_coef(self, feat_idx, label_idx)

    def get_decfun_bias(self, label_idx=0):
        return liblinear.get_decfun_bias(self, label_idx)

    def get_decfun(self, label_idx=0):
        w = [liblinear.get_decfun_coef(self, feat_idx, label_idx) for feat_idx in range(1, self.nr_feature + 1)]
        b = liblinear.get_decfun_bias(self, label_idx)
        return (w, b)

    def is_probability_model(self):
        return (liblinear.check_probability_model(self) == 1)

    def is_regression_model(self):
        return (liblinear.check_regression_model(self) == 1)


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

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


fillprototype(liblinear.train, POINTER(model), [POINTER(problem), POINTER(parameter)])
fillprototype(liblinear.cross_validation, None, [POINTER(problem), POINTER(parameter), c_int, POINTER(c_double)])

fillprototype(liblinear.predict_values, c_double, [POINTER(model), POINTER(feature_node), POINTER(c_double)])
fillprototype(liblinear.predict, c_double, [POINTER(model), POINTER(feature_node)])
fillprototype(liblinear.predict_probability, c_double, [POINTER(model), POINTER(feature_node), POINTER(c_double)])

fillprototype(liblinear.save_model, c_int, [c_char_p, POINTER(model)])
fillprototype(liblinear.load_model, POINTER(model), [c_char_p])

fillprototype(liblinear.get_nr_feature, c_int, [POINTER(model)])
fillprototype(liblinear.get_nr_class, c_int, [POINTER(model)])
fillprototype(liblinear.get_labels, None, [POINTER(model), POINTER(c_int)])
fillprototype(liblinear.get_decfun_coef, c_double, [POINTER(model), c_int, c_int])
fillprototype(liblinear.get_decfun_bias, c_double, [POINTER(model), c_int])

fillprototype(liblinear.free_model_content, None, [POINTER(model)])
fillprototype(liblinear.free_and_destroy_model, None, [POINTER(POINTER(model))])
fillprototype(liblinear.destroy_param, None, [POINTER(parameter)])
fillprototype(liblinear.check_parameter, c_char_p, [POINTER(problem), POINTER(parameter)])
fillprototype(liblinear.check_probability_model, c_int, [POINTER(model)])
fillprototype(liblinear.check_regression_model, c_int, [POINTER(model)])
fillprototype(liblinear.set_print_string_function, None, [CFUNCTYPE(None, c_char_p)])
