#!/usr/bin/env python

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

try:
    import numpy as np
    import scipy
    from scipy import sparse
except:
    scipy = None


if sys.version_info[0] < 3:
    range = xrange
    from itertools import izip as zip

__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__))
    dynamic_lib_name = 'clib.cp*'
    path_to_so = glob(path.join(dirname, dynamic_lib_name))[0]
    libsvm = CDLL(path_to_so)
except:
    try:
        if sys.platform == 'win32':
            libsvm = CDLL(path.join(dirname, r'..\..\windows\libsvm.dll'))
        else:
            libsvm = CDLL(path.join(dirname, '../../libsvm.so.3'))
    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 __init__(self, index=-1, value=0):
        self.index, self.value = index, value

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

def gen_svm_nodearray(xi, feature_max=None, isKernel=False):
    if feature_max:
        assert(isinstance(feature_max, int))

    xi_shift = 0 # ensure correct indices of xi
    if scipy and isinstance(xi, tuple) and len(xi) == 2\
            and isinstance(xi[0], np.ndarray) and isinstance(xi[1], np.ndarray): # for a sparse vector
        if not isKernel:
            index_range = xi[0] + 1 # index starts from 1
        else:
            index_range = xi[0] # index starts from 0 for precomputed kernel
        if feature_max:
            index_range = index_range[np.where(index_range <= feature_max)]
    elif scipy and isinstance(xi, np.ndarray):
        if not isKernel:
            xi_shift = 1
            index_range = xi.nonzero()[0] + 1 # index starts from 1
        else:
            index_range = np.arange(0, len(xi)) # index starts from 0 for precomputed kernel
        if feature_max:
            index_range = index_range[np.where(index_range <= feature_max)]
    elif isinstance(xi, (dict, list, tuple)):
        if isinstance(xi, dict):
            index_range = xi.keys()
        elif isinstance(xi, (list, tuple)):
            if not isKernel:
                xi_shift = 1
                index_range = range(1, len(xi) + 1) # index starts from 1
            else:
                index_range = range(0, len(xi)) # index starts from 0 for precomputed kernel

        if feature_max:
            index_range = filter(lambda j: j <= feature_max, index_range)
        if not isKernel:
            index_range = filter(lambda j:xi[j-xi_shift] != 0, index_range)

        index_range = sorted(index_range)
    else:
        raise TypeError('xi should be a dictionary, list, tuple, 1-d numpy array, or tuple of (index, data)')

    ret = (svm_node*(len(index_range)+1))()
    ret[-1].index = -1

    if scipy and isinstance(xi, tuple) and len(xi) == 2\
            and isinstance(xi[0], np.ndarray) and isinstance(xi[1], np.ndarray): # for a sparse vector
        for idx, j in enumerate(index_range):
            ret[idx].index = j
            ret[idx].value = (xi[1])[idx]
    else:
        for idx, j in enumerate(index_range):
            ret[idx].index = j
            ret[idx].value = xi[j - xi_shift]

    max_idx = 0
    if len(index_range) > 0:
        max_idx = index_range[-1]
    return ret, max_idx

try:
    from numba import jit
    jit_enabled = True
except:
    jit = lambda x: x
    jit_enabled = False

@jit
def csr_to_problem_jit(l, x_val, x_ind, x_rowptr, prob_val, prob_ind, prob_rowptr, indx_start):
    for i in range(l):
        b1,e1 = x_rowptr[i], x_rowptr[i+1]
        b2,e2 = prob_rowptr[i], prob_rowptr[i+1]-1
        for j in range(b1,e1):
            prob_ind[j-b1+b2] = x_ind[j]+indx_start
            prob_val[j-b1+b2] = x_val[j]
def csr_to_problem_nojit(l, x_val, x_ind, x_rowptr, prob_val, prob_ind, prob_rowptr, indx_start):
    for i in range(l):
        x_slice = slice(x_rowptr[i], x_rowptr[i+1])
        prob_slice = slice(prob_rowptr[i], prob_rowptr[i+1]-1)
        prob_ind[prob_slice] = x_ind[x_slice]+indx_start
        prob_val[prob_slice] = x_val[x_slice]

def csr_to_problem(x, prob, isKernel):
    if not x.has_sorted_indices:
        x.sort_indices()

    # Extra space for termination node and (possibly) bias term
    x_space = prob.x_space = np.empty((x.nnz+x.shape[0]), dtype=svm_node)
    prob.rowptr = x.indptr.copy()
    prob.rowptr[1:] += np.arange(1,x.shape[0]+1)
    prob_ind = x_space["index"]
    prob_val = x_space["value"]
    prob_ind[:] = -1
    if not isKernel:
        indx_start = 1 # index starts from 1
    else:
        indx_start = 0 # index starts from 0 for precomputed kernel
    if jit_enabled:
        csr_to_problem_jit(x.shape[0], x.data, x.indices, x.indptr, prob_val, prob_ind, prob.rowptr, indx_start)
    else:
        csr_to_problem_nojit(x.shape[0], x.data, x.indices, x.indptr, prob_val, prob_ind, prob.rowptr, indx_start)

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=False):
        if (not isinstance(y, (list, tuple))) and (not (scipy and isinstance(y, np.ndarray))):
            raise TypeError("type of y: {0} is not supported!".format(type(y)))

        if isinstance(x, (list, tuple)):
            if len(y) != len(x):
                raise ValueError("len(y) != len(x)")
        elif scipy != None and isinstance(x, (np.ndarray, sparse.spmatrix)):
            if len(y) != x.shape[0]:
                raise ValueError("len(y) != len(x)")
            if isinstance(x, np.ndarray):
                x = np.ascontiguousarray(x) # enforce row-major
            if isinstance(x, sparse.spmatrix):
                x = x.tocsr()
                pass
        else:
            raise TypeError("type of x: {0} is not supported!".format(type(x)))
        self.l = l = len(y)

        max_idx = 0
        x_space = self.x_space = []
        if scipy != None and isinstance(x, sparse.csr_matrix):
            csr_to_problem(x, self, isKernel)
            max_idx = x.shape[1]
        else:
            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)()
        if scipy != None and isinstance(y, np.ndarray):
            np.ctypeslib.as_array(self.y, (self.l,))[:] = y
        else:
            for i, yi in enumerate(y): self.y[i] = yi

        self.x = (POINTER(svm_node) * l)()
        if scipy != None and isinstance(x, sparse.csr_matrix):
            base = addressof(self.x_space.ctypes.data_as(POINTER(svm_node))[0])
            x_ptr = cast(self.x, POINTER(c_uint64))
            x_ptr = np.ctypeslib.as_array(x_ptr,(self.l,))
            x_ptr[:] = self.rowptr[:-1]*sizeof(svm_node)+base
        else:
            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 = None
        self.weight = None
        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
                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', 'prob_density_marks', '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_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(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 range(self.nr_class - 1))
                for i in range(self.l)]

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

            i = 0
            while True:
                if sparse_sv[i].index == -1:
                    break
                row[sparse_sv[i].index] = sparse_sv[i].value
                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)])

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])
