#!/usr/bin/env python

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

try:
	import scipy
	from scipy import sparse
except:
	scipy = None
	sparse = None

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

__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'liblinear.dll'))
	else:
		liblinear = CDLL(path.join(dirname, 'liblinear.so.3'))
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):
	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], scipy.ndarray) and isinstance(xi[1], scipy.ndarray): # for a sparse vector
		index_range = xi[0] + 1 # index starts from 1
		if feature_max:
			index_range = index_range[scipy.where(index_range <= feature_max)]
	elif scipy and isinstance(xi, scipy.ndarray):
		xi_shift = 1
		index_range = xi.nonzero()[0] + 1 # index starts from 1
		if feature_max:
			index_range = index_range[scipy.where(index_range <= feature_max)]
	elif isinstance(xi, (dict, list, tuple)):
		if isinstance(xi, dict):
			index_range = xi.keys()
		elif isinstance(xi, (list, tuple)):
			xi_shift = 1
			index_range = range(1, len(xi) + 1)
		index_range = filter(lambda j: xi[j-xi_shift] != 0, index_range)

		if feature_max:
			index_range = filter(lambda j: j <= feature_max, 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 = (feature_node*(len(index_range)+2))()
	ret[-1].index = -1 # for bias term
	ret[-2].index = -1

	if scipy and isinstance(xi, tuple) and len(xi) == 2\
			and isinstance(xi[0], scipy.ndarray) and isinstance(xi[1], scipy.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):
	for i in range(l):
		b1,e1 = x_rowptr[i], x_rowptr[i+1]
		b2,e2 = prob_rowptr[i], prob_rowptr[i+1]-2
		for j in range(b1,e1):
			prob_ind[j-b1+b2] = x_ind[j]+1
			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):
	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]-2)
		prob_ind[prob_slice] = x_ind[x_slice]+1
		prob_val[prob_slice] = x_val[x_slice]

def csr_to_problem(x, prob):
	# Extra space for termination node and (possibly) bias term
	x_space = prob.x_space = scipy.empty((x.nnz+x.shape[0]*2), dtype=feature_node)
	prob.rowptr = x.indptr.copy()
	prob.rowptr[1:] += 2*scipy.arange(1,x.shape[0]+1)
	prob_ind = x_space["index"]
	prob_val = x_space["value"]
	prob_ind[:] = -1
	if jit_enabled:
		csr_to_problem_jit(x.shape[0], x.data, x.indices, x.indptr, prob_val, prob_ind, prob.rowptr)
	else:
		csr_to_problem_nojit(x.shape[0], x.data, x.indices, x.indptr, prob_val, prob_ind, prob.rowptr)

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 (not isinstance(y, (list, tuple))) and (not (scipy and isinstance(y, scipy.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, (scipy.ndarray, sparse.spmatrix)):
			if len(y) != x.shape[0]:
				raise ValueError("len(y) != len(x)")
			if isinstance(x, scipy.ndarray):
				x = scipy.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)
		self.bias = -1

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

		self.x = (POINTER(feature_node) * l)()
		if scipy != None and isinstance(x, sparse.csr_matrix):
			base = addressof(self.x_space.ctypes.data_as(POINTER(feature_node))[0])
			x_ptr = cast(self.x, POINTER(c_uint64))
			x_ptr = scipy.ctypeslib.as_array(x_ptr,(self.l,))
			x_ptr[:] = self.rowptr[:-1]*sizeof(feature_node)+base
		else:
			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)

		if isinstance(self.x_space, list):
			for xi in self.x_space:
				xi[-2] = node
		else:
			self.x_space["index"][self.rowptr[1:]-2] = node.index
			self.x_space["value"][self.rowptr[1:]-2] = node.value

		self.bias = bias


class parameter(Structure):
	_names = ["solver_type", "eps", "C", "nr_thread", "nr_weight", "weight_label", "weight", "p", "init_sol"]
	_types = [c_int, c_double, c_double, c_int, c_int, POINTER(c_int), POINTER(c_double), c_double, POINTER(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_thread = 1
		self.nr_weight = 0
		self.weight_label = None
		self.weight = None
		self.init_sol = None
		self.bias = -1
		self.flag_cross_validation = False
		self.flag_C_specified = False
		self.flag_p_specified = False
		self.flag_solver_specified = False
		self.flag_find_parameters = False
		self.flag_omp = 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])
				self.flag_solver_specified = True
			elif argv[i] == "-c":
				i = i + 1
				self.C = float(argv[i])
				self.flag_C_specified = True
			elif argv[i] == "-p":
				i = i + 1
				self.p = float(argv[i])
				self.flag_p_specified = True
			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.flag_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] == "-n":
				i = i + 1
				self.flag_omp = True
				self.nr_thread = int(argv[i])
			elif argv[i].startswith("-w"):
				i = i + 1
				self.nr_weight += 1
				weight_label += [int(argv[i-1][2:])]
				weight += [float(argv[i])]
			elif argv[i] == "-q":
				self.print_func = PRINT_STRING_FUN(print_null)
			elif argv[i] == "-C":
				self.flag_find_parameters = True

			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]

		# default solver for parameter selection is L2R_L2LOSS_SVC
		if self.flag_find_parameters:
			if not self.flag_cross_validation:
				self.nr_fold = 5
			if not self.flag_solver_specified:
				self.solver_type = L2R_L2LOSS_SVC
				self.flag_solver_specified = True
			elif self.solver_type not in [L2R_LR, L2R_L2LOSS_SVC, L2R_L2LOSS_SVR]:
				raise ValueError("Warm-start parameter search only available for -s 0, -s 2 and -s 11")

		if self.flag_omp:
			if not self.flag_solver_specified:
				self.solver_type = L2R_L2LOSS_SVC
				self.flag_solver_specified = True
			elif self.solver_type not in [L2R_LR, L2R_L2LOSS_SVC, L2R_L2LOSS_SVR, L2R_L2LOSS_SVC_DUAL, L2R_L1LOSS_SVC_DUAL, L1R_LR, L1R_L2LOSS_SVC]:
				raise ValueError("Parallel LIBLINEAR is only available for -s 0, 1, 2, 3, 5, 6, 11 now")

		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 = 0.0001
			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.find_parameters, None, [POINTER(problem), POINTER(parameter), c_int, c_double, c_double, POINTER(c_double), POINTER(c_double), POINTER(c_double)])
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)])
