import numpy as np
import functools
import scipy.optimize as op

from math_helper import MathHelper
from model import Model


class NeuralNetwork:
    def __init__(self, lambda_val, input_layer_size, output_layer_size, hidden_layer_sizes, initial_thetas=None):
        if lambda_val is None or (type(lambda_val) != float and type(lambda_val) != int):
            raise TypeError("Lambda must be a numeric type.")

        if lambda_val < 0.:
            raise ValueError("Lambda must be a non-negative number.")

        if type(input_layer_size) != int or type(output_layer_size) != int:
            raise TypeError("Input and output layer sizes must be in int.")

        if input_layer_size <= 0 or output_layer_size <= 0:
            raise ValueError("Input and output layer sizes must be greater than zero.")

        if len(hidden_layer_sizes) < 1:
            raise ValueError("There must be at least one hidden layer.")

        if not all(isinstance(x, int) and x > 0 for x in hidden_layer_sizes):
            raise ValueError("All hidden layer sizes must be positive integer.")

        self.lambda_val = lambda_val
        self.input_layer_size = input_layer_size
        self.output_layer_size = output_layer_size
        self.hidden_layer_sizes = list(hidden_layer_sizes)
        self.hidden_layer_count = len(self.hidden_layer_sizes)
        self._initial_thetas = self._random_initialize_theta() if initial_thetas is None else [np.asarray(t) for t in
                                                                                               initial_thetas]
        self.helper = MathHelper()

    @classmethod
    def init(cls, lambda_val, input_layer_size, output_layer_size, hidden_layer_sizes):
        return cls(lambda_val=lambda_val, input_layer_size=input_layer_size, output_layer_size=output_layer_size,
                   hidden_layer_sizes=hidden_layer_sizes, initial_thetas=None)

    @classmethod
    def init_with_theta(cls, lambda_val, thetas):
        if len(thetas) < 2:
            raise ValueError("There must be at least one hidden layer and hence at least two weight matrices.")

        def _shape(item):
            if hasattr(item, "shape"):
                return item.shape
            r = len(item)
            c = 0
            if r > 0:
                c = len(item[0])
            return r, c

        ils = _shape(thetas[0])[1] - 1
        hls = [_shape(t)[0] for t in thetas]
        ols = hls.pop()

        return cls(lambda_val=lambda_val, input_layer_size=ils, output_layer_size=ols, hidden_layer_sizes=hls,
                   initial_thetas=thetas)

    def _random_initialize_theta(self):
        delta = 0.12

        def random_matrix(row, col):
            return np.random.rand(row, col) * 2 * delta - delta

        first_size_array = [self.input_layer_size]
        [first_size_array.append(x) for x in self.hidden_layer_sizes]
        paired_copy = first_size_array[1:]
        paired_copy.append(self.output_layer_size)
        sizes = zip(paired_copy, first_size_array)

        return [random_matrix(r, c + 1) for (r, c) in sizes]

    @staticmethod
    def _unroll_matrices(matrices):
        def mapper(x):
            return np.ravel(x).tolist()

        def reducer(x, y):
            return x + y

        combined_list = functools.reduce(reducer, map(mapper, matrices))
        return np.array(combined_list)

    def _roll_into_matrices(self, unrolled_vector):
        taken, prev_layer_size, current_layer_size = 0, self.input_layer_size, self.hidden_layer_sizes[0]
        matrices = []
        for i in range(self.hidden_layer_count):
            current_layer_size = self.hidden_layer_sizes[i]
            matrices.append(np.reshape(unrolled_vector[taken: taken + current_layer_size * (prev_layer_size + 1)],
                                       (current_layer_size, prev_layer_size + 1)))
            taken += current_layer_size * (prev_layer_size + 1)
            prev_layer_size = current_layer_size

        matrices.append(np.reshape(unrolled_vector[taken:], (self.output_layer_size, prev_layer_size + 1)))
        return matrices

    def cost_regularization(self, current_thetas, train_data_size):
        if self.lambda_val == 0:
            return 0

        def mapper(x):
            matrix = np.zeros(x.shape)
            matrix[:, 1:] = x[:, 1:]
            return np.multiply(matrix, matrix).sum()

        def reducer(x, y):
            return x + y

        return self.lambda_val * functools.reduce(reducer, map(mapper, current_thetas), 0) / (2. * train_data_size)

    def _theta_regularization(self, theta, train_data_size):
        result = np.zeros(theta.shape)

        if self.lambda_val == 0:
            return result

        result[:, 1:] = (self.lambda_val / train_data_size) * theta[:, 1:]
        return result

    def _calculate_cost_gradient(self, unrolled_theta_vector, X_in, Y_in):
        thetas = self._roll_into_matrices(unrolled_theta_vector)
        theta_count = len(thetas)
        X = np.asmatrix(X_in)
        Y = np.asmatrix(Y_in)
        m, n = X.shape
        eps = 0.#np.finfo(float).eps

        def single_sample_mapper(pair):
            x, y = pair
            if np.any((y != 0)  & (y != 1)):
                raise ValueError(
                    "Output value cannot be anything other than 0 and 1. If you want more than two level of output, try converting the out values into a vector of 0 and 1 only.")

            activations = []
            prev_activation = np.ones((1, n+1))
            prev_activation[:,1:] = np.asmatrix(x)[:,:]
            for i in range(theta_count):
                theta = np.asmatrix(thetas[i])
                activations.append(prev_activation)
                zt = np.dot(prev_activation, np.transpose(theta))
                at = np.ones((zt.shape[0], zt.shape[1] + 1))
                at[:, 1:] = self.helper.sigmoid(zt)
                prev_activation = at

            ht = prev_activation[:, 1:]

            # for y==1 and y==0 items separately
            j_partial = sum(-np.log(ht[y == 1] + eps))
            j_partial += sum(-np.log(1 + eps - ht[y == 0]))

            deltas = []

            prev_delta = np.transpose(ht - y)
            deltas.append(prev_delta)

            for i in range(1, theta_count):
                a = np.transpose(activations[-i])

                delta = np.multiply(np.dot(np.transpose(thetas[-i]), prev_delta), np.multiply(a, 1 - a))[1:,:]
                deltas.append(delta)
                prev_delta = delta

            DELs = [np.dot(d, a) for (d,a) in zip(deltas[::-1], activations)]

            return {'cost': j_partial, 'deltas' : DELs}


        def sample_pair_reducer(x, y):
            return {'cost': x['cost'] + y['cost'], 'deltas': [dx + dy for (dx,dy) in zip(x['deltas'], y['deltas'])]}

        result = functools.reduce(
            sample_pair_reducer,
            map(single_sample_mapper, zip(X, Y)),
            {'cost': 0, 'deltas': [np.zeros(t.shape) for t in thetas]});

        J = result["cost"]/m + self.cost_regularization(thetas, m)

        gradients = [(d/m)+self._theta_regularization(t, m) for (d,t) in zip(result["deltas"], thetas)]

        return (J, self._unroll_matrices(gradients))

    def train(self, X, Y, maxiter = 200, tolerance = 1e-5):
        if X.shape[0] != Y.shape[0]:
            raise(ValueError("X and Y must have same number of rows."))
        t = self._unroll_matrices(self._initial_thetas)
        res = op.minimize(
            fun = self._calculate_cost_gradient,
            x0 = t,
            jac = True,
            tol = tolerance,
            method = 'CG',
            options = {'maxiter': maxiter},
            args = (X, Y)
        );

        return Model(self._roll_into_matrices(res.x))

import numpy as np
import functools
import scipy.optimize as op

from math_helper import MathHelper
from model import Model


class NeuralNetwork:
    def __init__(self, lambda_val, input_layer_size, output_layer_size, hidden_layer_sizes, initial_thetas=None):
        if lambda_val is None or (type(lambda_val) != float and type(lambda_val) != int):
            raise TypeError("Lambda must be a numeric type.")

        if lambda_val < 0.:
            raise ValueError("Lambda must be a non-negative number.")

        if type(input_layer_size) != int or type(output_layer_size) != int:
            raise TypeError("Input and output layer sizes must be in int.")

        if input_layer_size <= 0 or output_layer_size <= 0:
            raise ValueError("Input and output layer sizes must be greater than zero.")

        if len(hidden_layer_sizes) < 1:
            raise ValueError("There must be at least one hidden layer.")

        if not all(isinstance(x, int) and x > 0 for x in hidden_layer_sizes):
            raise ValueError("All hidden layer sizes must be positive integer.")

        self.lambda_val = lambda_val
        self.input_layer_size = input_layer_size
        self.output_layer_size = output_layer_size
        self.hidden_layer_sizes = list(hidden_layer_sizes)
        self.hidden_layer_count = len(self.hidden_layer_sizes)
        self._initial_thetas = self._random_initialize_theta() if initial_thetas is None else [np.asarray(t) for t in
                                                                                               initial_thetas]
        self.helper = MathHelper()

    @classmethod
    def init(cls, lambda_val, input_layer_size, output_layer_size, hidden_layer_sizes):
        return cls(lambda_val=lambda_val, input_layer_size=input_layer_size, output_layer_size=output_layer_size,
                   hidden_layer_sizes=hidden_layer_sizes, initial_thetas=None)

    @classmethod
    def init_with_theta(cls, lambda_val, thetas):
        if len(thetas) < 2:
            raise ValueError("There must be at least one hidden layer and hence at least two weight matrices.")

        def _shape(item):
            if hasattr(item, "shape"):
                return item.shape
            r = len(item)
            c = 0
            if r > 0:
                c = len(item[0])
            return r, c

        ils = _shape(thetas[0])[1] - 1
        hls = [_shape(t)[0] for t in thetas]
        ols = hls.pop()

        return cls(lambda_val=lambda_val, input_layer_size=ils, output_layer_size=ols, hidden_layer_sizes=hls,
                   initial_thetas=thetas)

    def _random_initialize_theta(self):
        delta = 0.12

        def random_matrix(row, col):
            return np.random.rand(row, col) * 2 * delta - delta

        first_size_array = [self.input_layer_size]
        [first_size_array.append(x) for x in self.hidden_layer_sizes]
        paired_copy = first_size_array[1:]
        paired_copy.append(self.output_layer_size)
        sizes = zip(paired_copy, first_size_array)

        return [random_matrix(r, c + 1) for (r, c) in sizes]

    @staticmethod
    def _unroll_matrices(matrices):
        def mapper(x):
            return np.ravel(x).tolist()

        def reducer(x, y):
            return x + y

        combined_list = functools.reduce(reducer, map(mapper, matrices))
        return np.array(combined_list)

    def _roll_into_matrices(self, unrolled_vector):
        taken, prev_layer_size, current_layer_size = 0, self.input_layer_size, self.hidden_layer_sizes[0]
        matrices = []
        for i in range(self.hidden_layer_count):
            current_layer_size = self.hidden_layer_sizes[i]
            matrices.append(np.reshape(unrolled_vector[taken: taken + current_layer_size * (prev_layer_size + 1)],
                                       (current_layer_size, prev_layer_size + 1)))
            taken += current_layer_size * (prev_layer_size + 1)
            prev_layer_size = current_layer_size

        matrices.append(np.reshape(unrolled_vector[taken:], (self.output_layer_size, prev_layer_size + 1)))
        return matrices

    def cost_regularization(self, current_thetas, train_data_size):
        if self.lambda_val == 0:
            return 0

        def mapper(x):
            matrix = np.zeros(x.shape)
            matrix[:, 1:] = x[:, 1:]
            return np.multiply(matrix, matrix).sum()

        def reducer(x, y):
            return x + y

        return self.lambda_val * functools.reduce(reducer, map(mapper, current_thetas), 0) / (2. * train_data_size)

    def _theta_regularization(self, theta, train_data_size):
        result = np.zeros(theta.shape)

        if self.lambda_val == 0:
            return result

        result[:, 1:] = (self.lambda_val / train_data_size) * theta[:, 1:]
        return result

    def _calculate_cost_gradient(self, unrolled_theta_vector, X_in, Y_in):
        thetas = self._roll_into_matrices(unrolled_theta_vector)
        theta_count = len(thetas)
        X = np.asmatrix(X_in)
        Y = np.asmatrix(Y_in)
        m, n = X.shape
        eps = 0.#np.finfo(float).eps

        def single_sample_mapper(pair):
            x, y = pair
            if np.any((y != 0)  & (y != 1)):
                raise ValueError(
                    "Output value cannot be anything other than 0 and 1. If you want more than two level of output, try converting the out values into a vector of 0 and 1 only.")

            activations = []
            prev_activation = np.ones((1, n+1))
            prev_activation[:,1:] = np.asmatrix(x)[:,:]
            for i in range(theta_count):
                theta = np.asmatrix(thetas[i])
                activations.append(prev_activation)
                zt = np.dot(prev_activation, np.transpose(theta))
                at = np.ones((zt.shape[0], zt.shape[1] + 1))
                at[:, 1:] = self.helper.sigmoid(zt)
                prev_activation = at

            ht = prev_activation[:, 1:]

            # for y==1 and y==0 items separately
            j_partial = sum(-np.log(ht[y == 1] + eps))
            j_partial += sum(-np.log(1 + eps - ht[y == 0]))

            deltas = []

            prev_delta = np.transpose(ht - y)
            deltas.append(prev_delta)

            for i in range(1, theta_count):
                a = np.transpose(activations[-i])

                delta = np.multiply(np.dot(np.transpose(thetas[-i]), prev_delta), np.multiply(a, 1 - a))[1:,:]
                deltas.append(delta)
                prev_delta = delta

            DELs = [np.dot(d, a) for (d,a) in zip(deltas[::-1], activations)]

            return {'cost': j_partial, 'deltas' : DELs}


        def sample_pair_reducer(x, y):
            return {'cost': x['cost'] + y['cost'], 'deltas': [dx + dy for (dx,dy) in zip(x['deltas'], y['deltas'])]}

        result = functools.reduce(
            sample_pair_reducer,
            map(single_sample_mapper, zip(X, Y)),
            {'cost': 0, 'deltas': [np.zeros(t.shape) for t in thetas]});

        J = result["cost"]/m + self.cost_regularization(thetas, m)

        gradients = [(d/m)+self._theta_regularization(t, m) for (d,t) in zip(result["deltas"], thetas)]

        return (J, self._unroll_matrices(gradients))

    def train(self, X, Y, maxiter = 200, tolerance = 1e-5):
        if X.shape[0] != Y.shape[0]:
            raise(ValueError("X and Y must have same number of rows."))
        t = self._unroll_matrices(self._initial_thetas)
        res = op.minimize(
            fun = self._calculate_cost_gradient,
            x0 = t,
            jac = True,
            tol = tolerance,
            method = 'CG',
            options = {'maxiter': maxiter},
            args = (X, Y)
        );

        return Model(self._roll_into_matrices(res.x))

#np.finfo(float).eps # for y==1 and y==0 items separately