import os
import sys
import lr
import timeit
import numpy as np
import theano
from theano import tensor

def _get_rng(rand_state=None):
    if isinstance(rand_state, int) or rand_state is None:
        return np.random.RandomState(rand_state)
    return rand_state

def _xavier_tanh_w_init(n_inp, n_out, rand_state=42):
    rng = _get_rng(rand_state)
    bound = np.sqrt(6/(n_inp + n_out))
    return rng.uniform(low=-bound, high=bound, size=(n_inp, n_out))

def _xavier_sigmoid_w_init(n_inp, n_out, rand_state=42):
    rng = _get_rng(rand_state)
    bound = 4*np.sqrt(6/(n_inp + n_out))
    return rng.uniform(low=-bound, high=bound, size=(n_inp, n_out))

#Weight initialization methods for convolution layer.
_W_INIT_METHODS = {
    "xavier_tanh": _xavier_sigmoid_w_init,
    "xavier_sigmoid": _xavier_sigmoid_w_init
}

def _zero(x):
    return (x*0).sum()

def _l1(x):
    return abs(x).sum()

def _l2(x):
    return (x**2).sum()

#Regularization methods for weights.
_REGULARIZATIONS = {
    "none": _zero,
    "l1": _l1,
    "l2": _l2
}

def _grad(cost, wrt):
    try:
        return tensor.grad(cost=cost, wrt=wrt)
    except theano.gradient.DisconnectedInputError:
        return 0

class HiddenLayer:
    """
    Hidden Layer for Multi Layer Perceptron.
    """
    def __init__(
            self, 
            inp,
            n_inp, n_out,
            activation_f=tensor.tanh,
            w_init_f="xavier_tanh",
            reg="l2",
            rand_state=42):
        """
        Initialization of layer.
        Parameters:
        *inp: Input tensor.
        *n_inp: Number of input neurons.
        *n_out: Number of outut neurons.
        *activation_f: Activation function to use.
        *w_init_f: Weight initialization method.
        *reg: Regularization method for weights.
        *rand_state: Random state.
        """

        #checking validity of input method
        if isinstance(w_init_f, str):
            try:
                w_init_f = _W_INIT_METHODS[w_init_f]
            except KeyError:
                raise ValueError("'w_init_method' must be one in [%s]" %\
                    ", ".join(_W_INIT_METHODS.keys()))

        #input
        self.input = inp

        #creating weights matrix w
        self.w = theano.shared(
            np.asarray(
                w_init_f(n_inp, n_out),
                dtype=self.input.dtype),
            name="w")

        #regularization term symbolic expression
        if isinstance(reg, str):
            try:
                reg = _REGULARIZATIONS[reg]
            except KeyError:
                raise ValueError("'reg' must be one in [%s]" %\
                    ", ".join(_REGULARIZATIONS.keys()))
        self.reg = reg(self.w)

        #creating bias
        self.b = theano.shared(
            np.asarray(
                _get_rng(rand_state).uniform(
                    low=-0.5,
                    high=0.5,
                    size=(n_out,)),
                dtype=self.input.dtype),
            name="b")

        #model parameters
        self.params = [self.w, self.b]

        #symbolic expression for output
        linear_output = tensor.dot(self.input, self.w) + self.b
        self.output = activation_f(linear_output)

        #theano function to compute output
        self.f = theano.function([self.input], self.output)

class MultiLayerPerceptron:
    """
    Multi Layer Perceptron.
    """
    def __init__(
            self, 
            inp,
            n_inp, n_hidden, n_out,
            activation_f=tensor.tanh,
            w_init_f="xavier_tanh",
            reg="l2",
            rand_state=42):
        """
        Initialization of network.
        Parameters:
        *inp: Input tensor.
        *n_inp: Number of input neurons.
        *n_hidden: Number of hidden neurons.
        *n_out: Number of outut neurons.
        *activation_f: Activation function to use.
        *w_init_f: Weight initialization method.
        *reg: Regularization method for weights.
        *rand_state: Random state.
        """

        #input/output matrices
        self.inp = inp

        #hidden layer
        self.hidden_layer = HiddenLayer(
            inp=inp,
            n_inp=n_inp, n_out=n_hidden,
            activation_f=activation_f,
            reg=reg)

        #logistic regression layer
        self.log_reg_layer = lr.LogisticRegression(
            inp=self.hidden_layer.output,
            n_inp=n_hidden, n_out=n_out,
            reg=reg)

        #model parameters
        self.params = self.hidden_layer.params + self.log_reg_layer.params

        #regularization term symbolic expression
        self.reg = self.hidden_layer.reg + self.log_reg_layer.reg

        #cost(y) function
        self.cost = self.log_reg_layer.cost

        #score(y) function
        self.score = self.log_reg_layer.score

        #pred function
        self.pred = self.log_reg_layer.pred

bfc38884eb87df382104e15b11c7431da6a7a844
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class MultiLayerPerceptron:
    """
    Multi Layer Perceptron.
    """

    def __init__(self, inp, n_inp, n_hidden, n_out, activation_f=tensor.
        tanh, w_init_f='xavier_tanh', reg='l2', rand_state=42):
        """
        Initialization of network.
        Parameters:
        *inp: Input tensor.
        *n_inp: Number of input neurons.
        *n_hidden: Number of hidden neurons.
        *n_out: Number of outut neurons.
        *activation_f: Activation function to use.
        *w_init_f: Weight initialization method.
        *reg: Regularization method for weights.
        *rand_state: Random state.
        """
        self.inp = inp
        self.hidden_layer = HiddenLayer(inp=inp, n_inp=n_inp, n_out=
            n_hidden, activation_f=activation_f, reg=reg)
        self.log_reg_layer = lr.LogisticRegression(inp=self.hidden_layer.
            output, n_inp=n_hidden, n_out=n_out, reg=reg)
        self.params = self.hidden_layer.params + self.log_reg_layer.params
        self.reg = self.hidden_layer.reg + self.log_reg_layer.reg
        self.cost = self.log_reg_layer.cost
        self.score = self.log_reg_layer.score
        self.pred = self.log_reg_layer.pred

<mask token>


class HiddenLayer:
    """
    Hidden Layer for Multi Layer Perceptron.
    """

    def __init__(self, inp, n_inp, n_out, activation_f=tensor.tanh,
        w_init_f='xavier_tanh', reg='l2', rand_state=42):
        """
        Initialization of layer.
        Parameters:
        *inp: Input tensor.
        *n_inp: Number of input neurons.
        *n_out: Number of outut neurons.
        *activation_f: Activation function to use.
        *w_init_f: Weight initialization method.
        *reg: Regularization method for weights.
        *rand_state: Random state.
        """
        if isinstance(w_init_f, str):
            try:
                w_init_f = _W_INIT_METHODS[w_init_f]
            except KeyError:
                raise ValueError("'w_init_method' must be one in [%s]" %
                    ', '.join(_W_INIT_METHODS.keys()))
        self.input = inp
        self.w = theano.shared(np.asarray(w_init_f(n_inp, n_out), dtype=
            self.input.dtype), name='w')
        if isinstance(reg, str):
            try:
                reg = _REGULARIZATIONS[reg]
            except KeyError:
                raise ValueError("'reg' must be one in [%s]" % ', '.join(
                    _REGULARIZATIONS.keys()))
        self.reg = reg(self.w)
        self.b = theano.shared(np.asarray(_get_rng(rand_state).uniform(low=
            -0.5, high=0.5, size=(n_out,)), dtype=self.input.dtype), name='b')
        self.params = [self.w, self.b]
        linear_output = tensor.dot(self.input, self.w) + self.b
        self.output = activation_f(linear_output)
        self.f = theano.function([self.input], self.output)


class MultiLayerPerceptron:
    """
    Multi Layer Perceptron.
    """

    def __init__(self, inp, n_inp, n_hidden, n_out, activation_f=tensor.
        tanh, w_init_f='xavier_tanh', reg='l2', rand_state=42):
        """
        Initialization of network.
        Parameters:
        *inp: Input tensor.
        *n_inp: Number of input neurons.
        *n_hidden: Number of hidden neurons.
        *n_out: Number of outut neurons.
        *activation_f: Activation function to use.
        *w_init_f: Weight initialization method.
        *reg: Regularization method for weights.
        *rand_state: Random state.
        """
        self.inp = inp
        self.hidden_layer = HiddenLayer(inp=inp, n_inp=n_inp, n_out=
            n_hidden, activation_f=activation_f, reg=reg)
        self.log_reg_layer = lr.LogisticRegression(inp=self.hidden_layer.
            output, n_inp=n_hidden, n_out=n_out, reg=reg)
        self.params = self.hidden_layer.params + self.log_reg_layer.params
        self.reg = self.hidden_layer.reg + self.log_reg_layer.reg
        self.cost = self.log_reg_layer.cost
        self.score = self.log_reg_layer.score
        self.pred = self.log_reg_layer.pred

<mask token>


def _get_rng(rand_state=None):
    if isinstance(rand_state, int) or rand_state is None:
        return np.random.RandomState(rand_state)
    return rand_state


def _xavier_tanh_w_init(n_inp, n_out, rand_state=42):
    rng = _get_rng(rand_state)
    bound = np.sqrt(6 / (n_inp + n_out))
    return rng.uniform(low=-bound, high=bound, size=(n_inp, n_out))


<mask token>


def _zero(x):
    return (x * 0).sum()


<mask token>


def _l2(x):
    return (x ** 2).sum()


<mask token>


def _grad(cost, wrt):
    try:
        return tensor.grad(cost=cost, wrt=wrt)
    except theano.gradient.DisconnectedInputError:
        return 0


class HiddenLayer:
    """
    Hidden Layer for Multi Layer Perceptron.
    """

    def __init__(self, inp, n_inp, n_out, activation_f=tensor.tanh,
        w_init_f='xavier_tanh', reg='l2', rand_state=42):
        """
        Initialization of layer.
        Parameters:
        *inp: Input tensor.
        *n_inp: Number of input neurons.
        *n_out: Number of outut neurons.
        *activation_f: Activation function to use.
        *w_init_f: Weight initialization method.
        *reg: Regularization method for weights.
        *rand_state: Random state.
        """
        if isinstance(w_init_f, str):
            try:
                w_init_f = _W_INIT_METHODS[w_init_f]
            except KeyError:
                raise ValueError("'w_init_method' must be one in [%s]" %
                    ', '.join(_W_INIT_METHODS.keys()))
        self.input = inp
        self.w = theano.shared(np.asarray(w_init_f(n_inp, n_out), dtype=
            self.input.dtype), name='w')
        if isinstance(reg, str):
            try:
                reg = _REGULARIZATIONS[reg]
            except KeyError:
                raise ValueError("'reg' must be one in [%s]" % ', '.join(
                    _REGULARIZATIONS.keys()))
        self.reg = reg(self.w)
        self.b = theano.shared(np.asarray(_get_rng(rand_state).uniform(low=
            -0.5, high=0.5, size=(n_out,)), dtype=self.input.dtype), name='b')
        self.params = [self.w, self.b]
        linear_output = tensor.dot(self.input, self.w) + self.b
        self.output = activation_f(linear_output)
        self.f = theano.function([self.input], self.output)


class MultiLayerPerceptron:
    """
    Multi Layer Perceptron.
    """

    def __init__(self, inp, n_inp, n_hidden, n_out, activation_f=tensor.
        tanh, w_init_f='xavier_tanh', reg='l2', rand_state=42):
        """
        Initialization of network.
        Parameters:
        *inp: Input tensor.
        *n_inp: Number of input neurons.
        *n_hidden: Number of hidden neurons.
        *n_out: Number of outut neurons.
        *activation_f: Activation function to use.
        *w_init_f: Weight initialization method.
        *reg: Regularization method for weights.
        *rand_state: Random state.
        """
        self.inp = inp
        self.hidden_layer = HiddenLayer(inp=inp, n_inp=n_inp, n_out=
            n_hidden, activation_f=activation_f, reg=reg)
        self.log_reg_layer = lr.LogisticRegression(inp=self.hidden_layer.
            output, n_inp=n_hidden, n_out=n_out, reg=reg)
        self.params = self.hidden_layer.params + self.log_reg_layer.params
        self.reg = self.hidden_layer.reg + self.log_reg_layer.reg
        self.cost = self.log_reg_layer.cost
        self.score = self.log_reg_layer.score
        self.pred = self.log_reg_layer.pred

<mask token>


def _get_rng(rand_state=None):
    if isinstance(rand_state, int) or rand_state is None:
        return np.random.RandomState(rand_state)
    return rand_state


def _xavier_tanh_w_init(n_inp, n_out, rand_state=42):
    rng = _get_rng(rand_state)
    bound = np.sqrt(6 / (n_inp + n_out))
    return rng.uniform(low=-bound, high=bound, size=(n_inp, n_out))


<mask token>


def _zero(x):
    return (x * 0).sum()


def _l1(x):
    return abs(x).sum()


def _l2(x):
    return (x ** 2).sum()


<mask token>


def _grad(cost, wrt):
    try:
        return tensor.grad(cost=cost, wrt=wrt)
    except theano.gradient.DisconnectedInputError:
        return 0


class HiddenLayer:
    """
    Hidden Layer for Multi Layer Perceptron.
    """

    def __init__(self, inp, n_inp, n_out, activation_f=tensor.tanh,
        w_init_f='xavier_tanh', reg='l2', rand_state=42):
        """
        Initialization of layer.
        Parameters:
        *inp: Input tensor.
        *n_inp: Number of input neurons.
        *n_out: Number of outut neurons.
        *activation_f: Activation function to use.
        *w_init_f: Weight initialization method.
        *reg: Regularization method for weights.
        *rand_state: Random state.
        """
        if isinstance(w_init_f, str):
            try:
                w_init_f = _W_INIT_METHODS[w_init_f]
            except KeyError:
                raise ValueError("'w_init_method' must be one in [%s]" %
                    ', '.join(_W_INIT_METHODS.keys()))
        self.input = inp
        self.w = theano.shared(np.asarray(w_init_f(n_inp, n_out), dtype=
            self.input.dtype), name='w')
        if isinstance(reg, str):
            try:
                reg = _REGULARIZATIONS[reg]
            except KeyError:
                raise ValueError("'reg' must be one in [%s]" % ', '.join(
                    _REGULARIZATIONS.keys()))
        self.reg = reg(self.w)
        self.b = theano.shared(np.asarray(_get_rng(rand_state).uniform(low=
            -0.5, high=0.5, size=(n_out,)), dtype=self.input.dtype), name='b')
        self.params = [self.w, self.b]
        linear_output = tensor.dot(self.input, self.w) + self.b
        self.output = activation_f(linear_output)
        self.f = theano.function([self.input], self.output)


class MultiLayerPerceptron:
    """
    Multi Layer Perceptron.
    """

    def __init__(self, inp, n_inp, n_hidden, n_out, activation_f=tensor.
        tanh, w_init_f='xavier_tanh', reg='l2', rand_state=42):
        """
        Initialization of network.
        Parameters:
        *inp: Input tensor.
        *n_inp: Number of input neurons.
        *n_hidden: Number of hidden neurons.
        *n_out: Number of outut neurons.
        *activation_f: Activation function to use.
        *w_init_f: Weight initialization method.
        *reg: Regularization method for weights.
        *rand_state: Random state.
        """
        self.inp = inp
        self.hidden_layer = HiddenLayer(inp=inp, n_inp=n_inp, n_out=
            n_hidden, activation_f=activation_f, reg=reg)
        self.log_reg_layer = lr.LogisticRegression(inp=self.hidden_layer.
            output, n_inp=n_hidden, n_out=n_out, reg=reg)
        self.params = self.hidden_layer.params + self.log_reg_layer.params
        self.reg = self.hidden_layer.reg + self.log_reg_layer.reg
        self.cost = self.log_reg_layer.cost
        self.score = self.log_reg_layer.score
        self.pred = self.log_reg_layer.pred

import os
import sys
import lr
import timeit
import numpy as np
import theano
from theano import tensor

def _get_rng(rand_state=None):
    if isinstance(rand_state, int) or rand_state is None:
        return np.random.RandomState(rand_state)
    return rand_state

def _xavier_tanh_w_init(n_inp, n_out, rand_state=42):
    rng = _get_rng(rand_state)
    bound = np.sqrt(6/(n_inp + n_out))
    return rng.uniform(low=-bound, high=bound, size=(n_inp, n_out))

def _xavier_sigmoid_w_init(n_inp, n_out, rand_state=42):
    rng = _get_rng(rand_state)
    bound = 4*np.sqrt(6/(n_inp + n_out))
    return rng.uniform(low=-bound, high=bound, size=(n_inp, n_out))

#Weight initialization methods for convolution layer.
_W_INIT_METHODS = {
    "xavier_tanh": _xavier_sigmoid_w_init,
    "xavier_sigmoid": _xavier_sigmoid_w_init
}

def _zero(x):
    return (x*0).sum()

def _l1(x):
    return abs(x).sum()

def _l2(x):
    return (x**2).sum()

#Regularization methods for weights.
_REGULARIZATIONS = {
    "none": _zero,
    "l1": _l1,
    "l2": _l2
}

def _grad(cost, wrt):
    try:
        return tensor.grad(cost=cost, wrt=wrt)
    except theano.gradient.DisconnectedInputError:
        return 0

class HiddenLayer:
    """
    Hidden Layer for Multi Layer Perceptron.
    """
    def __init__(
            self, 
            inp,
            n_inp, n_out,
            activation_f=tensor.tanh,
            w_init_f="xavier_tanh",
            reg="l2",
            rand_state=42):
        """
        Initialization of layer.
        Parameters:
        *inp: Input tensor.
        *n_inp: Number of input neurons.
        *n_out: Number of outut neurons.
        *activation_f: Activation function to use.
        *w_init_f: Weight initialization method.
        *reg: Regularization method for weights.
        *rand_state: Random state.
        """

        #checking validity of input method
        if isinstance(w_init_f, str):
            try:
                w_init_f = _W_INIT_METHODS[w_init_f]
            except KeyError:
                raise ValueError("'w_init_method' must be one in [%s]" %\
                    ", ".join(_W_INIT_METHODS.keys()))

        #input
        self.input = inp

        #creating weights matrix w
        self.w = theano.shared(
            np.asarray(
                w_init_f(n_inp, n_out),
                dtype=self.input.dtype),
            name="w")

        #regularization term symbolic expression
        if isinstance(reg, str):
            try:
                reg = _REGULARIZATIONS[reg]
            except KeyError:
                raise ValueError("'reg' must be one in [%s]" %\
                    ", ".join(_REGULARIZATIONS.keys()))
        self.reg = reg(self.w)

        #creating bias
        self.b = theano.shared(
            np.asarray(
                _get_rng(rand_state).uniform(
                    low=-0.5,
                    high=0.5,
                    size=(n_out,)),
                dtype=self.input.dtype),
            name="b")

        #model parameters
        self.params = [self.w, self.b]

        #symbolic expression for output
        linear_output = tensor.dot(self.input, self.w) + self.b
        self.output = activation_f(linear_output)

        #theano function to compute output
        self.f = theano.function([self.input], self.output)

class MultiLayerPerceptron:
    """
    Multi Layer Perceptron.
    """
    def __init__(
            self, 
            inp,
            n_inp, n_hidden, n_out,
            activation_f=tensor.tanh,
            w_init_f="xavier_tanh",
            reg="l2",
            rand_state=42):
        """
        Initialization of network.
        Parameters:
        *inp: Input tensor.
        *n_inp: Number of input neurons.
        *n_hidden: Number of hidden neurons.
        *n_out: Number of outut neurons.
        *activation_f: Activation function to use.
        *w_init_f: Weight initialization method.
        *reg: Regularization method for weights.
        *rand_state: Random state.
        """

        #input/output matrices
        self.inp = inp

        #hidden layer
        self.hidden_layer = HiddenLayer(
            inp=inp,
            n_inp=n_inp, n_out=n_hidden,
            activation_f=activation_f,
            reg=reg)

        #logistic regression layer
        self.log_reg_layer = lr.LogisticRegression(
            inp=self.hidden_layer.output,
            n_inp=n_hidden, n_out=n_out,
            reg=reg)

        #model parameters
        self.params = self.hidden_layer.params + self.log_reg_layer.params

        #regularization term symbolic expression
        self.reg = self.hidden_layer.reg + self.log_reg_layer.reg

        #cost(y) function
        self.cost = self.log_reg_layer.cost

        #score(y) function
        self.score = self.log_reg_layer.score

        #pred function
        self.pred = self.log_reg_layer.pred
