# Path: lxmls/deep_learning/utils.py
# class Model(object):
#     def __init__(self, **config):
#         self.initialized = False
# 
#     def initialize_features(self, *args):
#         self.initialized = True
#         raise NotImplementedError(
#             "Need to implement initialize_features method"
#         )
# 
#     def get_features(self, input=None, output=None):
#         """
#         Default feature extraction is do nothing
#         """
#         return {'input': input, 'output': output}
# 
#     def predict(self, *args):
#         raise NotImplementedError("Need to implement predict method")
# 
#     def update(self, *args):
#         # This needs to return at least {'cost' : 0}
#         raise NotImplementedError("Need to implement update method")
#         return {'cost': None}
# 
#     def set(self, **kwargs):
#         raise NotImplementedError("Need to implement set method")
# 
#     def get(self, name):
#         raise NotImplementedError("Need to implement get method")
# 
#     def save(self):
#         raise NotImplementedError("Need to implement save method")
# 
#     def load(self, model_folder):
#         raise NotImplementedError("Need to implement load method")
# 
# def glorot_weight_init(shape, activation_function, random_seed=None):
#     """Layer weight initialization after Xavier Glorot et. al"""
# 
#     if random_seed is None:
#         random_seed = np.random.RandomState(1234)
# 
#     # Weights are uniform distributed with span depending on input and output
#     # sizes
#     num_inputs, num_outputs = shape
#     weight = random_seed.uniform(
#         low=-np.sqrt(6. / (num_inputs + num_outputs)),
#         high=np.sqrt(6. / (num_inputs + num_outputs)),
#         size=(num_outputs, num_inputs)
#     )
# 
#     # Scaling factor depending on non-linearity
#     if activation_function == 'sigmoid':
#         weight *= 4
#     elif activation_function == 'softmax':
#         weight *= 4
# 
#     return weight


import os
import yaml
import numpy as np
        import matplotlib.pyplot as plt
from six.moves import cPickle
from copy import deepcopy
from lxmls.deep_learning.utils import Model, glorot_weight_init
def initialize_mlp_parameters(geometry, loaded_parameters=None,
                              random_seed=None):
    """
    Initialize parameters from geometry or existing weights
    """

    num_layers = len(geometry) - 1
    num_hidden_layers = num_layers - 1
    activation_functions = ['sigmoid']*num_hidden_layers + ['softmax']

    # Initialize random seed if not given
    if random_seed is None:
        random_seed = np.random.RandomState(1234)

    if loaded_parameters is not None:
        assert len(loaded_parameters) == num_layers, \
            "New geometry not matching model saved"

    parameters = []
    for n in range(num_layers):

        # Weights
        if loaded_parameters is not None:
            weight, bias = loaded_parameters[n]
            assert weight.shape == (geometry[n+1], geometry[n]), \
                "New geometry does not match for weigths in layer %d" % n
            assert bias.shape == (1, geometry[n+1]), \
                "New geometry does not match for bias in layer %d" % n

        else:

# Path: lxmls/deep_learning/utils.py
# class Model(object):
#     def __init__(self, **config):
#         self.initialized = False
# 
#     def initialize_features(self, *args):
#         self.initialized = True
#         raise NotImplementedError(
#             "Need to implement initialize_features method"
#         )
# 
#     def get_features(self, input=None, output=None):
#         """
#         Default feature extraction is do nothing
#         """
#         return {'input': input, 'output': output}
# 
#     def predict(self, *args):
#         raise NotImplementedError("Need to implement predict method")
# 
#     def update(self, *args):
#         # This needs to return at least {'cost' : 0}
#         raise NotImplementedError("Need to implement update method")
#         return {'cost': None}
# 
#     def set(self, **kwargs):
#         raise NotImplementedError("Need to implement set method")
# 
#     def get(self, name):
#         raise NotImplementedError("Need to implement get method")
# 
#     def save(self):
#         raise NotImplementedError("Need to implement save method")
# 
#     def load(self, model_folder):
#         raise NotImplementedError("Need to implement load method")
# 
# def glorot_weight_init(shape, activation_function, random_seed=None):
#     """Layer weight initialization after Xavier Glorot et. al"""
# 
#     if random_seed is None:
#         random_seed = np.random.RandomState(1234)
# 
#     # Weights are uniform distributed with span depending on input and output
#     # sizes
#     num_inputs, num_outputs = shape
#     weight = random_seed.uniform(
#         low=-np.sqrt(6. / (num_inputs + num_outputs)),
#         high=np.sqrt(6. / (num_inputs + num_outputs)),
#         size=(num_outputs, num_inputs)
#     )
# 
#     # Scaling factor depending on non-linearity
#     if activation_function == 'sigmoid':
#         weight *= 4
#     elif activation_function == 'softmax':
#         weight *= 4
# 
#     return weight


# Path: lxmls/deep_learning/mlp.py
import os
import yaml
import numpy as np
        import matplotlib.pyplot as plt
from six.moves import cPickle
from copy import deepcopy
from lxmls.deep_learning.utils import Model, glorot_weight_init

def initialize_mlp_parameters(geometry, loaded_parameters=None,
                              random_seed=None):
    """
    Initialize parameters from geometry or existing weights
    """

    num_layers = len(geometry) - 1
    num_hidden_layers = num_layers - 1
    activation_functions = ['sigmoid']*num_hidden_layers + ['softmax']

    # Initialize random seed if not given
    if random_seed is None:
        random_seed = np.random.RandomState(1234)

    if loaded_parameters is not None:
        assert len(loaded_parameters) == num_layers, \
            "New geometry not matching model saved"

    parameters = []
    for n in range(num_layers):

        # Weights
        if loaded_parameters is not None:
            weight, bias = loaded_parameters[n]
            assert weight.shape == (geometry[n+1], geometry[n]), \
                "New geometry does not match for weigths in layer %d" % n
            assert bias.shape == (1, geometry[n+1]), \
                "New geometry does not match for bias in layer %d" % n

        else:

            weight = glorot_weight_init(