# coding: utf-8
#__author__ = 'Ravi: https://kaggle.com/company'

import pandas as pd
from lasagne import layers
from lasagne.nonlinearities import  softmax, rectify
from lasagne.updates import nesterov_momentum
from nolearn.lasagne import NeuralNet
import numpy as np


def fit_convolutional_model(reshaped_train_x, y, image_width, image_height, reshaped_test_x):
    print("\n\nRunning Convolutional Net.  Optimization progress below\n\n")
    net2 = NeuralNet(
        layers=[
        ('input', layers.InputLayer),
        ('conv1', layers.Conv2DLayer),      #Convolutional layer.  Params defined below
        ('pool1', layers.MaxPool2DLayer),   #Like downsampling, for execution speed
        ('conv2', layers.Conv2DLayer),
        ('pool2', layers.MaxPool2DLayer),
        ('hidden3', layers.DenseLayer),
        ('output', layers.DenseLayer),
        ],
        

    input_shape=(None, 1, image_width, image_height),
    conv1_num_filters=6, conv1_filter_size=(5, 5), conv1_nonlinearity=rectify,
    pool1_pool_size=(2, 2),
    conv2_num_filters=8, conv2_filter_size=(5, 5), conv2_nonlinearity=rectify,
    hidden3_num_units=150,
    pool2_pool_size=(2, 2),
    output_num_units=10, output_nonlinearity=softmax,

    update_learning_rate=0.1,
    update_momentum=1,

    regression=False,
    max_epochs=75,
    verbose=1,
    )
    net2.fit(reshaped_train_x, y)
    predictions = net2.predict(reshaped_test_x)
    return(predictions)

def write_preds(preds, fname):
    pd.DataFrame({"ImageId": range(1,len(preds)+1), "Label": preds}).to_csv(fname, index=False, header=True)


# Read data
train = pd.read_csv('../input/train.csv')
train_y = train.ix[:,0].values.astype('int32')
# Divide pixel brightnesses by max brightness so they are between 0 and 1
# This helps the network optimizer make changes on the right order of magnitude
pixel_brightness_scaling_factor = train.max().max()
train_x = (train.ix[:,1:].values/pixel_brightness_scaling_factor).astype('float32')
test_x = (pd.read_csv('../input/test.csv').values/pixel_brightness_scaling_factor).astype('float32')
# Fit a (non-convolutional) neural network

# Reshape the array of pixels so a convolutional neural network knows where
# each pixel belongs in the image
image_width = image_height = int(train_x.shape[1] ** .5)
train_x_reshaped = train_x.reshape(train_x.shape[0], 1, image_height, image_width)
test_x_reshaped = test_x.reshape(test_x.shape[0], 1, image_height, image_width)
# Run the convolutional neural network
convolution_based_preds = fit_convolutional_model(train_x_reshaped, train_y, image_width, image_height, test_x_reshaped)
write_preds(convolution_based_preds, "convolutional_nn.csv")