"""

=====================================

Visualization of MLP weights on MNIST

=====================================

Sometimes looking at the learned coefficients of a neural network can provide

insight into the learning behavior. For example if weights look unstructured,

maybe some were not used at all, or if very large coefficients exist, maybe

regularization was too low or the learning rate too high.

This example shows how to plot some of the first layer weights in a

MLPClassifier trained on the MNIST dataset.

The input data consists of 28x28 pixel handwritten digits, leading to 784

features in the dataset. Therefore the first layer weight matrix have the shape

(784, hidden_layer_sizes[0]). We can therefore visualize a single column of

the weight matrix as a 28x28 pixel image.

To make the example run faster, we use very few hidden units, and train only

for a very short time. Training longer would result in weights with a much

smoother spatial appearance.

"""

print(__doc__)

import matplotlib.pyplot as plt

from sklearn.datasets import fetch_mldata

from sklearn.neural_network import MLPClassifier

from sklearn.neural_network import MLPRegressor

from sklearn.metrics import mean_squared_error

from sklearn.metrics import log_loss

from sklearn.metrics import accuracy_score

from sklearn.metrics import mean_squared_error

from sklearn.decomposition import PCA

from sklearn.manifold import TSNE

from sklearn.decomposition import FastICA

from sklearn.decomposition import FactorAnalysis

from sklearn.manifold import Isomap

import umap

import numpy as np

import cv2

import glob

import time

import math

from sklearn.preprocessing import MultiLabelBinarizer

from sklearn.preprocessing import MinMaxScaler

import matplotlib.cm as cm

from sklearn.model_selection import train_test_split

import xlsxwriter

import pandas as pd

plt.close("all")

#mnist = fetch_mldata("MNIST original")

#img = cv2.imread('honolulu-airport.jpg',0)

#images = [ np.reshape( np.reshape (cv2.imread(file, 0)/255 , 394*1200) ,(1200,394)) for file in glob.glob("*.jpg")]

#plt.figure(1)

#plt.imshow(images[0], cmap = 'gray', interpolation = 'bicubic')

#plt.xticks([]), plt.yticks([]) # to hide tick values on X and Y axis

#plt.show()

#wx , wy =25, 25

Nhid1, Nhid2=500,500

#I = plt.imread("./Input/OPCarray_InputMask/1.png")

#plt.imshow(I)

# read Input mask images

images = [ plt.imread(file) for file in sorted(glob.glob("./Input/OPCarray_InputMask/*.png") )]

#plt.imshow(images[1])

#plt.pause(1)

#images = [ cv2.resize(cv2.imread(file, 0) , (

# int(wx),int(wy) ) ) for file in glob.glob("./Input/OPCarray_InputMask/*.tif")]

EntireImageSizexi=600

EntireImageSizeyi=400

EntireImageSizexo=65

EntireImageSizeyo=38

for idx in enumerate(images):

#resizing images, mainly downsampling

images[idx[0]]= np.round (cv2.resize(images[idx[0]],(EntireImageSizexi,EntireImageSizeyi)))

#Padding input images to larger size

#images[idx[0]]=np.pad(images[idx[0]], ((50,50),(50,50)),

# 'constant', constant_values=((0,0), (0,0)) )

#images=cv2.resize(images,(50,50))

# read binarized SEM images

images2 = [ plt.imread(file) for file in sorted( glob.glob("./Output/etched/OPCArray/DRIE_shallow_SiSubs/*.png") )]

for idx in enumerate(images2):

images2[idx[0]]=np.round ( cv2.resize(images2[idx[0]],(EntireImageSizexo,EntireImageSizeyo)) )

#plt.imshow(images2[1])

#plt.pause(1)

for ii in range(len(images)):

images[ii]= np.reshape (images[ii] , EntireImageSizexi*EntireImageSizeyi )

for ii in range(len(images2)):

images2[ii]= np.reshape (images2[ii] , EntireImageSizexo*EntireImageSizeyo)

subimages=np.asarray(images)

subimages2=np.asarray(images2)

split=45

X, y = np.float64(subimages) , np.float64(subimages2)

del subimages, subimages2

#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)

X_train, X_test = X[:split], X[split:]

y_train, y_test = y[:split], y[split:]

pca1=PCA(n_components=2,svd_solver = 'auto', random_state=1)

ica1=FastICA(n_components=20, random_state=1)

tsne1 = TSNE(n_components=2,random_state=1,method='exact' )

fa1=FactorAnalysis(n_components=5, random_state=1)

embedding = Isomap(n_components=20)

reducer = umap.UMAP(n_components=30, random_state=1)

#X_train_t =pca1.fit_transform(X_train)

#X_train_t =ica1.fit_transform(X_train)

#X_train_t = fa1.fit_transform(X_train)

#X_train_t=tsne1.fit_transform(X_train)

#X_train_t=embedding.fit_transform(X_train)

#X_train_t =reducer.fit_transform(X_train)

sc1=MinMaxScaler()

#X_train_t =sc1.fit_transform(X_train_t)

X_train_t=X_train

## convert your array into a dataframe

df = pd.DataFrame (X_train)

## save to xlsx file

filepath = 'my_excel_file.xlsx'

df.to_excel(filepath, index=False)

#X_test_t= =pca1.transform(X_test)

#X_test_t=ica1.transform(X_test)

#X_test_t= fa1.transform(X_test)

#X_test_t=embedding.transform(X_test)

#X_test_t=reducer.transform(X_test)

#X_test_t=sc1.transform(X_test_t)

X_test_t=X_test

plt.figure(1)

plt.scatter(X_train_t[:,0], X_train_t[:,1] )

plt.scatter(X_test_t[:,0], X_test_t[:,1],marker='^', c='g' )

start = time. time()

# mlp = MLPClassifier(hidden_layer_sizes=(100, 100), max_iter=400, alpha=1e-4,

# solver='sgd', verbose=10, tol=1e-4, random_state=1)

mlp = MLPClassifier(hidden_layer_sizes=(Nhid1,Nhid2 ), activation='relu', solver='adam', alpha=0.0001,

epsilon=1e-08, n_iter_no_change=10)

#mlp= MLPRegressor(hidden_layer_sizes=(Nhid1,Nhid2), activation='relu', solver='adam', alpha=0.001,batch_size='auto',

# learning_rate='constant', learning_rate_init=1e-4, power_t=0.5, max_iter=100000, shuffle=True,

# random_state=None, tol=1e-6, verbose=True, warm_start=False, momentum=0.9,

# nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999,

# epsilon=1e-08)

mlp.fit(X_train_t, y_train)

ytrain_pred=mlp.predict(X_train_t)

ytest_pred=mlp.predict(X_test_t)

end = time. time()

print('execuation time is %f' % (end - start))

print("Training set score: %f" % mlp.score(X_train_t, y_train))

print("Test set score: %f" % mlp.score(X_test_t, y_test))

print('log loss')

#print (log_loss(y_test, mlp.predict_proba(X_test) ) )

print('accuracy score')

#print (accuracy_score(y_test, ytest_pred ) )

print('mean squared error in training set')

print (mean_squared_error(y_train, ytrain_pred ) )

print('mean squared error in test set')

print (mean_squared_error(y_test, ytest_pred ) )

#X_train =sc1.inverse_transform(X_train_t)

#X_train=tsne1.inverse_transform(X_train_t)

#X_test =sc1.inverse_transform(X_test_t)

#X_test=tsne1.inverse_transform(X_test_t)

y_train=np.reshape(y_train , (split, EntireImageSizeyo,EntireImageSizexo))

ytrain_pred=np.reshape(ytrain_pred , (split,EntireImageSizeyo,EntireImageSizexo))

y_test=np.reshape(y_test , (len(images)-split,EntireImageSizeyo,EntireImageSizexo))

ytest_pred=np.reshape(ytest_pred , (len(images)-split,EntireImageSizeyo,EntireImageSizexo))

#save real images---in case of random train test split, no ordering is required

#since order already lost

lll=[str(kk) for kk in range(1,51)]

lll=sorted(lll)

for hh in range(0,45):

plt.imsave('./output/etched/OPCArray/Temp1_real/train/'+ lll[hh]+'.png', y_train[hh],cmap=cm.gray)

for hh in range(0,5):

plt.imsave('./output/etched/OPCArray/Temp1_real/test/'+ lll[hh+45]+'.png', y_test[hh],cmap=cm.gray)

# save preidcted images

lll=[str(kk) for kk in range(1,51)]

lll=sorted(lll)

for hh in range(0,45):

plt.imsave('./output/etched/OPCArray/Temp2/train/'+ lll[hh]+'.png', ytrain_pred[hh],cmap=cm.gray)

for hh in range(0,5):

plt.imsave('./output/etched/OPCArray/Temp2/test/'+ lll[hh+45]+'.png', ytest_pred[hh],cmap=cm.gray)

plt.figure(2)

plt.imshow(np.uint8(ytrain_pred[2]), cmap = 'gray', interpolation = 'bicubic')

plt.xticks([]), plt.yticks([]) # to hide tick values on X and Y axis

plt.show()

plt.figure(3)

plt.imshow(np.uint8(y_train[2]), cmap = 'gray', interpolation = 'bicubic')

plt.xticks([]), plt.yticks([]) # to hide tick values on X and Y axis

plt.show()

#import sys

#sys.exit("Error message")

plt.figure(4)

plt.imshow(np.uint8(ytest_pred[3]), cmap = 'gray', interpolation = 'bicubic')

plt.xticks([]), plt.yticks([]) # to hide tick values on X and Y axis

plt.show()

plt.figure(5)

plt.imshow(np.uint8(y_test[3]), cmap = 'gray', interpolation = 'bicubic')

plt.xticks([]), plt.yticks([]) # to hide tick values on X and Y axis

plt.show()