Michy mick001

## convolutional_nn_tutorial_2.R
# This script is used to resize images from 64x64 to 28x28 pixels

# Clear workspace
rm(list=ls())

# Load EBImage library
require(EBImage)

# Load data
X <- read.csv("olivetti_X.csv", header = F)

## convolutional_nn_tutorial_1.py
# -*- coding: utf-8 -*-

# Imports
from sklearn.datasets import fetch_olivetti_faces
import numpy as np

# Download Olivetti faces dataset
olivetti = fetch_olivetti_faces()
x = olivetti.images
y = olivetti.target

## rnn_example.R
# Clear workspace
rm(list=ls())

# Load libraries
require(rnn)

# Set seed for reproducibility purposes
set.seed(10)

# Set frequency

## im_rec_3.R
rm(list=ls())

# Load MXNet
require(mxnet)

# Train test datasets
train <- read.csv("train_28.csv")
test <- read.csv("test_28.csv")

# Fix train and test datasets

## im_rec_2.R
# Generate a train-test dataset

# Clean environment and load required packages
rm(list=ls())
require(EBImage)

# Set wd where resized greyscale images are located
setwd("C://dogs_resized")

# Out file

## im_rec_1.R
# Resize images and convert to grayscale

rm(list=ls())
require(EBImage)

# Set wd where images are located
setwd("C://dogs_images")
# Set d where to save images
save_in <- "C://dogs_images_resized"
# Load images names

## pressure_drop_revised.py
# Imports
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
from numpy import vectorize

# Let's define the variables
Q = np.linspace(0.01,1,50) # Flow rate m^3/s

## copulas_revised_2_7.R
# Sample 1000 observations from the distribution
sim <- rmvdc(my_dist, 1000)

# Plot the data for a visual comparison
plot(mydata$x, mydata$y, main = 'Test dataset x and y', col = "blue")
points(sim[,1], sim[,2], col = 'red')
legend('bottomright', c('Observed', 'Simulated'), col = c('blue', 'red'), pch=21)

cor(mydata, method = "kendall")
##           x         y

## copulas_revised_2_6.R
# Build the bivariate distribution
my_dist <- mvdc(claytonCopula(param = 1.48, dim = 2), margins = c("gamma","gamma"), paramMargins = list(list(shape = x_shape, rate = x_rate), list(shape = y_shape, rate = y_rate)))

# Generate random sample observations from the multivariate distribution
v <- rMvdc(5000, my_dist)
# Compute the density
pdf_mvd <- dMvdc(v, my_dist)
# Compute the CDF
cdf_mvd <- pMvdc(v, my_dist)

## copulas_revised_2_5.R
gf <- gofCopula(normalCopula(dim = 2), as.matrix(mydata), N = 50)
gf

## 	Parametric bootstrap goodness-of-fit test with 'method'="Sn", 'estim.method'="mpl"
##
## data:  x
## statistic = 0.29449, parameter = 0.59983, p-value = 0.009804

gfc <- gofCopula(claytonCopula(dim = 2), as.matrix(mydata), N = 50)
gfc
	# This script is used to resize images from 64x64 to 28x28 pixels

	# Clear workspace
	rm(list=ls())

	# Load EBImage library
	require(EBImage)

	# Load data
	X <- read.csv("olivetti_X.csv", header = F)
	# -- coding: utf-8 --

	# Imports
	from sklearn.datasets import fetch_olivetti_faces
	import numpy as np

	# Download Olivetti faces dataset
	olivetti = fetch_olivetti_faces()
	x = olivetti.images
	y = olivetti.target
	# Clear workspace
	rm(list=ls())

	# Load libraries
	require(rnn)

	# Set seed for reproducibility purposes
	set.seed(10)

	# Set frequency
	rm(list=ls())

	# Load MXNet
	require(mxnet)

	# Train test datasets
	train <- read.csv("train_28.csv")
	test <- read.csv("test_28.csv")

	# Fix train and test datasets
	# Generate a train-test dataset

	# Clean environment and load required packages
	rm(list=ls())
	require(EBImage)

	# Set wd where resized greyscale images are located
	setwd("C://dogs_resized")

	# Out file
	# Resize images and convert to grayscale

	rm(list=ls())
	require(EBImage)

	# Set wd where images are located
	setwd("C://dogs_images")
	# Set d where to save images
	save_in <- "C://dogs_images_resized"
	# Load images names
	# Imports
	import numpy as np
	import matplotlib.pyplot as plt
	from mpl_toolkits.mplot3d import Axes3D
	from matplotlib import cm
	from matplotlib.ticker import LinearLocator, FormatStrFormatter
	from numpy import vectorize

	# Let's define the variables
	Q = np.linspace(0.01,1,50) # Flow rate m^3/s
	# Sample 1000 observations from the distribution
	sim <- rmvdc(my_dist, 1000)

	# Plot the data for a visual comparison
	plot(mydata$x, mydata$y, main = 'Test dataset x and y', col = "blue")
	points(sim[,1], sim[,2], col = 'red')
	legend('bottomright', c('Observed', 'Simulated'), col = c('blue', 'red'), pch=21)

	cor(mydata, method = "kendall")
	## x y
	# Build the bivariate distribution
	my_dist <- mvdc(claytonCopula(param = 1.48, dim = 2), margins = c("gamma","gamma"), paramMargins = list(list(shape = x_shape, rate = x_rate), list(shape = y_shape, rate = y_rate)))

	# Generate random sample observations from the multivariate distribution
	v <- rMvdc(5000, my_dist)
	# Compute the density
	pdf_mvd <- dMvdc(v, my_dist)
	# Compute the CDF
	cdf_mvd <- pMvdc(v, my_dist)
	gf <- gofCopula(normalCopula(dim = 2), as.matrix(mydata), N = 50)
	gf

	## Parametric bootstrap goodness-of-fit test with 'method'="Sn", 'estim.method'="mpl"
	##
	## data: x
	## statistic = 0.29449, parameter = 0.59983, p-value = 0.009804

	gfc <- gofCopula(claytonCopula(dim = 2), as.matrix(mydata), N = 50)
	gfc