# Data Science and Machine Learning 
# Seyed Mahmoud Mirkhan, 1404
# Chapter 1 Computer Programming


# R, Functions of Numbers 
x = 3.14159
print(round(x, 3)) 
floor(x); floor(x) + 1 
library(MASS)
fractions(3.14)   # ans: 157/50
print(Inf) 

# Python, Functions of Numbers 
import numpy as np 
x = 3.14159
print(np.round(x, 3))   
np.floor(x); np.ceil(x)   
from fractions import Fraction
print(Fraction(3.14)) # ans: ! 7070651414971679/2251799813685248
print(float('inf')    

% Matlab, Functions of Numbers 
x = 3.14159;
disp(round(x, 3))
floor(x); ceil(x)
rats(3.14)   % ans = '    157/50    ' 
disp(Inf)

# Data Science and Machine Learning 
# Seyed Mahmoud Mirkhan, 1404
# Chapter 1 Computer Programming

# R 
print(paste(exp(pi), pi ^ exp(1)))  

# Python 
import numpy as np 
print(np.exp(np.pi), np.pi ** np.exp(1))

% Matlab 
sprintf("%f %f", exp(1) ^ pi, pi ^ exp(1))

# Data Science and Machine Learning 
# Seyed Mahmoud Mirkhan, 1404
# Chapter 1 Computer Programming

# R, Vector indices with conditions 
a <- seq(0, 1, length.out=11) # or a<-seq(0, 1 0.1)
a[]               # Create vector 0, 0.1, …, 0.9, 1
a[c(2, 4, 6)]  # Select indices 2, 4 and 6 of vector
a[-c(2, 4, 6)] # Select indices except 2, 4 and 6 
# --------------------------------- 
a[length(a)-1]    # Select index second from end 
a[1:3]            # Select indices 1 through 3 
a[seq(1,10,2)]    # Select odd indices of vector
a[seq(11,1,-1)]   # Select reverse indices 
# --------------------------------- 
a[a > 0.5]        # Select vector greater than 0.5
which(a > 0.5)    # Select indices greater than 0.5 
a[a > 0.8 | a <= 0.1]  # Select a > 0.8 or a <= 0.1
which(a > 0.8 | a <= 0.1) # Select indices 
# --------------------------------- 

# Python, Vector indices with conditions 
import numpy as np
a = np.linspace(0, 1, 11); # a or print(a) or a[:] 
a[np.array([1, 3, 5])] 
# --------------------------------- 
# Show array indices except above indices 
import itertools 
[item for idx, item in enumerate(a) if idx not in {1,3,5}] 
indices = [1, 3, 5]
b = np.where(np.isin(np.arange(len(a)), indices), -1, a)
print(b[b != -1])
# --------------------------------- 
a[-2]
a[0:3]
a[0:10:2]  # a[::2] 
a[::-1]
# --------------------------------- 
a[a > 0.5] 
np.where(a > 0.5)   
a1 = [a for a in a if a > 0.8 or a <= 0.1]
print(a1)
a[(a <= 0.1) | (a > 0.8)]  
print(np.concatenate((np.where(a <= 0.1), np.where(a > 0.8)), axis=1)) 
a[np.logical_or(a <= 0.1 , a > 0.8)]
func = np.vectorize(lambda t: t<= 0.1 or t > 0.8)
a[func(a)] 

% Matlab, Vector indices with conditions 
a = linspace(0, 1, 11) 
a([2 4 6]) 
a(setdiff(1:end, [2 4 6]))
% --------------------------------- 
a(length(a)-1)
a(1:3)
a(1:2:10)   
a(10:-1:1)
a(a > 0.5) 
find(a > 0.5)    
a(a > 0.8 | a <= 0.1)  
find(a > 0.8 | a <= 0.1)

# Data Science and Machine Learning 
# Seyed Mahmoud Mirkhan, 1404
# Chapter 1 Computer Programming

# R, Selection of Component of Matrix 
a = matrix(1:9, nrow=3, byrow=TRUE); a 
a[2,3]          # component(2,3) of matrix
a[2,]           # Second row
a[,3]           # Third column
a[1:2, 2:3] # Submatrix, rows(1,2) and columns (2,3)
a[seq(1,3,2), seq(1,3,2)] # Selection submatrix 
# a[row(a) > 2] # ans: [1] 7 8 9
# a[row(a) > 2 & col(a) > 2] # ans: [1] 9  
which(a > 6, arr.ind = TRUE)  

# Python, Selection of Component of Matrix 
import numpy as np 
a = np.array([[1,2,3], [4,5,6], [7,8,9]]); a 
a[1,2]
a[1,:]
a[:,2]
a[[[0],[1]], [1,2]]
a[np.ix_(np.arange(0,3,2), np.arange(0,3,2))] 
np.where(a > 6)    

% Matlab, Selection of Component of Matrix 
a = [1 2 3;4 5 6; 7 8 9] 
a(2,3)              
a(2,:)               
a(:,3)               
a(1:2, 2:3) 
a(1:2:3, 1:2:3) 
[row, col] = find(a > 6)

# Data Science and Machine Learning 
# Seyed Mahmoud Mirkhan, 1404
# Chapter 1 Computer Programming

# R, show iris data, The file is in the R software
head(iris) # help(iris)
iris1 = read.table("d:/data/iris.csv", header=TRUE, sep=",")
write.csv(iris, file="d:/data/iris.csv", row.names=FALSE)
plot(iris[,1], iris[,3], col=iris[,5], pch=16)

# Python, Show iris data
import pandas as pd
iris1 = pd.read_csv("d:/data/iris.csv") # or read file, iris_1
iris1.head()

% Matlab, Show iris data
iris1 = iris_dataset; size(iris1)
iris1(:,1:6)
iris2 = readtable("d:/data/iris.csv"); iris2(1:6,:)

# Data Science and Machine Learning 
# Seyed Mahmoud Mirkhan, 1404
# Chapter 1 Computer Programming

# Python, MNIST data of handwriting set of images 
import tensorflow as tf
import numpy as np
from tensorflow.keras.datasets import mnist
from matplotlib import pyplot
(train_X, train_y), (test_X, test_y) = mnist.load_data()
i = 31   # Showing digit 8 with index 31
pyplot.imshow(train_X[i])
pyplot.show()
pyplot.imshow(train_X[i], cmap=pyplot.get_cmap('gray'))
pyplot.show()
print(train_X[i])
print(np.shape(train_X[i]))

% Matlab, MNIST data of handwriting set of images 
load('d:/data/mnist.mat'); help('mnist.mat')
size(training.images)
size(test.images) %image (training.images(:,:,18)*255);
image (rescale(training.images(:,:,18),0,255));
axis square equal
colormap(gray) % Change color Image to Gray Style
colorbar
training.labels(18)
title (sprintf ("Digit: %d", training.labels(18)))
training; training.images(:,:,18)*255

# R, MNIST data of handwriting set of images 
data <- read.csv("d:/data/train.csv")
dim(data); head(data[1:6]); unique(unlist(data[1]))
min(data[2:785]); max(data[2:785])
sample_8 <- matrix(as.numeric(data[31,-1]), nrow=28, byrow=TRUE)
# Rotate the matrix by reversing elements in each column
rotate <- function(x) t(apply(x, 2, rev)) 
par(mfrow = c(1,2))
image(rotate(sample_8))
image(rotate(sample_8), col = grey.colors(255))
# Write matrix data to clipboard and paste to excel
write.table(sample_8, 'clipboard', sep='\t')

# Data Science and Machine Learning 
# Seyed Mahmoud Mirkhan, 1404
# Chapter 1 Computer Programming

% Matlab, List of name and image of flowers 
flowers = {}; 
flowers_name = {}  
flowers_name{1} = "Sunflower";   
flowers{1} =  "d:/data/flower_photos/sunflowers/678714585_addc9aaaef.jpg";
flowers_name{2} = " Rose ";   
flowers{2} = "d:/data/flower_photos/roses/5402157745_a384f0583d_n.jpg";
for (i = 1:2)
  a = imread(flowers{i});
  subplot(1,2,i)
  imshow(a);
  title(flowers_name{i}); 
end

# R, List of name and image of flowers 
flowers = list(); flowers_name = list()
flowers_name[[1]] = "Sunflower"   
flowers[[1]]="d:/data/flower_photos/sunflowers/678714585_addc9aaaef.jpg"
flowers_name[[2]] = " Rose "   
flowers[[2]] = "d:/data/flower_photos/roses/5402157745_a384f0583d_n.jpg"
# ------------------------------------------     
library("jpeg"); par(mfrow=c(1,2))  
for (i in 1:2){  
  a <- readJPEG(flowers[[i]])
  plot(0:1, 0:1, type="n", ann=FALSE, axes=FALSE)
  rasterImage(a,0,0,1,1) 
  title(flowers_name[[i]]) }
par(mfrow=c(1,1))   
# ------------------------------------------     
library(imager); 
par(mfrow=c(1,2))  
for (i in 1:2){  
  a <- load.image(flowers[[i]])
  plot(a)
  title(flowers_name[[i]]) }
par(mfrow=c(1,1)) 


# Python, List of dictionaries with name and image of flowers 
flowers = [
    {"name": "Sunflower", "image": "d:/data/flower_photos/sunflowers/678714585_addc9aaaef.jpg"},
    {"name": "Rose", "image": "d:/data/flower_photos/roses/5402157745_a384f0583d_n.jpg"}]
from matplotlib import pyplot as plt
from matplotlib import image as mpimg
for flower in flowers:
    a = mpimg.imread(flower["image"])
    plt.title(flower["name"]) 
    plt.imshow(a)
    plt.show()      
    
fig, (ax1, ax2) = plt.subplots(1, 2)
a1 = mpimg.imread(flowers[0]["image"])
ax1.set_title(flowers[0]["name"]) 
ax1.imshow(a1) 
a2 = mpimg.imread(flowers[1]["image"])
ax2.set_title(flowers[1]["name"]) 
ax2.imshow(a2)
plt.show()