The Fibonacci Sequence is the series of numbers:

0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89 …

https://www.mathsisfun.com/numbers/fibonacci-sequence.html

In this blog I want to demonstrate code of 30 programming languages to compute the fibonacci sequence, lets start with Pascal and maXbox:

f:=0; g:=1;
for it:= 1 to 30 do begin
  f:= f+g
  g:= f-g
end;
writeln(itoa(f));
>>> 832040

Second language is SuperCollider (Smalltalk):

var f1, b1;
f1 = 0;
b1 = 1;
30.do({ arg i;
  f1 = f1 + b1;
  b1 = f1 - b1;
("" ++ (i+1) ++ " f1: " ++ f1).postln;
}));
30: 832040

Third language is Python:

f=0; g=1
for i in range(30):
  f=f+g
  g=f-g
>>> f
832040

print(“”.join(map(lambda x: chr(ord(x)^3),’Hello, world!’)))
>>> Kfool/#tlqog”

shortest cryptography as 3 is key

4. Fibonacci series program in C

#include <stdio.h>

int main()
{
  int n, first = 0, second = 1, next, c;

  printf(“Enter the number of terms\n“);
  scanf(“%d”, &n);

  printf(“First %d terms of Fibonacci series are:\n“, n);

  for (c = 0; c < n; c++)
  {
    if (c <= 1)
      next = c;
    else
    {
      next = first + second;
      first = second;
      second = next;
    }
    printf(“%d\n“, next);
  }

  return 0;
}

5. Java:

1. class FibonacciExample1{  
2. public static void main(String args[])  
3. {    
4.  int n1=0,n2=1,n3,i,count=31;    
5.  System.out.print(n1+” “+n2);//printing 0 and 1    
6.  for(i=2;i<count;++i)//loop starts from 2 because 0 and 1 are already printed    
7.  {    
8.   n3=n1+n2;    
9.   System.out.print(” “+n3);    
10.   n1=n2;    
11.   n2=n3;    
12.  }    
13. }}  

6. C++

1. #include <iostream>  
2. using namespace std;  
3. int main() {  
4.   int n1=0,n2=1,n3,i,number;    
5.  cout<<"Enter the number of elements: ";    
6.  cin>>number;    
7.  cout<<n1<<" "<<n2<<" "; //printing 0 and 1    
8.  for(i=2;i<number;++i) //loop starts from 2, 0 and 1 printed    
9.  {    
10.    n3=n1+n2;    
11.    cout<<n3<<" ";    
12.    n1=n2;    
13.    n2=n3;    
14.  }    
15.    return 0;  
16.    }  

7. C#

1. using System;  
2.   public class FibonacciExample  
3.    {  
4.      public static void Main(string[] args)  
5.       {  
6.          int n1=0,n2=1,n3,i,number;    
7.          Console.Write("Enter the number of elements: ");    
8.          number = int.Parse(Console.ReadLine());  
9.          Console.Write(n1+" "+n2+" "); //printing 0 and 1    
10.          for(i=2;i<number;++i)   
11.          {    
12.           n3=n1+n2;    
13.           Console.Write(n3+" ");    
14.           n1=n2;    
15.           n2=n3;    
16.          }    
17.       }  
18.    }  

8. PHP

1. <?php  
2. $num = 0;  
3. $n1 = 0;  
4. $n2 = 1;  
5. echo "<h3>Fibonacci series for first 30 numbers: </h3>";  
6. echo "\n";  
7. echo $n1.' '.$n2.' ';  
8. while ($num < 28 )  
9. {  
10.     $n3 = $n2 + $n1;  
11.     echo $n3.' ';  
12.     $n1 = $n2;  
13.     $n2 = $n3;  
14.     $num = $num + 1;  
15. ?>  

9. JavaScript

while (num >= 0){
  temp = a;
     a = a + b;
     b = temp;
     num--;
  }

10. Delphi

function Fibonacci(aNumber: Integer): Integer;
begin
  if aNumber < 0 then
    raise Exception.Create('Fibonacci sequence is not defined for negative integers.');
  case aNumber of
  0: Result:= 0;
  1: Result:= 1;
  else
    Result:= Fibonacci(aNumber - 1) + Fibonacci(aNumber - 2);
  end;
end;

11. Pascal

function fibonacci4(numb: byte): integer;
var f, g, i: integer;
begin
  f:= 0; g:= 1;
  for i:= 1 to 30 do begin
    f:= f+g
    g:= f-g
  end;
 result:= f
end;

12. Pearl

#!/bin/perl -wl
#
# Prints the sequence of Fibonacci numbers with arbitrary
# precision. If an argument N is given, the program stops
# after generating Fibonacci(N).

use strict;
use bigint;

my $n = @ARGV ? shift : 1e9999;

              exit if $n < 0;
print "0: 0"; exit if $n < 1;
print "1: 1"; exit if $n < 2;

my ($a, $b) = (0, 1);
for my $k (2 .. $n) {
    ($a, $b) = ($b, $a+$b);
    print "$k: $b";
}

13. Ruby

http://www.codecodex.com/wiki/Calculate_the_Fibonacci_sequence

1. #!/usr/bin/env ruby  
2. a, b = 0, 1  
3. puts “0: #{a}”  
4. 100.times do |n|  
5.      puts “#{n+1}: #{b}”  
6.      a, b = b, a+b  
7. end  

Data Science Stuff

BASTA EKON 2020 Demo for Neural Net Visuals, locs=266

from converter import app, request
import unittest
import os

os.environ[“PATH”] += os.pathsep + ‘C:/Program Files/Pandoc/’

BASEPA = ‘C:/maXbox/maxbox3/maxbox3/maXbox3/crypt/viper2/’

class TestStrings(unittest.TestCase):
def test_upper(self):
self.assertEqual(“spam”.upper(), “SPAM”)
def test_lower(self):
self.assertEqual(“Spam”.lower(), “spam”)

class ConvertTestCase(unittest.TestCase):
def setUp(self) -> None:
app.testing = True

def test_two_paragaraps(self):
    markdown = "This is the text of paragraph 1.\n\nThis is the second text."
    with app.test_client() as test_client:
        response = test_client.post('/', data=markdown)
        tree = response.get_json()
    self.assertEqual({"type": "block-blocks", "blocks": [{
        "type": "block-paragraph",
        "spans": [{"type": "span-regular",
                   "text": "This is the text of paragraph 1."}]
    }, {
        "type": "block-paragraph",
        "spans": [{"type": "span-regular",
                   "text": "This is the second text."}]
    }]}, tree)

class RequestContextTestCase(unittest.TestCase):
def setUp(self) -> None:
app.testing = True

def test_request_context_multiline_text(self):
    markdown = "This is the text of paragraph 1.\n\nThis is the second text."
    with app.test_request_context('/', method='POST', data=markdown):
        self.assertEqual(request.get_data(as_text=True), markdown)

import random
import math
import numpy as np
import matplotlib.pyplot as plt
from sklearn.svm import SVR
from sklearn.metrics import mean_squared_error

Next, we create sample data.

Datarange = 100

random.seed(12345)
def getData(N):
x,y = [],[]
for i in range(N):
a = i/8+random.uniform(-1,1)
yy = math.sin(a)+3+random.uniform(-1,1)
x.append([a])
y.append([yy])
return np.array(x), np.array(y)

x,y = getData(Datarange)

model = SVR(gamma=’auto’) #=’scale’
print(model)

model.fit(x,y)
pred_y = model.predict(x)

for yo, yp in zip(y[1:15,:], pred_y[1:15]):
print(yo,yp)

“””
[2.12998819] 2.3688522493273485
[2.91907141] 3.285632204333334
[3.02825117] 2.953252316970487
[3.21241735] 3.3448365096752717
[2.84114287] 2.7413569211507602
[2.09354503] 2.629728633229279
[2.71700547] 3.092804036168382
[2.97862119] 3.2818706188759346
[3.40296856] 3.0690924469559113
[3.15686687] 3.8962639841272315
[3.95510045] 2.963577687955483
[4.06240409] 2.8227461040611517
[3.52296771] 3.623387735802008
[4.41282252] 3.8982877638029247
“””

x_ax=range(Datarange)
plt.scatter(x_ax, y, s=5, color=”blue”, label=”original”)
plt.plot(x_ax, pred_y, lw=1.5, color=”red”, label=”predicted”)
plt.title(‘high variance – be overfitted’)
plt.legend()
plt.show()

score=model.score(x,y)
print(score)

0.6066306757957185

mse =mean_squared_error(y, pred_y)
print(“Mean Squared Error:”,mse)

Mean Squared Error: 0.30499845231798917

rmse = math.sqrt(mse)
print(“Root Mean Squared Error:”, rmse)

Root Mean Squared Error: 0.5522666496521306

How to Fit Regression Data with CNN Model in Python

https://www.datatechnotes.com/2019/12/how-to-fit-regression-data-with-cnn.html

from sklearn.datasets import load_boston
from keras.models import Sequential
from keras.layers import Dense, Conv1D, Flatten
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt

boston = load_boston()
x, y = boston.data, boston.target

print(x.shape)

(506, 13)

An x data has two dimensions that are the number of rows and columns.

Here, we need to add the third dimension that will be the number of the single input row.

x = x.reshape(x.shape[0], x.shape[1], 1)
print(‘Shape with single input row ‘,x.shape)

xtrain, xtest, ytrain, ytest=train_test_split(x, y, test_size=0.15)

model = Sequential()
model.add(Conv1D(32, 2, activation=”relu”, input_shape=(13, 1)))
model.add(Flatten())
model.add(Dense(64, activation=”relu”))
model.add(Dense(1))
model.compile(loss=”mse”, optimizer=”adam”)

model.summary()

Next, we’ll fit the model with train data.

model.fit(xtrain, ytrain, batch_size=12,epochs=200, verbose=0)

ypredtrain = model.predict(xtrain)
print(“MSE Train: %.4f” % mean_squared_error(ytrain, ypredtrain))

Now we can predict the test data with the trained model.

ypred = model.predict(xtest)

21.21026409947595

print(“MSE Predicts: %.4f” % mean_squared_error(ytest, ypred))
print(‘MSE Evaluate: ‘,model.evaluate(xtest, ytest))

MSE: 19.8953

x_ax = range(len(ypred))
plt.scatter(x_ax, ytest, s=5, color=”blue”, label=”original”)
plt.plot(x_ax, ypred, lw=0.8, color=”red”, label=”predicted”)
plt.legend()
plt.show()

“””
https://medium.com/code-heroku/introduction-to-exploratory-data-analysis-eda-c0257f888676

Here is a quick overview of the things that you are going to learn in this article:

Descriptive Statistics
Grouping of Data
Handling missing values in dataset
ANOVA: Analysis of variance
Correlation

“””

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import stats
df = pd.read_csv(BASEPA+’data/automobile2.csv’) #read data from CSV file

print(df.head())
print(df.describe())

print(df[‘num-of-doors’].value_counts())

sns.boxplot(x=’num-of-cylinders’,y=’price’,data=df)
plt.show()

plt.scatter(df[‘engine-size’],df[‘price’])
plt.xlabel(‘Engine Size’)
plt.ylabel(‘Price’)
plt.show()

“””
count,bin_edges = np.histogram(df[‘peak-rpm’])
df[‘peak-rpm’].plot(kind=’hist’,xticks=bin_edges)
plt.xlabel(‘Value of peak rpm’)
plt.ylabel(‘Number of cars’)
plt.grid()
plt.show()
“””

df_temp = df[[‘num-of-doors’,’body-style’,’price’]]
df_group = df_temp.groupby([‘num-of-doors’,’body-style’],as_index=False).mean()
print(df_group)

show missing values

sns.heatmap(df.isnull())

plt.show()

ANOVA is a statistical method which is used for figuring out the relation between different groups of categorical data.

F-test score: It calculates the variation between sample group means divided by variation within sample group.

Correlation is a statistical metric for measuring to what extent different variables are interdependent.

temp_df = df[[‘make’,’price’]].groupby([‘make’])
print(stats.f_oneway(temp_df.get_group(‘audi’)[‘price’],temp_df.get_group(‘volvo’)[‘price’]))

temp_df.plot()

plt.show()

print(stats.f_oneway(temp_df.get_group(‘jaguar’)[‘price’],temp_df.get_group(‘honda’)[‘price’]))

“””
Notice that in this case, we got a very high F-Test score(around 401) with a p value around 1.05 * 10^-11
because, the variance between the average price of “jaguar” and “honda” is huge.
“””

In demography, demographic transition is a phenomenon and theory which refers to the historical shift from high birth rates and high infant death rates in societies with minimal technology, education (especially of women) and economic development, to low birth rates and low death rates in societies with advanced technology, education and economic development, as well as the stages between these two scenarios.

“””
plt.bar(df[‘make’],df[‘price’])
plt.xlabel(‘Maker’)
plt.ylabel(‘Price’)
plt.show()
“””
“””
correlation_matrix = df.corr()
sns.heatmap(correlation_matrix, annot=True)
plt.show()
“””

sns.regplot(x=’engine-size’,y=’price’,data=df)
plt.show()
sns.regplot(x=’highway-mpg’,y=’price’,data=df)
plt.show()

if name__ == ‘__main‘:
unittest.main()

ref for remote testing: https://www.javatpoint.com/selenium-python