shiny new

library(shiny)
ui <- fluidPage(
   titlePanel("TABLE"),
    sidebarLayout(
      sidebarPanel(
        sliderInput("num", "integer", 1, 20, 1,
                    step = 1, animate =
                      animationOptions(interval=400, loop=TRUE))),
      mainPanel(
        tableOutput("prod")
      )) )
 
server <- function(input, output) {
    output$prod <- renderPrint({ x<-input$num
    for(i in 1:10){
      a=x*i
      cat(x,"x",i,"=",a,"<br>")
       }})}

shinyApp(ui = ui, server = server)

table shiny

library(shiny)
fluidPage(
  titlePanel("Manish"),
  sidebarLayout(
    sidebarPanel(
      sliderInput("num", "integer", 1, 20, 1,
         step = 1, animate =
 animationOptions(interval=400, loop=TRUE))),
    mainPanel(
      tableOutput("prod")
    ) ))
______________________________________________
function(input, output) {
  output$prod = renderPrint({ x=input$num
  for(i in 1:10){
    a=x*i
    cat(x,"x",i,"=",a,"<br>") } }) }

Shiny Code

library(shiny)
ui<-fluidPage(
  sidebarPanel(
    sliderInput("num","integer",1,200,5,step=1,animate = animationOptions(interval = 1000,loop = TRUE))),
  radioButtons("radio",label = h1("Choose one"),choices = list("Table"=1,"Even/Odd"=2,"Fact"=3),selected = 1),
  hr(),
  mainPanel(
    tableOutput("abc")
  )
)


server<-function(input,output)
{
 
    output$abc<-renderPrint({
      r<-input$radio
      x<-input$num
      if(r==1){
        for(i in 1:10)
        {
          cat(x,"X",i,"=",x*i,"<br>")
        }
      }else if(r==2)
      {
        if(x%%2==0)
        {
          print("Even")
        }else{
          print("Odd")
        }
      }
      else{
        print(factorial(x))
      }
    })
   
}   
   
shinyApp(ui=ui,server=server)

R Softwares

R      - https://cran.r-project.org/bin/windows/base/
RStudio   - https://www.rstudio.com/products/rstudio/download/

OCR -AI -ML

https://digi.bib.uni-mannheim.de/tesseract/tesseract-ocr-setup-3.05.02-20180621.exe
________________________________________________________________________
create path variable:
TESSDATA_PREFIX=C:\Program Files (x86)\Tesseract-OCR


_______________________________________________________________________
import os
import tempfile
import subprocess
import pytesseract
pytesseract.pytesseract.tesseract_cmd = 'C:/Program Files (x86)/Tesseract-OCR/tesseract'
def ocr(path):
    temp = tempfile.NamedTemporaryFile(delete=False)

    process = subprocess.Popen(['tesseract', path, temp.name], stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
    process.communicate()

    with open(temp.name + '.txt', 'r') as handle:
        contents = handle.read()

    os.remove(temp.name + '.txt')

    #os.remove(temp.name)
    

    return contents


str = ocr(r'C:\Users\Freeware Sys\Downloads\alpha.jpg')
print(str)

MR - Classification

word_list = ['Emma','Woodhouse','father','Taylor','Miss','been','she','her']
    #i'm using this example text in place of the file you are using
text = 'This is an example text. It will contain words you are looking for, like Emma, Emma, Emma, Woodhouse, Woodhouse, Father, Father, Taylor,Miss,been,she,her,her,her. I made them repeat to show that the code works.'
text = text.replace(',',' ') #these statements remove irrelevant punctuation
text = text.replace('.','')
text = text.lower() #this makes all the words lowercase, so that capitalization wont affect the frequency measurement
for repeatedword in word_list:
        counter = 0 #counter starts at 0
        for word in text.split():
            if repeatedword.lower() == word:
                counter = counter + 1 #add 1 every time there is a match in the list
        print(repeatedword,':', counter) #prints the word from 'word_list' and its frequency

ML - ANN

from numpy import exp, array, random, dot

training_set_inputs = array([[0, 0, 1], [1, 1, 1], [1, 0, 1], [0, 1, 1]])

training_set_outputs = array([[0, 1, 1, 0]]).T

random.seed(1)

synaptic_weights = 2 * random.random((3, 1)) - 1

for iteration in range(10000):

    output = 1 / (1 + exp(-(dot(training_set_inputs, synaptic_weights))))

    synaptic_weights += dot(training_set_inputs.T, (training_set_outputs - output) * output * (1 - output))

print 1 / (1 + exp(-(dot(array([1, 0, 0]), synaptic_weights))))

ML - Decision Tree - Classification

install pydotplus and graphviz

Graphviz is a tool for drawing graphics using dot files. Pydotplus is a module to Graphviz’s Dot language.

import pydotplus

from sklearn.datasets import load_iris

from sklearn import tree

import collections

# Data Collection

X = [ [180, 15,0],    

      [177, 42,0],

      [136, 35,1],

      [174, 65,0],

      [141, 28,1]]

Y = ['man', 'woman', 'woman', 'man', 'woman']    

data_feature_names = [ 'height', 'hair length', 'voice pitch' ]

# Training

clf = tree.DecisionTreeClassifier()

clf = clf.fit(X,Y)

# Visualize data

dot_data = tree.export_graphviz(clf,

                                feature_names=data_feature_names,

                                out_file=None,

                                filled=True,

                                rounded=True)

graph = pydotplus.graph_from_dot_data(dot_data)

colors = ('turquoise', 'orange')

edges = collections.defaultdict(list)

for edge in graph.get_edge_list():

    edges[edge.get_source()].append(int(edge.get_destination()))

for edge in edges:

    edges[edge].sort()    

    for i in range(2):

        dest = graph.get_node(str(edges[edge][i]))[0]

        dest.set_fillcolor(colors[i])

graph.write_png('tree.png')