Sunday, 4 November 2018

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')

Friday, 2 November 2018

Private Cloud

https://drive.google.com/open?id=1yvF6w_SOHYKXyxrHQ1Fgd1tQY5HxCQyg

Blockchain with R

block_example <- list(index = 1,
                     timestamp = "2018-11-02 08.32 IST",
                     data = "Infoway",
                     previous_hash = 0,
                     proof = 9,
                     new_hash = NULL)
_________________________________________________________________
library("digest")
digest("Manish" ,"sha256")
________________________________________________________________

    hash_block <- function(block){
      block$new_hash <- digest(c(block$index,
                                 block$timestamp,
                                 block$data,
                                 block$previous_hash), "sha256")
      return(block)
    }
____________________________________________________________
      # Increment the proof number until a number is found that is divisable by 99 and by the proof of the previous block
   
proof_of_work <- function(last_proof){
      proof <- last_proof + 1
      while (!(proof %% 99 == 0 & proof %% last_proof == 0 )){
        proof <- proof + 1}
 return(proof) }
_________________________________________________________________
    gen_new_block <- function(previous_block){
      new_proof <- proof_of_work(previous_block$proof)
      new_block <- list(index = previous_block$index + 1,
                        timestamp = Sys.time(),
                        data = paste0("this is block ", previous_block$index +1),
                        previous_hash = previous_block$new_hash,
                        proof = new_proof)
      new_block_hashed <- hash_block(new_block)
      return(new_block_hashed) }
_____________________________________________________________________

    block_genesis <-  list(index = 1,
                           timestamp = Sys.time(),
                           data = "Genesis Block",
                           previous_hash = "0",
                           proof = 1)
_____________________________________________________________________
blockchain <- list(block_genesis)
    previous_block <- blockchain[[1]]

      # How many blocks should we add to the chain after the genesis block
      num_of_blocks_to_add <- 5

      # Add blocks to the chain
      for (i in 1: num_of_blocks_to_add){
        block_to_add <- gen_new_block(previous_block)
        blockchain[i+1] <- list(block_to_add)
        previous_block <- block_to_add

        print(cat(paste0("Block ", block_to_add$index, " has been added", "\n",
                   "\t", "Proof: ", block_to_add$proof, "\n",
                   "\t", "Hash: ", block_to_add$new_hash))) }





















Tuesday, 30 October 2018

Assignment

Web Log Dataset

https://drive.google.com/open?id=1eIJmFifhOXbvBlRmQfDoCatXFmnoH-Dp

Aadhar DataSet

https://drive.google.com/open?id=1BB3c3UWOxezQXXi8MUdRs0hRtSGtmVVY

Aadhar Questions

https://drive.google.com/open?id=1d5eGxGTC07c5Kthm6RVNw3ieZBYavY47


IIT AI workshop DataSet

https://drive.google.com/open?id=1oD1mFi4zmqz5xGuCJ7_OdUKZgW5fekct


https://drive.google.com/file/d/1M4sGfss3DLYosCxWZoai2i9X1AMZoolb/view?usp=sharing

R Setup



https://cran.r-project.org/bin/windows/base/


https://www.rstudio.com/products/rstudio/download/

Monday, 29 October 2018

Selenium Testing

https://drive.google.com/drive/folders/1h1DiwsGuyPl2OEwchXzX5ZF9tdiCRx1y?usp=sharing