arshren

from sklearn import datasets
from sklearn.semi_supervised import LabelPropagation
from sklearn.metrics import confusion_matrix, classification_report
import numpy as np

rnd = np.random.RandomState(42)
# load the cancer dataset
cancer = datasets.load_breast_cancer()

# Randomly unlabel some records in the dataset

arshren

#Import required libraries
import tensorflow as tf
from keras.datasets import mnist

#Creating the MNIST Dataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test= x_train/255.0, x_test/255.0
x_train=x_train[...,tf.newaxis].astype('float32')
x_test=x_test[...,tf.newaxis].astype('float32')

arshren

#Import required libraries
import tensorflow as tf
from keras.datasets import mnist

#Creating the MNIST Dataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test= x_train/255.0, x_test/255.0
x_train=x_train[...,tf.newaxis].astype('float32')
x_test=x_test[...,tf.newaxis].astype('float32')

arshren

#Import required libraries
import tensorflow as tf
from keras.datasets import mnist

#Creating the MNIST Dataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test= x_train/255.0, x_test/255.0
x_train=x_train[...,tf.newaxis].astype('float32')
x_test=x_test[...,tf.newaxis].astype('float32')

arshren

---
title: "Titanic  Data Analysis"
output: html_document
author: Authored by Renu
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Data Analysis of Titanic dataset using R Markdown

arshren

import numpy as np
import cv2
frame_count=0
# Open the Video
video = cv2. VideoCapture('people_motion.avi')

# read the fiurst frame  of the video as the initial background image
ret, Prev_frame= video.read()

arshren

# importing libraries
from PIL import Image
import numpy as np
import cv2
import math

# setting variables
tracking_people={}
person_id=0
frame_num = 0

arshren

# import the necessary packages
from scipy.spatial import distance as dist
import numpy as np
import imutils
from imutils import contours
from imutils import perspective
import cv2

# detect aruco marker
def findArucoMarkers(img, markerSize = 6, totalMarkers=100, draw=True):

arshren

# Import libararies
import numpy as np
import pandas as pd
import os

import torch_geometric.transforms as T
from torch_geometric.datasets import Planetoid
import matplotlib.pyplot as plt
import torch
import torch.nn.functional as F

arshren

# Fibonaci of 5 using recursion
def calc_fibonaci(n):
    if n<=1:
        return n
    return calc_fibonaci(n-1) + calc_fibonaci(n-2)
print(calc_fibonaci(6))

# Memoization
def calc_fibonaci(n):
    cache=[-1 for x in range(n+1)]