Day #42 - Classes in python

Today it's bit more on classes in python. It is similar to C# / C++ / Java

	import pandas as pd
	import random
	class DemoClass:
	#Class members
	df = pd.DataFrame()
	a = []
	#Initialization
	def __init__(self):
	self.df = pd.DataFrame({'A':[1,2,3,4],'B':[5,6,7,8]})
	for i in range(1,56):
	self.a.append(i)
	print('Initialized values')
	print(self.df)
	print(self.a)
	#Custom methods modify
	def modify(self):
	self.df = self.df.drop('A',1)
	print('Modified df')
	print(self.df)
	#Custom methods reinitialize
	def reinitialize(self):
	print('Reinitialized')
	self.df = pd.DataFrame({1:[1,2,3,4],2:[1,2,3,4],10:[1,2,3,4]})
	self.df = self.df.drop(1,1)
	#Custom methods displaydata
	def displaydata(self):
	print('In Display Data')
	print(random.sample(self.a,15))
	print(self.df)
	#Object Creation
	x = DemoClass()
	#Methods Invocation
	x.displaydata()
	x.modify()
	x.displaydata()
	x.reinitialize()
	x.displaydata()

view raw classexample.py hosted with ❤ by GitHub

Day #41 - Machine Learning Interesting Qns

I do read through a lot of materials. Some readings are very clear and needs bookmark. Some of those questions and answers

1. How does KNN predict class label for new example ?
• Find the nearest K neighbour of example which needs to be classified. Take the major vote based on class labels of the K neighbours found
2. Classification - Map input to discrete outputs
3. Generative Model - Naive Bayes
4. Discriminative Model - SVM, Decision Trees, Neural Networks, Boosting, KNN
5. Regression - Map input to continuous outputs
6. Decision Tress - Embedded Implicit Feature Selection method
7. PCA
• Taking Data into a new space
• Number of Eigen Values = Number of original dimensions
• Pick the top k Eigen Value Vectors

       8. Linearly non-separable in normal plane. With SVM Kernal Technique we can project it in hyper plane and make it linearly separable

       
       9. Linearly Separable

Happy Learning!!!

Day #40 - Download Images from Web using Python

This post is about downloading images from a URL

• Read from the input file
• Perform recursive download for all files
• Try catch handled errors and downloaded file successfully

	import urllib.request
	#Read file tab spaced
	f = open("facescrub_actresses.txt",'r')
	readlines = f.readlines()
	i = 1
	title = "female"
	for line in readlines:
	name = title + str(i) + ".jpg"
	i = i+1
	#Download only 300 images
	if(i==300):
	break
	values = line.split('\t')
	print('Imagepath-',values[3])
	try:
	#Download Image to directory
	urllib.request.urlretrieve(values[3],name)
	except:
	print("Error for",values[3])

view raw DownloadImage.py hosted with ❤ by GitHub