dic = {}
dic['one'] = 'This is one'
dic[2] = 'This is two'
print(dic['one'])
print(dic)
dic['one'] = 'One has changed'
print(dic)This is one
{'one': 'This is one', 2: 'This is two'}
{'one': 'One has changed', 2: 'This is two'}
l = [1, 2, 3, 4, 5, 6, 7, 8]
s = "This is a string."
print(l[1:5])
print(s[0:6])[2, 3, 4, 5]
This i
trip1 = 15
if trip1 <= 25:
print('25 and Under')
else:
print("25 and Over")25 and Under
stat1 = True
stat2 = False
if stat1:
print("1st stat is true")
elif stat2:
print("2nd stat true")
else:
print("Both are false")1st stat is true
exampleList = [1, 2, 3, 4, 5]
for i in exampleList:
print(i)1
2
3
4
5
#String with dynamic object
x=list(range(2,6))
print("Initial List: {}".format(x))
for idx, i in enumerate(x):
x[idx]= i**2
print("The new list:{}".format(x))
#During each step of the for loop, enumerate(x)iterates through the list
#and store the index in [idx] and value in [i].Initial List: [2, 3, 4, 5]
The new list:[4, 9, 16, 25]
newList = [x**2 for x in range (2, 6)]
print(newList)[4, 9, 16, 25]
i = 0
while i < 5:
print (i, end=" ") # prints each iteration on the same line
i += 1 # adds 1 to the variable i
print() # prints a blank line
print("done") # Note that this is printed outside the loop0 1 2 3 4
done
y = range(1, 51)
for i in y:
if i%3==0 and i%2!=0:
print(i)3
9
15
21
27
33
39
45
def func1(s):
"Some stuff"
print("Number of characters in the string: ", len(s))
return 2*len(s)func1("test function")Number of characters in the string: 13
26
def powers(x):
xs = x**2
xc = x**3
xf = x**4
return xs, xc, xfpowers(5)(25, 125, 625)
y1, y2, y3 = powers(5)
print(y2)125
Anonymous functions are defined by the keyword lambda in Python. Functions f and g in the cell below basically do the same thing. But g is an anonymous function.
# define a function
def f(x):
return x**2. # x to the power of 2 - the function give us x squared
# use an anonymous function instead
g = lambda x: x**2. # x to the power of 2 - in other words x squared
print(f(8)) # call the f function and ask for the square of 8
print(g(8)) # call the g anonymous function and ask for the square of 8In the cell below, we used anonymous function n_increment(). We create new functions by passing n to n_incremenet(). For example f5 and f9 are functions that add 5 and 9 to their inputs respectively.
def n_increment(n):
return lambda x: x+n
add5 = n_increment(5)
print(add5(2))7
from math import*
def square_rooted(x):
return round(sqrt(sum([a*a for a in x])),3)
def cosine_similarity(x,y):
numerator = sum(a*b for a,b in zip(x,y))
denominator = square_rooted(x)*square_rooted(y)
return round(numerator/float(denominator),3)
print (cosine_similarity([1,1,1], [3,3,3]))1.0
from math import*
def jaccard_similarity(x,y):
intersection_cardinality = len(set.intersection(*[set(x), set(y)]))
union_cardinality = len(set.union(*[set(x), set(y)]))
return intersection_cardinality/float(union_cardinality)
print (jaccard_similarity(["banana","orange","grapes"],["apple", "banana", "grapes", "pear"]))0.4
from math import *v1 = [1,1,1]
v2 = [3,3,3]
v3 = [3,0,2,6]def eclu_dist(x,y):
return sqrt(sum(pow(a-b, 2) for a,b in zip(x,y)))print(eclu_dist(v1,v2))3.4641016151377544
def man_dist(x,y):
return sum(abs(a-b) for a,b in zip(x,y))print(man_dist(v1,v2))
print(man_dist(v1,v3))7
10
c1 = [1,2]
c2 = [4,3]
c3 = [4,1]pt = [2,2]print(man_dist(pt, c1))
print(man_dist(pt, c2))
print(man_dist(pt, c3))1
3
3
import numpy as npa = np.random.randn(5,1)
print(a.shape)
print(a)(5, 1)
[[ 0.02065934]
[-0.63937074]
[-0.20289277]
[-0.26456445]
[ 0.70719702]]
a_trans = a.T
print(a_trans.shape)
print(a_trans)(1, 5)
[[ 0.02065934 -0.63937074 -0.20289277 -0.26456445 0.70719702]]
a_dot = np.dot(a, a_trans)
print(a_dot.shape)
print(a_dot)(5, 5)
[[ 4.26808391e-04 -1.32089784e-02 -4.19163093e-03 -5.46572735e-03
1.46102247e-02]
[-1.32089784e-02 4.08794938e-01 1.29723697e-01 1.69154768e-01
-4.52161079e-01]
[-4.19163093e-03 1.29723697e-01 4.11654743e-02 5.36782133e-02
-1.43485159e-01]
[-5.46572735e-03 1.69154768e-01 5.36782133e-02 6.99943491e-02
-1.87099191e-01]
[ 1.46102247e-02 -4.52161079e-01 -1.43485159e-01 -1.87099191e-01
5.00127623e-01]]
##Using Pip Install
import sys
!{sys.executable} -m pip install tensorflowRequirement already satisfied: tensorflow in ./anaconda2/envs/ML/lib/python3.7/site-packages (1.14.0)
Requirement already satisfied: astor>=0.6.0 in ./anaconda2/envs/ML/lib/python3.7/site-packages (from tensorflow) (0.8.0)
Requirement already satisfied: keras-preprocessing>=1.0.5 in ./anaconda2/envs/ML/lib/python3.7/site-packages (from tensorflow) (1.1.0)
Requirement already satisfied: termcolor>=1.1.0 in ./anaconda2/envs/ML/lib/python3.7/site-packages (from tensorflow) (1.1.0)
Requirement already satisfied: keras-applications>=1.0.6 in ./anaconda2/envs/ML/lib/python3.7/site-packages (from tensorflow) (1.0.8)
Requirement already satisfied: tensorboard<1.15.0,>=1.14.0 in ./anaconda2/envs/ML/lib/python3.7/site-packages (from tensorflow) (1.14.0)
Requirement already satisfied: tensorflow-estimator<1.15.0rc0,>=1.14.0rc0 in ./anaconda2/envs/ML/lib/python3.7/site-packages (from tensorflow) (1.14.0)
Requirement already satisfied: absl-py>=0.7.0 in ./anaconda2/envs/ML/lib/python3.7/site-packages (from tensorflow) (0.7.1)
Requirement already satisfied: google-pasta>=0.1.6 in ./anaconda2/envs/ML/lib/python3.7/site-packages (from tensorflow) (0.1.7)
Requirement already satisfied: six>=1.10.0 in ./anaconda2/envs/ML/lib/python3.7/site-packages (from tensorflow) (1.12.0)
Requirement already satisfied: wheel>=0.26 in ./anaconda2/envs/ML/lib/python3.7/site-packages (from tensorflow) (0.33.4)
Requirement already satisfied: protobuf>=3.6.1 in ./anaconda2/envs/ML/lib/python3.7/site-packages (from tensorflow) (3.8.0)
Requirement already satisfied: numpy<2.0,>=1.14.5 in ./anaconda2/envs/ML/lib/python3.7/site-packages (from tensorflow) (1.16.4)
Requirement already satisfied: wrapt>=1.11.1 in ./anaconda2/envs/ML/lib/python3.7/site-packages (from tensorflow) (1.11.2)
Requirement already satisfied: grpcio>=1.8.6 in ./anaconda2/envs/ML/lib/python3.7/site-packages (from tensorflow) (1.22.0)
Requirement already satisfied: gast>=0.2.0 in ./anaconda2/envs/ML/lib/python3.7/site-packages (from tensorflow) (0.2.2)
Requirement already satisfied: h5py in ./anaconda2/envs/ML/lib/python3.7/site-packages (from keras-applications>=1.0.6->tensorflow) (2.9.0)
Requirement already satisfied: setuptools>=41.0.0 in ./anaconda2/envs/ML/lib/python3.7/site-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow) (41.0.1)
Requirement already satisfied: werkzeug>=0.11.15 in ./anaconda2/envs/ML/lib/python3.7/site-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow) (0.15.4)
Requirement already satisfied: markdown>=2.6.8 in ./anaconda2/envs/ML/lib/python3.7/site-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow) (3.1.1)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pdCreate to arrays X and Y
x = np.array([1,2,3,4])
y = np.array([10,12,33,56])
xx = np.array([1.5,2.5,3.5])
yy = np.array([10.5, 15.5, 20])plot x and y
plt.plot(x,y, '*r') # The plt.plot function takes the argument for plot type *r = red stars
plt.show() # By having a plt.show() for each plot this will create 2 seperate plots
plt.plot(xx,yy, '.b') # This will overlay the current plot with blue dots (.b)
plt.show() # This line shows the plot
A = np.array([(1,2), (3,4)])
print(A)[[1 2]
[3 4]]
An all zero matrix
B = np.zeros([3,3])
print(B)[[0. 0. 0.]
[0. 0. 0.]
[0. 0. 0.]]
All 1 matrix
C = np.ones([3,3])
print(C)[[1. 1. 1.]
[1. 1. 1.]
[1. 1. 1.]]
Identity Matrix
D = np.identity(3)
print(D)[[1. 0. 0.]
[0. 1. 0.]
[0. 0. 1.]]
Matrix of random numbers
E = np.random.randn(4,3)
print(E)[[-1.99030373 -1.13753217 0.57406588]
[-0.53403094 0.68025265 0.55211424]
[ 0.12718483 0.62083608 -1.46039388]
[-1.08696216 -0.43184155 0.24903507]]
print(A)
print()
print("After addition of a scalar: ")
print(A+3)[[1 2]
[3 4]]
After addition of a scalar:
[[4 5]
[6 7]]
aa = np.identity(2)
bb = np.random.randn(2,2)
print("Matrix AA")
print(aa)
print("Matrix BB")
print(bb)Matrix AA
[[1. 0.]
[0. 1.]]
Matrix BB
[[-0.90370294 -0.3111972 ]
[-0.35184552 0.19191158]]
Lets add aa and bb together
result = aa + bb
print(result)[[ 0.09629706 -0.3111972 ]
[-0.35184552 1.19191158]]
ac = np.random.randn(3,3)
ca = np.random.randn(3,2)print(np.shape(ac))
print(np.shape(ca))(3, 3)
(3, 2)
print(ac.dot(ca))
print("+++++++++++++++++++++++++++++++++++++++")
print(np.dot(ac,ca))[[-3.32449661 3.56454976]
[ 1.48532252 -2.68407261]
[-2.17719238 1.78806864]]
+++++++++++++++++++++++++++++++++++++++
[[-3.32449661 3.56454976]
[ 1.48532252 -2.68407261]
[-2.17719238 1.78806864]]
The otherway around does not work as the coulmns of the first is not equal to the rows of the second
print(np.dot(ca,ac))---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-161-c54724c0e8ab> in <module>
----> 1 print(np.dot(ca,ac))
ValueError: shapes (3,2) and (3,3) not aligned: 2 (dim 1) != 3 (dim 0)
cc = np.random.randn(3,3)
cc_inverse = np.linalg.inv(cc)
print("This is the original matrix:")
print(cc)
print("This is it's inverse:")
print(cc_inverse)This is the original matrix:
[[-0.14424467 -0.82311042 0.17631817]
[ 0.10232438 0.24796583 -0.33836574]
[ 0.72756318 -0.40811641 -0.39661549]]
This is it's inverse:
[[-1.44027345 -2.42695693 1.43023331]
[-1.25240731 -0.43294114 -0.18741003]
[-1.35335603 -4.00658613 0.29517306]]
Now let's check if the condition holds up:
print(np.dot(cc, cc_inverse)) #should produce an identity matrix
print("Which is also identical to: ")
print(np.dot(cc_inverse, cc))[[ 1.00000000e+00 -1.43583071e-16 -5.15807765e-17]
[ 2.81786443e-17 1.00000000e+00 1.30809612e-17]
[-1.51406610e-16 1.51720120e-16 1.00000000e+00]]
Which is also identical to:
[[ 1.00000000e+00 -1.07924509e-17 -1.82598655e-16]
[ 3.29573910e-17 1.00000000e+00 -2.04147114e-17]
[ 1.87792607e-17 1.54536858e-17 1.00000000e+00]]
AA = np.arange(6).reshape(3,2)
BB = np.arange(8).reshape(2,4)
print(AA)
print("===========")
print(BB)[[0 1]
[2 3]
[4 5]]
===========
[[0 1 2 3]
[4 5 6 7]]
Transpose of A
print(AA.T)[[0 2 4]
[1 3 5]]
A note: Let matrix A be of dimension n×m and let B be of dimension m×p. Then (AB)′=B′A′
print(np.dot(AA,BB).T)[[ 4 12 20]
[ 5 17 29]
[ 6 22 38]
[ 7 27 47]]
print(np.dot(BB.T, AA.T))[[ 4 12 20]
[ 5 17 29]
[ 6 22 38]
[ 7 27 47]]
print(A)
print("This is the first column of the matrix A: ")
print(A[:,0])[[1 2]
[3 4]]
This is the first column of the matrix A:
[1 3]
print(A[-1,1])4
Using logical checks to extract values from matrices:
#give the element in the last column that is greater than 3
print(A[:,1]>3)[False True]
Create a 12×2 matrix and print it out:
A = np.arange(24).reshape(12,2)
print(A)[[ 0 1]
[ 2 3]
[ 4 5]
[ 6 7]
[ 8 9]
[10 11]
[12 13]
[14 15]
[16 17]
[18 19]
[20 21]
[22 23]]
for i in A:
print(i)[0 1]
[2 3]
[4 5]
[6 7]
[8 9]
[10 11]
[12 13]
[14 15]
[16 17]
[18 19]
[20 21]
[22 23]
for j in A.T:
print(j)[ 0 2 4 6 8 10 12 14 16 18 20 22]
[ 1 3 5 7 9 11 13 15 17 19 21 23]
import numpy as np
from scipy.optimize import fmin
import math## Define the function
def f(x):
val = math.pow(x,2) +1
return val
funMin = fmin(f,np.random.randn(1,1))
print(funMin)Optimization terminated successfully.
Current function value: 1.000000
Iterations: 18
Function evaluations: 36
[1.33226763e-15]
import sys
!{sys.executable} -m pip install -U scikit-learnRequirement already up-to-date: scikit-learn in ./anaconda2/envs/ML/lib/python3.7/site-packages (0.21.2)
Requirement already satisfied, skipping upgrade: joblib>=0.11 in ./anaconda2/envs/ML/lib/python3.7/site-packages (from scikit-learn) (0.13.2)
Requirement already satisfied, skipping upgrade: numpy>=1.11.0 in ./anaconda2/envs/ML/lib/python3.7/site-packages (from scikit-learn) (1.16.4)
Requirement already satisfied, skipping upgrade: scipy>=0.17.0 in ./anaconda2/envs/ML/lib/python3.7/site-packages (from scikit-learn) (1.3.0)
from sklearn import datasetsdigits = datasets.load_digits()
print(digits){'data': array([[ 0., 0., 5., ..., 0., 0., 0.],
[ 0., 0., 0., ..., 10., 0., 0.],
[ 0., 0., 0., ..., 16., 9., 0.],
...,
[ 0., 0., 1., ..., 6., 0., 0.],
[ 0., 0., 2., ..., 12., 0., 0.],
[ 0., 0., 10., ..., 12., 1., 0.]]), 'target': array([0, 1, 2, ..., 8, 9, 8]), 'target_names': array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), 'images': array([[[ 0., 0., 5., ..., 1., 0., 0.],
[ 0., 0., 13., ..., 15., 5., 0.],
[ 0., 3., 15., ..., 11., 8., 0.],
...,
[ 0., 4., 11., ..., 12., 7., 0.],
[ 0., 2., 14., ..., 12., 0., 0.],
[ 0., 0., 6., ..., 0., 0., 0.]],
[[ 0., 0., 0., ..., 5., 0., 0.],
[ 0., 0., 0., ..., 9., 0., 0.],
[ 0., 0., 3., ..., 6., 0., 0.],
...,
[ 0., 0., 1., ..., 6., 0., 0.],
[ 0., 0., 1., ..., 6., 0., 0.],
[ 0., 0., 0., ..., 10., 0., 0.]],
[[ 0., 0., 0., ..., 12., 0., 0.],
[ 0., 0., 3., ..., 14., 0., 0.],
[ 0., 0., 8., ..., 16., 0., 0.],
...,
[ 0., 9., 16., ..., 0., 0., 0.],
[ 0., 3., 13., ..., 11., 5., 0.],
[ 0., 0., 0., ..., 16., 9., 0.]],
...,
[[ 0., 0., 1., ..., 1., 0., 0.],
[ 0., 0., 13., ..., 2., 1., 0.],
[ 0., 0., 16., ..., 16., 5., 0.],
...,
[ 0., 0., 16., ..., 15., 0., 0.],
[ 0., 0., 15., ..., 16., 0., 0.],
[ 0., 0., 2., ..., 6., 0., 0.]],
[[ 0., 0., 2., ..., 0., 0., 0.],
[ 0., 0., 14., ..., 15., 1., 0.],
[ 0., 4., 16., ..., 16., 7., 0.],
...,
[ 0., 0., 0., ..., 16., 2., 0.],
[ 0., 0., 4., ..., 16., 2., 0.],
[ 0., 0., 5., ..., 12., 0., 0.]],
[[ 0., 0., 10., ..., 1., 0., 0.],
[ 0., 2., 16., ..., 1., 0., 0.],
[ 0., 0., 15., ..., 15., 0., 0.],
...,
[ 0., 4., 16., ..., 16., 6., 0.],
[ 0., 8., 16., ..., 16., 8., 0.],
[ 0., 1., 8., ..., 12., 1., 0.]]]), 'DESCR': ".. _digits_dataset:\n\nOptical recognition of handwritten digits dataset\n--------------------------------------------------\n\n**Data Set Characteristics:**\n\n :Number of Instances: 5620\n :Number of Attributes: 64\n :Attribute Information: 8x8 image of integer pixels in the range 0..16.\n :Missing Attribute Values: None\n :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\n :Date: July; 1998\n\nThis is a copy of the test set of the UCI ML hand-written digits datasets\nhttps://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\n\nThe data set contains images of hand-written digits: 10 classes where\neach class refers to a digit.\n\nPreprocessing programs made available by NIST were used to extract\nnormalized bitmaps of handwritten digits from a preprinted form. From a\ntotal of 43 people, 30 contributed to the training set and different 13\nto the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\n4x4 and the number of on pixels are counted in each block. This generates\nan input matrix of 8x8 where each element is an integer in the range\n0..16. This reduces dimensionality and gives invariance to small\ndistortions.\n\nFor info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\nT. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\nL. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\n1994.\n\n.. topic:: References\n\n - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\n Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\n Graduate Studies in Science and Engineering, Bogazici University.\n - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\n - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\n Linear dimensionalityreduction using relevance weighted LDA. School of\n Electrical and Electronic Engineering Nanyang Technological University.\n 2005.\n - Claudio Gentile. A New Approximate Maximal Margin Classification\n Algorithm. NIPS. 2000."}
print(digits.target)[0 1 2 ... 8 9 8]
Linear regression using SciKit-learn
from sklearn.linear_model import LinearRegression
import numpy as np
#Generate training data
X = np.random.rand(100, 1)
Y = np.exp(X)
#Create linear model
linearModel = LinearRegression()
#Fit linear model to training data
linearModel.fit(X,Y)
#Generate test data
X_test = np.random.rand(1000,1)
Y_test = linearModel.predict(X_test)
plt.plot(X_test,Y_test, ".r")
plt.plot(X,Y, ".b")
plt.show()
Creating the document
corpus = [
'This is the first document.',
'This is the second second document.',
'And the third one.',
'Is this the first document?',
]
print ("The corpus is the list: {}".format(corpus))The corpus is the list: ['This is the first document.', 'This is the second second document.', 'And the third one.', 'Is this the first document?']
Using CountVectorizer in SciKit we can implement tozenisation and occurrence counting in a single class:
Load module for sklearn:
from sklearn.feature_extraction.text import CountVectorizerInitiate module:
vectoriser = CountVectorizer()
vectoriserCountVectorizer(analyzer='word', binary=False, decode_error='strict',
dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',
lowercase=True, max_df=1.0, max_features=None, min_df=1,
ngram_range=(1, 1), preprocessor=None, stop_words=None,
strip_accents=None, token_pattern='(?u)\\b\\w\\w+\\b',
tokenizer=None, vocabulary=None)
Create the term freq matrix:
termFreq = vectoriser.fit_transform(corpus)
vectoriser.get_feature_names()['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this']
Now we can transform the term freq output into an array:
termFreq.toarray()array([[0, 1, 1, 1, 0, 0, 1, 0, 1],
[0, 1, 0, 1, 0, 2, 1, 0, 1],
[1, 0, 0, 0, 1, 0, 1, 1, 0],
[0, 1, 1, 1, 0, 0, 1, 0, 1]])
To do a TF-IDF transformation, we use TfidfTransformer:
from sklearn.feature_extraction.text import TfidfVectorizer
TFvector = TfidfVectorizer()
TFvectorTfidfVectorizer(analyzer='word', binary=False, decode_error='strict',
dtype=<class 'numpy.float64'>, encoding='utf-8',
input='content', lowercase=True, max_df=1.0, max_features=None,
min_df=1, ngram_range=(1, 1), norm='l2', preprocessor=None,
smooth_idf=True, stop_words=None, strip_accents=None,
sublinear_tf=False, token_pattern='(?u)\\b\\w\\w+\\b',
tokenizer=None, use_idf=True, vocabulary=None)
#Apply to corpus:
tfVectorisation = TFvector.fit_transform(corpus)
tfVectorisation.toarray()array([[0. , 0.43877674, 0.54197657, 0.43877674, 0. ,
0. , 0.35872874, 0. , 0.43877674],
[0. , 0.27230147, 0. , 0.27230147, 0. ,
0.85322574, 0.22262429, 0. , 0.27230147],
[0.55280532, 0. , 0. , 0. , 0.55280532,
0. , 0.28847675, 0.55280532, 0. ],
[0. , 0.43877674, 0.54197657, 0.43877674, 0. ,
0. , 0.35872874, 0. , 0.43877674]])
By default, the tf-idf vectorization returns a sparse matrix. We can see the output by converting it to a dense matrix with:
print(vectoriser.vocabulary_)
tfVectorisation.todense(){'this': 8, 'is': 3, 'the': 6, 'first': 2, 'document': 1, 'second': 5, 'and': 0, 'third': 7, 'one': 4}
matrix([[0. , 0.43877674, 0.54197657, 0.43877674, 0. ,
0. , 0.35872874, 0. , 0.43877674],
[0. , 0.27230147, 0. , 0.27230147, 0. ,
0.85322574, 0.22262429, 0. , 0.27230147],
[0.55280532, 0. , 0. , 0. , 0.55280532,
0. , 0.28847675, 0.55280532, 0. ],
[0. , 0.43877674, 0.54197657, 0.43877674, 0. ,
0. , 0.35872874, 0. , 0.43877674]])
import sys
!{sys.executable} -m pip install wordcloudCollecting wordcloud
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�[?25hRequirement already satisfied: numpy>=1.6.1 in /Users/ragyibrahim/anaconda2/envs/ML/lib/python3.7/site-packages (from wordcloud) (1.16.4)
Collecting pillow (from wordcloud)
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�[?25hInstalling collected packages: pillow, wordcloud
Successfully installed pillow-6.1.0 wordcloud-1.5.0
from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
SOME_TEXT = "A tag cloud is a visual representation for text data, typically\
used to depict keyword metadata on websites, or to visualize free form text. Some more text and tag."
wordcloud = WordCloud(stopwords = STOPWORDS, background_color= 'white', width = 1200,
height = 1000).generate_from_text(SOME_TEXT)
print(wordcloud.words_)
fig = plt.figure()
plt.imshow(wordcloud)
plt.show(){'text': 1.0, 'tag': 0.6666666666666666, 'cloud': 0.3333333333333333, 'visual': 0.3333333333333333, 'representation': 0.3333333333333333, 'data': 0.3333333333333333, 'typicallyused': 0.3333333333333333, 'depict': 0.3333333333333333, 'keyword': 0.3333333333333333, 'metadata': 0.3333333333333333, 'websites': 0.3333333333333333, 'visualize': 0.3333333333333333, 'free': 0.3333333333333333, 'form': 0.3333333333333333}
Load required mdoules
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeansCreate sample dataset
x = [1,5,1.5,8,1,9]
y=[2,8,1.8,8,0.6,11]Plot data
plt.scatter(x,y)
plt.show()
Now lets create a matrix X with x,y coordinates
X = np.array([
[1,2],
[5,8],
[1.5, 1.8],
[8,8],
[1,0.6],
[9,11]
])Initiate K-Means algorithm with 2 clusters
kmeans_1 = KMeans(n_clusters=2)
kmeans_1.fit(X)KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=2, n_init=10, n_jobs=None, precompute_distances='auto',
random_state=None, tol=0.0001, verbose=0)
Now, we have fit the KMeans model to our data, X. The model will have identified 2 clusters, with 2 cluster centres (centroids). we can get this data as:
centroids = kmeans_1.cluster_centers_
labels = kmeans_1.labels_
print(centroids)
print(labels)[[1.16666667 1.46666667]
[7.33333333 9. ]]
[0 1 0 1 0 1]
Lets try to visualise the clusters by plotting them. The centroids will be marked as “X”
colors = ['g.', 'r.', 'c.', 'y.']
for i in range(len(X)):
print("coordinates: ", X[i], "label: ", labels[i])
plt.plot(X[i][0], X[i][1], colors[labels[i]], markersize = 10)
#Visualise the centroids
plt.scatter(centroids[:, 0], centroids[:, 1], marker = "X", s = 150, linewidths = 1, zorder = 10 )
plt.show()coordinates: [1. 2.] label: 0
coordinates: [5. 8.] label: 1
coordinates: [1.5 1.8] label: 0
coordinates: [8. 8.] label: 1
coordinates: [1. 0.6] label: 0
coordinates: [ 9. 11.] label: 1
import numpy as npx=np.array([[1,0],[0,-1]])
y=np.array([[-3,-2], [-4,1], [0,4], [4,1], [2,-3]])
z= np.dot(y,x)
print(z)[[-3 2]
[-4 -1]
[ 0 -4]
[ 4 -1]
[ 2 3]]