# -*- coding: utf-8 -*-

"""blogger_py.ipynb

Automatically generated by Colaboratory.

Original file is located at

https://colab.research.google.com/drive/1pjzSPxUXmQJxyiVjpq-Aog-T7MsUoEav

"""

import numpy,pylab,torch,torchvision

import torchvision.transforms as transforms

tr0=(0.5,0.5,0.5)

transform=transforms.Compose([transforms.ToTensor(),

transforms.Normalize(tr0,tr0)])

trainset=torchvision.datasets.CIFAR10(root='./data',

train=True,

download=True,

transform=transform)

trainloader=torch.utils.data.DataLoader(trainset,

batch_size=int(8),

shuffle=True,

num_workers=int(2))

classes=('plane','car','bird','cat','deer',

'dog','frog','horse','ship','truck')

def imshow(img):

pylab.show()

dataiter=iter(trainloader); images,labels=dataiter.next()

imshow(torchvision.utils.make_grid(images))

print(' '.join('%5s'%classes[labels[j]]

for j in range(8)))

import networkx as nx, pylab as pl

G3=nx.MultiDiGraph()

pl.figure(figsize=(9,9)); pl.axis('off')

edges=[('♔','♕'),('♔','♖'),('♔','♗'),

('♛','♜'),('♛','♝'),('♜','♝'),('♜','♞')]

G3.add_edges_from(edges); pos=nx.shell_layout(G3)

nx.draw_networkx_edges(G3,pos,width=3,alpha=.5,

edge_color='slategray',

arrowsize=50,arrowstyle='-|>')

nx.draw_networkx_nodes(G3,pos,node_size=1600,alpha=.7,

node_shape='h',node_color='steelblue')

nx.draw_networkx_labels(G3,pos,font_size=25,

font_weight='bold');

"""The fragment of the truth table of the expression F is given. <br/>

Values in empty table cells are unknown and can be any of the possible ones.<br/>

x1 x2 x3 F <br/>

&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 0 <br/>

0 &nbsp; 1 &nbsp; 0 &nbsp; 0 <br/>

List in ascending order without commas and spaces <br/>

the numbers of logical expressions that can correspond to F.

- 1) ¬x1∧x2∧¬x3

- 2) x1∨¬x2∨¬x3

- 3) x1∧¬x2∧¬x3

- 4) ¬x1∧¬x2∧x3

- 5) x1∨¬x2∨x3

"""

def F(x):

int(x[0] or (not x[1]) or x[2])]

def Fcheck(f,x):

print('Answer:',s)

Fcheck(F,[0,1,0])

"""These symbols are encoded unequally (along the length) by code: A-0, Б-10, В-110, Г-111. <br/>

The initial message: ВГВБАВА. Encode the message with this code. <br/>

Obtain the resulting sequence in a hexadecimal form. <br/>

In the answer, write down the sequence of hexadecimal digits without commas.

"""

def strdecode(s):

s0=s; ln=[('А','0'),('Б','10'),('В','110'),('Г','111')]

for (l,n) in ln: s=s.replace(l,n)

print(s0,' => ',s,' => ',hex(int(s,2))[2:].upper())

strdecode('ВГВБАВА')

import numpy,pandas

style_dict={'font-size':'150%','text-shadow':'#aaa 2px 2px 2px'}

head_styler=(('color','slategray'),('font-size','200%'),

('text-shadow','#aaa 2px 2px 2px'))

index=[chr(i) for i in range(ord('a'),ord('h')+1)];

columns=['&#x2654;','&#x2655;','&#x2656;',

'&#x2657;','&#x2658;','&#x2659;']

df=pandas.DataFrame(numpy.random.randn(8,6),

index=index,columns=columns)

df.style.set_properties(**style_dict)\

.background_gradient(cmap='Pastel1')\

.set_table_styles([dict(selector='th',props=head_styler)])

# Commented out IPython magic to ensure Python compatibility.

import pylab; from skimage import data,color,measure

from IPython.core.magic import register_line_magic

@register_line_magic

def vector(file):

pylab.xticks([]); pylab.yticks([]); pylab.show()

# %vector pattern03.jpeg

import cv2,pylab,urllib; path='https://olgabelitskaya.github.io/new/'

for f in ['10.png','24.png']:

input_file=urllib.request.urlopen(path+f); output_file=open(f,'wb')

output_file.write(input_file.read())

output_file.close(); input_file.close()

edge=cv2.imread('10.png'); edge2=cv2.imread('24.png');

edge=cv2.GaussianBlur(edge,(2*3+1,2*3+1),-1)[3:-3,3:-3]

edge2=cv2.GaussianBlur(edge2,(2*5+1,2*5+1),-1)[5:-5,5:-5]

s1='size: %s, shape: %s, type: %s'%(edge.size,edge.shape,edge.dtype)

s2='size: %s, shape: %s, type: %s'%(edge2.size,edge2.shape,edge2.dtype)

gray_edge=cv2.cvtColor(edge,cv2.COLOR_BGR2GRAY)

gray_edge2=cv2.cvtColor(edge2,cv2.COLOR_BGR2GRAY)

edges=cv2.Canny(gray_edge,60,230); edge[edges!=0]=(0,0,255)

edges2=cv2.Canny(gray_edge2,50,120); edge2[edges2!=0]=(0,0,255)

pylab.figure(figsize=(12,7))

pylab.subplot(121); pylab.title(s1,fontsize=10,loc='left')

pylab.imshow(cv2.cvtColor(edge,cv2.COLOR_BGR2RGB))

pylab.subplot(122); pylab.title(s2,fontsize=10,loc='left')

pylab.imshow(cv2.cvtColor(edge2,cv2.COLOR_BGR2RGB))

pylab.show()

import pylab,numpy

columns=['Fresh','Milk','Grocery','Frozen',

'Detergents_Paper','Delicatessen']

rows=['Customer 1','Customer 2','Customer 3']

colors = ['#3636ff','#36ff36','#ff3636']

data=[[26373,36423,22019,5154,4337,16523],

[16165,4230,7595,201,4003,57],

[14276,803,3045,485,100,518]]

pylab.figure(figsize=(12,7))

pylab.ylabel('Value'); pylab.xticks([]);

x=numpy.arange(len(columns))+0.2

y=numpy.array([0.0]*len(columns))

bar_width=0.5; cell_text=[]

for i in range(len(rows)):

pylab.bar(x,data[i],bar_width,bottom=y,color=colors[i])

y=y+data[i]; cell_text.append(['%1.0f'%(d)for d in data[i]])

pylab.table(cellText=cell_text,rowLabels=rows,

rowColours=colors,colLabels=columns,loc='bottom')

pylab.title('Samples of the Wholesale Customers Dataset')

pylab.subplots_adjust(left=0.1,bottom=0.05); pylab.show()

from sklearn.feature_extraction.text import CountVectorizer

corpus=['Have you already set your goals for the New Year?',

'Good luck!']

c_vectorizer=CountVectorizer(min_df=1); corpus_features=c_vectorizer.fit_transform(corpus)

corpus_array=corpus_features.toarray().astype('int16'); c_analyzer=c_vectorizer.build_analyzer()

import pylab; pylab.figure(figsize=(12,7))

pylab.title("The Words' Occurrence in Sentences",fontsize=12)

for i in range(len(corpus_array)):

pylab.scatter(range(len(corpus_array[i])),(corpus_array[i]*0.5+i),marker='s')

pylab.grid(); pylab.show()

import numpy,pylab,time

from sklearn import manifold,datasets

X,y=datasets.load_digits(return_X_y=True)

tsne=manifold.TSNE(n_components=2,learning_rate=700.0)

t0=time.time(); X_emb=tsne.fit_transform(X)

t=time.time()-t0

x_min,x_max=numpy.min(X_emb,0),numpy.max(X_emb,0)

X_emb=(X_emb-x_min)/(x_max-x_min)

f,ax=pylab.subplots(1,figsize=(9,9))

for i in range(X_emb.shape[0]):

pylab.text(X_emb[i,0],X_emb[i,1],str(y[i]),

color=pylab.cm.hsv(.1*y[i]))

pylab.title("t-SNE embedding %f s"%t)

pylab.axis("off"); pylab.show()

import pandas,seaborn,pylab

pylab.style.use('seaborn-pastel')

user="https://raw.githubusercontent.com/OlgaBelitskaya/"

path1="machine_learning_engineer_nd009/"

path2="master/Machine_Learning_Engineer_ND_P3/"

data=pandas.read_csv(user+path1+path2+"customers.csv")

data.drop(['Region','Channel'],axis=1,inplace=True)

data.plot.area(stacked=False,figsize=(12,7))

pylab.title('Data Values by Product Categories')

pylab.grid(); pylab.show()

import pandas,pylab

fp='https://data.cityofnewyork.us/resource/'

f='h7rb-945c.json'

input_file=urllib.request.urlopen(fp+f)

output_file=open(f,'wb')

output_file.write(input_file.read())

output_file.close(); input_file.close()

se=pandas.read_json(fp+f)

fl=['graduation_rate','city','council_district']

se=se[fl].dropna()

se['graduation_rate_range']=se.shape[0]*['0']

se['graduation_rate']=se['graduation_rate']\

.astype('str').str.replace("N/A","0").astype('float')

se['council_district']=se['council_district']\

.astype('int').astype('str')

se.loc[se['graduation_rate']<=0.5,

'graduation_rate_range']='gr <= 0.5'

se.loc[(se['graduation_rate']>0.5)&(se['graduation_rate']<=0.75),

'graduation_rate_range']='0.5 < gr <= 0.75'

se.loc[(se['graduation_rate']>0.75)&(se['graduation_rate']<=0.9),

'graduation_rate_range'] = '0.75 < gr <= 0.9'

se.loc[se['graduation_rate']>0.90,

'graduation_rate_range']='gr > 0.9'

gr_list=['gr <= 0.5','0.5 < gr <= 0.75',

'0.75 < gr <= 0.9','gr > 0.9']

fig,ax=pylab.subplots(nrows=2,ncols=1,figsize=(12,12))

s='Student Graduation Rate by Council Districts and Cities'

ax[0].tick_params('x',rotation=90)

pandas.crosstab(se['council_district'],

se['graduation_rate_range'])[gr_list]\

.plot.bar(ax=ax[0],cmap=pylab.cm.bwr)

city_list=['New York','Bronx','Brooklyn','Staten Island',

'Jamaica','Flushing','Long Island City','Manhattan']

data=se.loc[se['city'].isin(city_list)]

pandas.crosstab(data['city'],

se['graduation_rate_range'])[gr_list]\

.plot.barh(ax=ax[1],cmap=pylab.cm.bwr)

pylab.suptitle(s); pylab.grid(); pylab.show()

import pandas,pylab,urllib

fp='https://data.cityofnewyork.us/resource/'

f='h7rb-945c.json'

input_file=urllib.request.urlopen(fp+f)

output_file=open(f,'wb')

output_file.write(input_file.read())

output_file.close(); input_file.close()

se=pandas.read_json(fp+f)

fl=['dbn','ell_programs','language_classes',

'longitude','council_district','city']

se=se[fl].dropna()

f,ax=pylab.subplots(ncols=2,figsize=(12,7))

for i in range(2):

se[fl[i+5]]=se[fl[i+5]].astype('str')\

.str.replace("N/A","0").astype('float')

se.plot(kind="scatter",

x="longitude",y="latitude",

s=10,c=fl[i+5],

cmap=pylab.cm.jet,

title=fl[i+5].replace("_"," "),

ax=ax[i],alpha=.8,grid=True)

ax[i].set_facecolor('slategray')

pylab.show()

import numpy

A=numpy.array(range(1,9)).reshape(2,2,2)

B=['♔','♕','♖','♗'];

B=numpy.array(B,dtype=object).reshape(2,2)

AB1=numpy.tensordot(A,B,1)

AB01=numpy.tensordot(A,B,(0,1))

AB10=numpy.tensordot(A,B,(1,0))

def result(X):

print(X.shape)

result(AB1); result(AB01); result(AB10)

import sympy

x,y,z,t,u,v,w=sympy.symbols('x,y,z,t,u,v,w')

A=sympy.Array([[[x*sympy.cos(u)*z**2-y,

[x-sympy.exp(z)*w**7,y*t**8*u*v]]])

for el in [x,y,z,t,u,v,w]:

print(A.diff(el))

import pylab,numpy

from mpl_toolkits.mplot3d import Axes3D

t=1.1; cq=numpy.arange(0,1,0.13)

cq=numpy.concatenate((cq,numpy.repeat(cq,2)))

xq=[1,-1,1,-1,1,-1,1,-1]

yq=[1,1,-1,-1,1,1,-1,-1]

zq=[1,1,1,1,-1,-1,-1,-1]

f=pylab.figure(figsize=(9,9))

ax=f.add_subplot(111,projection='3d')

q=ax.quiver(8*[0],8*[0],8*[0],xq,yq,zq,lw=10,

colors=pylab.cm.hsv(cq),alpha=0.5)

ax.set_xlabel('$\mathscr{X}$',fontsize=15)

ax.set_ylabel('$\mathscr{Y}$',fontsize=15)

ax.set_zlabel('$\mathscr{Z}$',fontsize=15)

ax.set_xlim(-t,t); ax.set_ylim(-t,t)

ax.set_zlim(-t,t); pylab.show()