markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
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QUESTION 3 | import numpy as np
A = np.array([2,7,4])
B = np.array([3,9,8])
cross = np.cross(A,B)
print(cross) | [20 -4 -3]
| Apache-2.0 | PRELIM_EXAM.ipynb | Singko25/Linear-Algebra-58020 |
from google.colab import drive
drive.mount('/content/drive')
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision.datasets as dset
import torchvision.transforms as T
from torch.utils.data import TensorDataset
from torch.utils.data import DataLoader
from torch... | _____no_output_____ | MIT | Mean & SD.ipynb | sharlenechen0113/Real-Estate-Price-Prediction | |
Install Python. jupyter --no-browserIn the favorite browser, typehttp://localhost:8888 (or the port that is assigned)Basic usage of jupyter notebooks.- create a newdocument by clicking the New Notebook- start typing code in the shaded textbox- execute the code |
x = 0.1
N = 3
a = 1
b = 0
c = -1
print('f(' + str(x) + ') = ' + str(a*x**2 + b*x + c))
a = 1
b = 1
print(a*b,a*(b+1),a*(b+2),a*(b+3))
a = 2
print(a*b,a*(b+1),a*(b+2),a*(b+3))
a = 3
print(a*b,a*(b+1),a*(b+2),a*(b+3))
a = 4
print(a*b,a*(b+1),a*(b+2),a*(b+3))
| (1, 2, 3, 4)
(2, 4, 6, 8)
(3, 6, 9, 12)
(4, 8, 12, 16)
| MIT | fe588/fe588_introduction.ipynb | bkoyuncu/notes |
Fibionacci Series | N = 0
a_1 = 1
a_2 = 0
x = 1
if N>0:
print('x_' + str(0) + ' = ' + str(x) )
for i in range(1,N):
x = a_1 + a_2
print('x_' + str(i) + ' = ' + str(x) )
a_2 = a_1
a_1 = x
l = -1
r = 1
delta = 0.1
steps = (r-l)/delta+1
print '-'*20
print('| '),
print('x'),
print('| '),
print('3*x**2 + 2*x +... | 1 112.0
2 125.44
3 140.4928
4 157.351936
5 176.23416832
6 197.382268518
7 221.068140741
8 247.596317629
9 277.307875745
| MIT | fe588/fe588_introduction.ipynb | bkoyuncu/notes |
Arrays, lists | a = [1,2,5,7,5, 3.2, 7]
names = ['Ali','Veli','Fatma','Asli']
#for s in names:
# print(s)
print(names[3])
print(len(names))
for i in range(len(names)-1,-1,-1):
print(names[i])
for i in range(len(names)):
print(names[len(names)-i])
for n in reversed(names):
print(n) | Asli
Fatma
Veli
Ali
| MIT | fe588/fe588_introduction.ipynb | bkoyuncu/notes |
Average and standard deviation | #x = [0.1,3,-2.1,5,12,3,17]
x = [1,-1,0]
s1 = 0.0
for a in x:
s1 += a
mean = s1/len(x)
s2 = 0.0
for a in x:
s2 += (a-mean)**2
variance = s2/len(x)
print('mean = '),
print(mean)
print('variance = '),
print(variance)
| mean = 0.0
variance = 0.666666666667
| MIT | fe588/fe588_introduction.ipynb | bkoyuncu/notes |
Find the minimum in an array | a = [2,5,1.2, 0,-4, 3]
mn = a[0]
for i in range(1,len(a)):
if a[i]<mn:
mn = a[i]
print(mn)
a.sort()
a.append(-7)
v = a.pop()
a.reverse()
v = a.pop(0)
a.sort
a = 5
a.bit_length | _____no_output_____ | MIT | fe588/fe588_introduction.ipynb | bkoyuncu/notes |
Homework: Value counts given an array of integers | a = [5, 3, 1, 1, 6, 3, 2]
ua = []
for j in a:
found = False
for i in ua:
if j==i:
found = True;
break;
if not found:
ua.append(j)
print(ua)
for i in ua:
s = 0
for j in a:
if i==j:
s = s+1
print(i, s... | [True, True, False, False, True, True, False, True, False]
[False, False, True, True, False, False, True, False, False]
| MIT | fe588/fe588_introduction.ipynb | bkoyuncu/notes |
Generate random walk in an array | import random
N = 10
mu = 0
sig = 1
x = 0
a = [x]
for i in range(N):
w = random.gauss(mu, sig)
x = x + w
a.append(x)
print(a)
len(a) | [0, 0.07173293905450501, -0.3652340160453349, -0.07610430577230803, -1.4172015782500376, -0.31469586619290335, -1.4458834127459201, -0.7189045208807692, 0.9895551731951309, 0.1012103597338051, -1.0353093339238497]
| MIT | fe588/fe588_introduction.ipynb | bkoyuncu/notes |
List Comprehension |
N = 100
mu = 0
sig = 1
a = [random.gauss(mu, sig) for i in range(N)]
for i in range(len(a)-1):
a[i+1] = a[i] + a[i+1]
%matplotlib inline
import matplotlib.pylab as plt
plt.plot(a)
plt.show() | /Users/cemgil/anaconda/envs/py27/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
| MIT | fe588/fe588_introduction.ipynb | bkoyuncu/notes |
Moving Average | # Window Lenght
W = 20
y = []
for i in range(len(a)):
s = 0
n = 0
for j in range(W):
if i-j < 0:
break;
s = s + a[i-j]
n = n + 1
y.append(s/n)
plt.plot(a)
plt.plot(y)
plt.show()
| _____no_output_____ | MIT | fe588/fe588_introduction.ipynb | bkoyuncu/notes |
Moving average, second version | # Window Lenght
W = 20
y = []
s = 0
n = 0
for i in range(len(a)):
s = s + a[i]
if i>=W:
s = s - a[i-W]
else:
n = n + 1
y.append(s/n)
plt.plot(a)
plt.plot(y)
plt.show()
def mean(a):
s = 0
for x in a:
s = s+x
return s/float(len(a))
def var(a):
m... | 2.5
1.25
| MIT | fe588/fe588_introduction.ipynb | bkoyuncu/notes |
Mean and Variance, online calculation | def mean(a):
mu = 0.0
for i in range(len(a)):
mu = i/(i+1.0)*mu + 1.0/(i+1.0)*a[i]
return mu
a = [3,4,1,2]
#print(a)
print(mean(a)) | 2.5
| MIT | fe588/fe588_introduction.ipynb | bkoyuncu/notes |
Implement the recursive formula for the variance | for i in range(1,len(a)+1):
print(i)
a = [i**2 for i in range(10)]
a
st = 'if'
if st == 'if':
print('if')
elif st == 'elif':
print('elif')
else:
print('not elif')
if x<10 and x>3:
for i in range(10):
if i%2:
continue
print i
x
del x
from math import exp
import math as m
... | _____no_output_____ | MIT | fe588/fe588_introduction.ipynb | bkoyuncu/notes |
Catalog | def fun(x, par):
print(x, par['volatility'])
params = {'volatility': 0.1, 'interest_rate': 0.08}
sig = params['volatility']
r = params['interest_rate']
fun(3, params)
plate = {'Istanbul':34}
city = 'Istanbul'
print 'the number plate for', city,'is', plate[city]
plate = {'Istanbul':34, 'Adana': '01', 'Ankara': ... | _____no_output_____ | MIT | fe588/fe588_introduction.ipynb | bkoyuncu/notes |
Tuples (Immutable Arrays, no change possible after creation) | a = ('Ankara', '06')
a.count('Istanbul')
%matplotlib inline
import numpy as np
import matplotlib.pylab as plt
x = np.arange(-2,2,0.1)
plt.plot(x,x)
plt.plot(x,x**2)
plt.plot(x,np.sin(x))
plt.show()
| _____no_output_____ | MIT | fe588/fe588_introduction.ipynb | bkoyuncu/notes |
Numpy arrays versus matrices | A = np.random.rand(3,5)
x = np.random.rand(5,1)
print(A.dot(x))
A = np.mat(A)
x = np.mat(x)
print(A*x)
a = np.mat(np.random.rand(3,1))
b = np.mat(np.random.rand(3,1))
print(a)
print(b)
a.T*b
N = 1000
D = 3
X = np.random.rand(N, D)
mu = X.mean(axis=0, keepdims=True)
#print(mu)
print((X - mu).T.dot(X-mu)/(N-1.))
n... | [[ 1 2 3 4 5 6 7 8 9 10]
[ 2 4 6 8 10 12 14 16 18 20]
[ 3 6 9 12 15 18 21 24 27 30]
[ 4 8 12 16 20 24 28 32 36 40]
[ 5 10 15 20 25 30 35 40 45 50]
[ 6 12 18 24 30 36 42 48 54 60]
[ 7 14 21 28 35 42 49 56 63 70]
[ 8 16 24 32 ... | MIT | fe588/fe588_introduction.ipynb | bkoyuncu/notes |
B&S with Monte Carlo Call and Put pricing, use a catalog and numpy, avoid using for loops | import numpy as np
def European(Param, S0=1., T=1., Strike=1.,N=10000 ):
'''
Price_Call, Price_Put = European(Param, S0, T, Strike,N)
Param: Market parameters, a catalog with fields
Param['InterestRate'] : Yearly risk free interest rate
Param['Volatility'] :
S0 : ... | European
Call= 15.5726197769
Put = 5.13556380233
Asian
Call= 8.16477817074
Put = 3.17271035914
Lookback
Call= 25.6819276647
Put = 12.5838549789
FloatingLookback
Call= 23.0385882044
Put = 15.3296952253
| MIT | fe588/fe588_introduction.ipynb | bkoyuncu/notes |
Next week Assignment:Consolidate all pricing methods into one single function avoiding code repetitions. | def OptionPricer(type_of_option, Params):
'''
Price_Call, Price_Put = OptionPricer(type_of_option, Param, S0, T, Strike, Steps, N)
type_of_option = 'European'
'Asian', 'Lookback', 'FloatingLookback'
Param: Parameter catalog with fields
Param['InterestRate'] :... | _____no_output_____ | MIT | fe588/fe588_introduction.ipynb | bkoyuncu/notes |
Next week: Kalman Filtering (Learn Numpy and matplotlib) | th = 0.5
A = np.mat([[np.cos(th), np.sin(th)],[-np.sin(th), np.cos(th)]])
x = np.mat([1,0]).T
x = np.mat([[1],[0]])
for t in range(10):
x = A*x + 0*np.random.randn(2,1)
print(x)
name = raw_input("What is your name? ")
print name
def fun(x):
print x,
x = map(fun,range(1,10+1))
x = map(fun,range(1,10+... | _____no_output_____ | MIT | fe588/fe588_introduction.ipynb | bkoyuncu/notes |
Importing classes from the `tcalc` module | from TCalc.tcalc import eyepiece, telescope, barlow_lens, focal_reducer | _____no_output_____ | MIT | docs/tutorials/TCalc_tutorial.ipynb | Bhavesh012/Telescope-Calculator |
To quickly access the docstring, run `help(classname)` | help(eyepiece) | Help on class eyepiece in module TCalc.tcalc:
class eyepiece(builtins.object)
| eyepiece(f_e, fov_e=50)
|
| Class representing a single eyepiece
| Args:
| f_e: focal length of the eyepiece (mm)
| fov_e: field of view of the eyepiece (deg). Defaults to 50 degrees.
|
| Methods defined here:
|... | MIT | docs/tutorials/TCalc_tutorial.ipynb | Bhavesh012/Telescope-Calculator |
For an example, let's try to have estimate the specifications of Celestron's 8 SE telescope. | c8 = telescope(D_o=203.2, f_o=2032, user_D_eye=None, user_age=22) # adding configuration of 8in scope
omni_40 = eyepiece(40, 52) # defining 40 mm eyepiece
omni_25 = eyepiece(25, 52) # defining 25 mm eyepiece
# adding eyepiece to the telescope
c8.add_eyepiece(omni_40, id='omni_40', select=True)
c8.add_eyepiece(omni_25, ... |
The telescope has the following layout:
Aperture diameter: 203.2 mm
Focal length: 2032 mm, corresponding to a focal ratio of 10.0
'barlow 1', a Barlow lens, has been added to the optical path. This increases the focal length by 2
This results in
Focal length: 4064 mm, corresponding to a foca... | MIT | docs/tutorials/TCalc_tutorial.ipynb | Bhavesh012/Telescope-Calculator |
You can notice that if used a *2x barlow lens* on a *40mm eyepiece*, the brightness of the object will be decresead by **4 times!**This way you can simulate different scenarios and find out which accesories are optimal for your purpose. This will save you both time and money on costly accesories! For advanced users, t... | c8.show_resolving_power()
c8.show_magnification_limits()
c8.show_eyepiece_limits() | _____no_output_____ | MIT | docs/tutorials/TCalc_tutorial.ipynb | Bhavesh012/Telescope-Calculator |
Week 6 - SMM695Matteo DevigiliJune, 28th 2021[_PySpark_](https://spark.apache.org/docs/latest/api/python/index.html): during this lecture, we will approach Spark through Python **Agenda**:1. Introduction to Spark1. Installing PySpark1. PySpark Basics1. PySpark and Pandas1. PySpark and SQL1. Load data from your DBMS I... | #to create a spark session object
from pyspark.sql import SparkSession
# functions
import pyspark.sql.functions as F
# data types
from pyspark.sql.types import *
# import datetime
from datetime import date as dt | _____no_output_____ | MIT | week-6/sc_6.ipynb | mattDevigili/dms-smm695 |
* More info on **Functions** at these [link-1](https://spark.apache.org/docs/latest/api/python/pyspark.sql.htmlmodule-pyspark.sql.functions) & [link-2](https://spark.apache.org/docs/2.3.0/api/sql/index.htmlyear)* More info on **Data Types** at this [link](https://spark.apache.org/docs/latest/sql-ref-datatypes.html) Op... | # to open a Session
spark = SparkSession.builder.appName('last_dance').getOrCreate() | _____no_output_____ | MIT | week-6/sc_6.ipynb | mattDevigili/dms-smm695 |
**Spark UI**The spark UI is useful to monitor your application. You have the following tabs:* *Jobs*: info concerning Spark jobs* *Stages*: info on individual stages and their tasks* *Storage*: info on data that is currently in our spark application* *Environment*: info on configurations and current settings of our app... | spark | _____no_output_____ | MIT | week-6/sc_6.ipynb | mattDevigili/dms-smm695 |
Create DataframeIn order to create a dataframe from scratch, we need to:1. Create a schema, passing: * Column names * Data types1. Pass values as an array of tuples | # Here, I define a schema
# .add(field, data_type=None, nullable=True, metadata=None)
schema = StructType().add("id", "integer", True).add("first_name", "string", True).add(
"last_name", "string", True).add("dob", "date", True)
'''
schema = StructType().add("id", IntegerType(), True).add("first_name", StringType(... | _____no_output_____ | MIT | week-6/sc_6.ipynb | mattDevigili/dms-smm695 |
**Transformations*** Immutability: once created, data structures can not be changed* Lazy evaluation: computational instructions will be executed at the very last **Actions*** view data* collect data* write to output data sources PySpark and Pandas Load a csv Loading a csv file from you computer, you need to type:* P... | # import pandas
import pandas as pd
# import SparkFiles
from pyspark import SparkFiles
# target dataset
url = 'https://raw.githubusercontent.com/fivethirtyeight/data/master/bechdel/movies.csv'
# loading data with pandas
db = pd.read_csv(url)
# loading data with pyspark
spark.sparkContext.addFile(url)
df = spark.read... | _____no_output_____ | MIT | week-6/sc_6.ipynb | mattDevigili/dms-smm695 |
Inspecting dataframes | # pandas info
db.info()
# pyspark schema
df.printSchema()
# pandas fetch 5
db.head(5)
# pyspark fetch 5
df.show(5)
df.take(5)
# pandas filtering:
db[db.year == 1970]
# pyspark filtering:
df[df.year == 1970].show()
# get columns and data types
print("""
Pandas db.columns:
===================
{}
PySpark df.columns:
===... | _____no_output_____ | MIT | week-6/sc_6.ipynb | mattDevigili/dms-smm695 |
Columns | # pandas add a column
db['newcol'] = db.domgross/db.intgross
# pyspark add a column
df=df.withColumn('newcol', df.domgross/df.intgross)
# pandas rename columns
db.rename(columns={'newcol': 'dgs/igs'}, inplace=True)
# pyspark rename columns
df=df.withColumnRenamed('newcol', 'dgs/igs') | _____no_output_____ | MIT | week-6/sc_6.ipynb | mattDevigili/dms-smm695 |
Drop | # pandas drop `code' column
db.drop('code', axis=1, inplace=True)
# pyspark drop `code' column
df=df.drop('code')
# pandas dropna()
db.dropna(subset=['domgross'], inplace=True)
# pyspark dropna()
df=df.dropna(subset='domgross') | _____no_output_____ | MIT | week-6/sc_6.ipynb | mattDevigili/dms-smm695 |
Stats | # pandas describe
db.describe()
# pyspark describe
df.describe(['year', 'budget']).show() | _____no_output_____ | MIT | week-6/sc_6.ipynb | mattDevigili/dms-smm695 |
Pyspark and SQL | # pyspark rename 'budget_2013$'
df=df.withColumnRenamed('budget_2013$', 'budget_2013')
# Create a temporary table
df.createOrReplaceTempView('bechdel')
# Run a simple SQL command
sql = spark.sql("""SELECT imdb, year, title, budget FROM bechdel LIMIT(5)""")
sql.show()
# AVG budget differences
sql_avg = spark.sql(
... | _____no_output_____ | MIT | week-6/sc_6.ipynb | mattDevigili/dms-smm695 |
Load data from DBMS To run the following you need to restart the notebook. | # to create a spark session object
from pyspark.sql import SparkSession | _____no_output_____ | MIT | week-6/sc_6.ipynb | mattDevigili/dms-smm695 |
PostgreSQL To interact with postgre you need to: * Download the *postgresql-42.2.22.jar file* [here](https://jdbc.postgresql.org/download.html)* Include the path to the downloaded jar file into SparkSession() | # Open a session running data from PostgreSQL
spark_postgre = SparkSession \
.builder \
.appName("last_dance_postgre") \
.config("spark.jars", "/Users/matteodevigili/py3venv/dms695/share/py4j/postgresql-42.2.22.jar") \
.getOrCreate()
spark_postgre
# Read data from PostgreSQL running at localhost
df = sp... | _____no_output_____ | MIT | week-6/sc_6.ipynb | mattDevigili/dms-smm695 |
MongoDB For further reference check the [Python Guide provided by Mongo](https://docs.mongodb.com/spark-connector/current/python-api/) or the [website for the mongo-spark connector](https://spark-packages.org/package/mongodb/mongo-spark). | # add path to Mongo
spark_mongo = SparkSession \
.builder \
.appName("last_dance_mongo") \
.config("spark.mongodb.input.uri", "mongodb://127.0.0.1/amazon.music") \
.config("spark.mongodb.output.uri", "mongodb://127.0.0.1/amazon.music") \
.config('spark.jars.packages', 'org.mongodb.spark:mongo-spark-... | _____no_output_____ | MIT | week-6/sc_6.ipynb | mattDevigili/dms-smm695 |
______Universidad Tecnológica Nacional, Buenos Aires__\__Ingeniería Industrial__\__Cátedra de Investigación Operativa__\__Autor: Rodrigo Maranzana______ Ejercicio 3 Un agente comercial realiza su trabajo en tres ciudades A, B y C. Para evitar desplazamientos innecesarios está todo el día en la misma ciudad y allí pern... | import numpy as np | _____no_output_____ | Apache-2.0 | 04_markov/.ipynb_checkpoints/ejercicio_3-checkpoint.ipynb | juanntripaldi/pyOperativ |
Ingresamos los datos de la matriz de transición en una matriz numpy: | # Matriz de transición como numpy array:
T = np.array([[0.1, 0.3, 0.6],
[0.2, 0.2, 0.6],
[0.2, 0.4, 0.4]])
# Printeamos T
print(f'Matriz de transición: \n{T}') | Matriz de transición:
[[0.1 0.3 0.6]
[0.2 0.2 0.6]
[0.2 0.4 0.4]]
| Apache-2.0 | 04_markov/.ipynb_checkpoints/ejercicio_3-checkpoint.ipynb | juanntripaldi/pyOperativ |
Ejercicio A En primer lugar, calculamos la matriz de transición habiendo pasado 4 días: elevamos la matriz a la cuarta usando el método de la potencia de álgebra lineal de la librería Numpy. | # Cálculo de la matriz de transición a tiempo 4:
T4 = np.linalg.matrix_power(T, 4)
# printeamos la matriz de transicion de 4 pasos:
print(f'Matriz de transición a tiempo 4: \n{T4}\n') | Matriz de transición a tiempo 4:
[[0.1819 0.3189 0.4992]
[0.1818 0.319 0.4992]
[0.1818 0.3174 0.5008]]
| Apache-2.0 | 04_markov/.ipynb_checkpoints/ejercicio_3-checkpoint.ipynb | juanntripaldi/pyOperativ |
Sabiendo que $p_0$ considera que el agente está en el nodo C:$ p_0 = (0, 0, 1) $ | # Generación del vector inicial p_0:
p_0 = np.array([0, 0, 1])
# printeamos el vector inicial:
print(f'Vector de estado a tiempo 0: \n{p_0}\n') | Vector de estado a tiempo 0:
[0 0 1]
| Apache-2.0 | 04_markov/.ipynb_checkpoints/ejercicio_3-checkpoint.ipynb | juanntripaldi/pyOperativ |
Calculamos: $ p_0 T^4 = p_4 $ | # Cálculo del estado a tiempo 4, p_4:
p_4 = np.dot(p_0, T4)
# printeamos p4:
print(f'Vector de estado a tiempo 4: \n{p_4}\n') | Vector de estado a tiempo 4:
[0.1818 0.3174 0.5008]
| Apache-2.0 | 04_markov/.ipynb_checkpoints/ejercicio_3-checkpoint.ipynb | juanntripaldi/pyOperativ |
Dado el vector $ p_4 $, nos quedamos con el componente perteneciente al estado C. | # Componente del nodo C:
p_4_c = p_4[2]
# printeamos lo obtenido:
print(f'Probabilidad de estar en c habiendo iniciado en c: \n{p_4_c}\n') | Probabilidad de estar en c habiendo iniciado en c:
0.5008
| Apache-2.0 | 04_markov/.ipynb_checkpoints/ejercicio_3-checkpoint.ipynb | juanntripaldi/pyOperativ |
Forma alternativa de resolución:El resultado es el mismo si consideramos que la componente ${T^4}_{cc}$ es la probabilidad de transición del nodo c al mismo nodo habiendo pasado 4 ciclos.Veamos cómo se obtiene esa componente: | # Componente de cc de la matriz de transición a tiempo 4:
T4cc = T4[2,2]
print('\n ** Probabilidad de estar en c habiendo iniciado en c: \n %.5f' % T4cc) |
** Probabilidad de estar en c habiendo iniciado en c:
0.50080
| Apache-2.0 | 04_markov/.ipynb_checkpoints/ejercicio_3-checkpoint.ipynb | juanntripaldi/pyOperativ |
Ejercicio B Dada una matriz $A$ proveniente del sistema de ecuaciones que resuelve $\pi T = \pi$ | # Matriz A:
A = np.array([[-0.9, 0.2, 0.2],
[ 0.3, -0.8, 0.4],
[ 0.6, 0.6, -0.6],
[1, 1, 1]])
# Printeamos A:
print(f'Matriz asociada al sistema lineal de ecuaciones: \n{A}') | Matriz asociada al sistema lineal de ecuaciones:
[[-0.9 0.2 0.2]
[ 0.3 -0.8 0.4]
[ 0.6 0.6 -0.6]
[ 1. 1. 1. ]]
| Apache-2.0 | 04_markov/.ipynb_checkpoints/ejercicio_3-checkpoint.ipynb | juanntripaldi/pyOperativ |
Y dado un vector $B$ relacionado con los términos independientes del sistema de ecuaciones anteriormente mencionado. | # Vector B:
B = np.array([0, 0, 0, 1])
# Printeamos B:
print(f'Vector de términos independientes: \n{B}') | Vector de términos independientes:
[0 0 0 1]
| Apache-2.0 | 04_markov/.ipynb_checkpoints/ejercicio_3-checkpoint.ipynb | juanntripaldi/pyOperativ |
Dado que el solver de numpy solamente admite sistemas lineales cuadrados por el algoritmo que usa para la resolución [1], debemos eliminar una de las filas (cualquiera) de la matriz homogénea y quedarnos con la fila relacionada a la ecuación $ \sum_i{\pi_i} = 1$.Hacemos lo mismo para el vector de términos independiente... | # Copio la matriz A original, para que no se modifique.
A_s = A.copy()
# Eliminamos la primer fila de la matriz A:
A_s = np.delete(A_s, 0, 0)
# Printeamos:
print(f'Matriz asociada al sistema lineal de ecuaciones: \n{A_s}')
print(f'\n -> Dimensión: {A_s.shape}')
# Copio el vector B original, para que no se modifique.... |
Vector de términos independientes:
[0 0 1]
-> Dimensión: (3,)
| Apache-2.0 | 04_markov/.ipynb_checkpoints/ejercicio_3-checkpoint.ipynb | juanntripaldi/pyOperativ |
Cumpliendo con un sistema cuadrado, usamos el método solve de numpy para obtener $x$ del sistema $Ax = B$ | x = np.linalg.solve(A_s, B_s)
print('\n ** Vector solución de estado estable: \n %s' % x) |
** Vector solución de estado estable:
[0.18181818 0.31818182 0.5 ]
| Apache-2.0 | 04_markov/.ipynb_checkpoints/ejercicio_3-checkpoint.ipynb | juanntripaldi/pyOperativ |
Forma alternativa: usando una matriz no cuadradaComo explicamos anteriormente no podemos usar el método $solve$ en matrices no cuadradas. En su lugar podemos usar el método de los mínimos cuadrados para aproximar la solución[2]. Este método no tiene restricciones en cuanto a la dimensión de la matriz.El desarrollo del... | x_lstsq, _, _, _ = np.linalg.lstsq(A, B, rcond=None)
print('\n ** Vector solución de estado estable: \n %s' % x_lstsq) |
** Vector solución de estado estable:
[0.18181818 0.31818182 0.5 ]
| Apache-2.0 | 04_markov/.ipynb_checkpoints/ejercicio_3-checkpoint.ipynb | juanntripaldi/pyOperativ |
Cálculo auxiliar: partiendo directamente de la matriz de transiciónEn la resolución original, usamos una matriz A relacionada al sistema lineal de ecuaciones que resolvimos a mano. Ahora veremos otra forma de llegar a la solución solamente con los datos dados y tratamiento de matrices.Partiendo del sistema original: $... | # Primero calculamos la traspuesta de la matriz de transición:
Tt = np.transpose(T)
print(f'\nT traspuesta: \n{Tt}')
# Luego con calculamos la matriz A, sabiendo que es la traspuesta de T menos la identidad.
A1 = Tt - np.identity(Tt.shape[0])
print(f'\nMatriz A: \n{A1}') |
T traspuesta:
[[0.1 0.2 0.2]
[0.3 0.2 0.4]
[0.6 0.6 0.4]]
Matriz A:
[[-0.9 0.2 0.2]
[ 0.3 -0.8 0.4]
[ 0.6 0.6 -0.6]]
| Apache-2.0 | 04_markov/.ipynb_checkpoints/ejercicio_3-checkpoint.ipynb | juanntripaldi/pyOperativ |
Seguimos con: $B = 0$ | # El vector B, es un vector de ceros:
B1 = np.zeros(3)
print(f'\nVector B: \n{B1}') |
Vector B:
[0. 0. 0.]
| Apache-2.0 | 04_markov/.ipynb_checkpoints/ejercicio_3-checkpoint.ipynb | juanntripaldi/pyOperativ |
A partir de aca, simplemente aplicamos el método que ya sabemos. Agregamos la información correspondiente a: $\sum_i{\pi_i} = 1$. | # Copio la matriz A1 original, para que no se modifique.
A1_s = A1.copy()
# Agregamos las probabilidades a la matriz A
eq_suma_p = np.array([[1, 1, 1]])
A1_s = np.concatenate((A1_s, eq_suma_p), axis=0)
# Printeamos:
print(f'Matriz A: \n{A1_s}')
# Copio el vector B1 original, para que no se modifique.
B1_s = B1.copy... |
Vector B:
[0. 0. 0. 1.]
| Apache-2.0 | 04_markov/.ipynb_checkpoints/ejercicio_3-checkpoint.ipynb | juanntripaldi/pyOperativ |
Resolvemos por mínimos cuadrados: | # Resolvemos con método de mínimos cuadrados:
x_lstsq, _, _, _ = np.linalg.lstsq(A1_s, B1_s, rcond=None)
# Printeamos la solucion:
print(f'\nVector solución de estado estable: {x_lstsq}') |
Vector solución de estado estable: [0.18181818 0.31818182 0.5 ]
| Apache-2.0 | 04_markov/.ipynb_checkpoints/ejercicio_3-checkpoint.ipynb | juanntripaldi/pyOperativ |
Wavefront set inpainting real phantom In this notebook we are implementing a Wavefront set inpainting algorithm based on a hallucination network | %matplotlib inline
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
# Import the needed modules
from data.data_factory import generate_realphantom_WFinpaint, DataGenerator_realphantom_WFinpaint
from ellipse.ellipseWF_factory import plot_WF
import matplotlib.pyplot as plt
import numpy.random as rnd
import numpy as np
... | /store/kepler/datastore/andrade/GitHub_repos/Joint_CTWF_Recon/WF_inpaint/data/data_factory.py:7: UserWarning:
This call to matplotlib.use() has no effect because the backend has already
been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
or matplotlib.backends is imported for the first time... | MIT | WF_inpaint/WF_inpaint_realphantom_unet_train.ipynb | arsenal9971/DeeMicrolocalReconstruction |
Data generator | batch_size = 1
size = 256
nClasses = 180
lowd = 40
y_arr, x_true_arr =generate_realphantom_WFinpaint(batch_size, size, nClasses, lowd)
plt.figure(figsize=(6,6))
plt.axis('off')
plot_WF(y_arr[0,:,:,0])
plt.figure(figsize=(6,6))
plt.axis('off')
plot_WF(x_true_arr[0,:,:,0]) | _____no_output_____ | MIT | WF_inpaint/WF_inpaint_realphantom_unet_train.ipynb | arsenal9971/DeeMicrolocalReconstruction |
Load the model | # Tensorflow and seed
seed_value = 0
import random
random.seed(seed_value)
import tensorflow as tf
tf.set_random_seed(seed_value)
# Importing relevant keras modules
from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger
from tensorflow.keras.models import load_model
from shared.shared import create_increasi... | Epoch 1/10000
111/112 [============================>.] - ETA: 13s - loss: 0.9985 - my_mean_squared_error: 111.1464 - mean_squared_error: 111.1464 - mean_absolute_error: 0.9985 - l2_on_wedge: 107.8654 - my_psnr: -5.8870 | MIT | WF_inpaint/WF_inpaint_realphantom_unet_train.ipynb | arsenal9971/DeeMicrolocalReconstruction |
7. Vertical Vibration of Quarter Car ModelThis notebook introduces the base excitation system by examning the behavior of a quarter car model.After the completion of this assignment students will be able to:- excite a system with a sinusoidal input- understand the difference in transient and steady state solutions- cr... | import numpy as np
import matplotlib.pyplot as plt
%matplotlib notebook
from resonance.linear_systems import SimpleQuarterCarSystem
sys = SimpleQuarterCarSystem() | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
The simple quarter car model has a suspension stiffness and damping, along with the sprung car mass in kilograms, and a travel speed parameter in meters per second. | sys.constants
sys.coordinates
sys.speeds | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
A sinusoidal roadThe road is described as:$$y(t) = Ysin\omega_b t$$where $Y$ is the amplitude of the sinusoidal road undulations and $\omega_b$ is the frequency of the a function of the car's speed. If the distance between the peaks (amplitude 0.01 meters) of the sinusoidal road is 6 meters and the car is traveling at... | Y = 0.01 # m
v = sys.constants['travel_speed']
bump_distance = 6 # m
wb = v / bump_distance * 2 * np.pi # rad /s
print(wb) | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
Now with the amplitude and frequency set you can use the `sinusoidal_base_displacing_response()` function to simulate the system. | traj = sys.sinusoidal_base_displacing_response(Y, wb, 20.0)
traj.head()
traj.plot(subplots=True); | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
We've written an animation for you. You can play it with: | sys.animate_configuration(fps=20) | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
**Exercise**Try different travel speeds and see what kind of behavior you can observe. Make sure to set the `travel_speed` constant and the frequency value for `sinusoidal_base_displacing_response()` to be consistent. TransmissibilityWhen designing a car the designer wants the riders to feel comfortable and to isolate... | from scipy.optimize import curve_fit
def cosine_func(times, amp, freq, phase_angle):
return amp * np.cos(freq * times - phase_angle)
frequencies = np.linspace(1.0, 20.0, num=100)
amplitudes = []
for omega in frequencies:
traj = sys.sinusoidal_base_displacing_response(Y, omega, 20.0)
popt, pcov = cu... | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
The second thing to investigate is the *force transmissibility*. This is the ratio of the force applied by the suspension to the sprung car. Riders will feel this force when the car travels over bumps. Reducing this is also preferrable. The force applied to the car can be compared to the **Excersice**Create a measureme... | Y = 0.01 # m
bump_distance = 6 # m
def force_on_car(suspension_damping, suspension_stiffness,
car_vertical_position, car_vertical_velocity,
travel_speed, time):
wb = travel_speed / bump_distance * 2 * np.pi
y = Y * np.sin(wb * time)
yd = Y * wb * np.cos(wb * ti... | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
Force transmissibility will be visited more in your next homework. Arbitrary Periodic Forcing (Fourier Series)Fourier discovered that any periodic function with a period $T$ can be described by an infinite series of sums of sines and cosines. See the wikipedia article for more info (https://en.wikipedia.org/wiki/Four... | import sympy as sm | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
The function `init_printing()` enables LaTeX based rendering in the Jupyter notebook of all SymPy objects. | sm.init_printing() | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
Symbols can be created by using the `symbols()` function. | x, y, z = sm.symbols('x, y, z')
x, y, z | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
The `integrate()` function allows you to do symbolic indefinite or definite integrals. Note that the constants of integration are not included in indefinite integrals. | sm.integrate(x * y, x) | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
The `Integral` class creates and unevaluated integral, where as the `integrate()` function automatically evaluates the integral. | expr = sm.Integral(x * y, x)
expr | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
To evaluate the unevaluated form you call the `.doit()` method. Note that all unevaluated SymPy objects have this method. | expr.doit() | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
This shows how to create an unevaluated definite integral, store it in a variable, and then evaluate it. | expr = sm.Integral(x * y, (x, 0, 5))
expr
expr.doit() | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
Fourier Coefficients for the Sawtooth functionNow let's compute the Fourier coefficients for a saw tooth function. The function that describes the saw tooth is:$$F(t) = \begin{cases} A \left( \frac{4t}{T} - 1 \right) & 0 \leq t \leq T/2 \\ A \left( 3 - \frac{4t}{t} \right) & T/2 \leq t \leq T \end{cases}$$w... | A, T, wT, t = sm.symbols('A, T, omega_T, t', real=True, positive=True)
A, T, wT, t | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
The first Fourier coefficient $a_0$ describes the average value of the periodic function. and is:$$a_0 = \frac{2}{T} \int_0^T F(t) dt$$This integral will have to be done in two parts:$$a_0 = a_{01} + a_{02} = \frac{2}{T} \int_0^{T/2} F(t) dt + \frac{2}{T} \int_{T/2}^T F(t) dt$$These two integrals are evaluated below. N... | ao_1 = 2 / T * sm.Integral(A * (4 * t / T - 1), (t, 0, T / 2))
ao_1
ao_1.doit()
ao_2 = 2 / T * sm.Integral(A * (3 - 4 * t / T), (t, T / 2, T))
ao_2
ao_2.doit() | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
But SymPy can also handle piecewise directly. The following shows how to define a piecewise function. | F_1 = A * (4 * t / T - 1)
F_2 = A * (3 - 4 * t / T)
F = sm.Piecewise((F_1, t<=T/2),
(F_2, T/2<t))
F
F_of_t_only = F.xreplace({A: 0.01, T: 2 * sm.pi / wb})
F_of_t_only
sm.plot(F_of_t_only, (t, 0, 2 * np.pi / wb)) | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
The integral can be taken of the entire piecewise function in one call. | sm.integrate(F, (t, 0, T)) | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
Now the Fourier coefficients $a_n$ and $b_n$ can be computed.$$a_n = \frac{2}{T}\int_0^T F(t) \cos n\omega_Tt dt \\b_n = \frac{2}{T}\int_0^T F(t) \sin n\omega_Tt dt$$ | n = sm.symbols('n', real=True, positive=True) | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
For $a_n$: | an = 2 / T * sm.Integral(F * sm.cos(n * wT * t), (t, 0, T))
an
an.doit() | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
This can be simplified: | an = an.doit().simplify()
an | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
Now substitute the $2\pi/T$ for $\omega_T$. | an = an.subs({wT: 2 * sm.pi / T})
an | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
Let's see how this function varies with increasing $n$. We will use a loop but the SymPy expressions will not automatically display because they are inside a loop. So we need to use SymPy's `latex()` function and the IPython display tools. SymPy's `latex()` function transforms the SymPy expression into a string of mat... | sm.latex(an, mode='inline') | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
The `display()` and `LaTeX()` functions then turn the LaTeX string in to a displayed version. | from IPython.display import display, Latex | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
Now we can see how $a_n$ varies with $n=1,2,\ldots$. | for n_i in range(1, 6):
ans = an.subs({n: n_i})
display(Latex('$a_{} = $'.format(n_i) + sm.latex(ans, mode='inline'))) | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
For even $n$ values the coefficient is zero and for even values it varies with the inverse of $n^2$. More precisely:$$a_n =\begin{cases}0 & \textrm{if }n\textrm{ is even} \\-\frac{8A}{n^2\pi^2} & \textrm{if }n\textrm{ is odd}\end{cases}$$SymPy can actually reduce this further if your set the assumption that $n$ is an i... | n = sm.symbols('n', real=True, positive=True, integer=True)
an = 2 / T * sm.Integral(F * sm.cos(n * wT * t), (t, 0, T))
an = an.doit().simplify()
an.subs({wT: 2 * sm.pi / T}) | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
The odd and even versions can be computed by setting the respective assumptions. | n = sm.symbols('n', real=True, positive=True, integer=True, odd=True)
an = 2 / T * sm.Integral(F * sm.cos(n * wT * t), (t, 0, T))
an = an.doit().simplify()
an.subs({wT: 2 * sm.pi / T}) | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
Note that $b_n$ is always zero: | bn = 2 / T * sm.Integral(F * sm.sin(n * wT * t), (t, 0, T))
bn
bn.doit().simplify().subs({wT: 2 * sm.pi / T}) | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
Numerical evalution of the Fourier SeriesNow the Fourier coefficients can be used to plot the approximation of the saw tooth forcing function. | import numpy as np | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
The following function plots the actual sawtooth function. It does it all in one line by cleverly using the absolute value and the modulo functions. | def sawtooth(A, T, t):
return (4 * A / T) * (T / 2 - np.abs(t % T - T / 2) ) - A
A = 1
T = 2
t = np.linspace(0, 5, num=500)
plt.figure()
plt.plot(t, sawtooth(A, T, t)); | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
ExerciseWrite a function that computes the Fourier approximation of the sawtooth function for a given value of $n$, i.e. using a finite number of terms. Then plot it for $n=2, 4, 6, 8, 10$ on top of the actual sawtooth function. How many terms of the infinite series are needed to get a good sawtooth?```pythondef sawto... | def sawtooth_approximation(n, A, T, t):
# odd values of indexing variable up to n
n = np.arange(1, n+1)[:, np.newaxis]
# cos coefficients
an = A *(8 * (-1)**n - 8) / 2 / np.pi**2 / n**2
# sawtooth frequency
wT = 2 * np.pi / T
# sum of n cos functions
f = np.sum(an * np.cos(n * wT * t), a... | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
Below is a interactive plot that shows the same thing as above. | A = 1
T = 2
t = np.linspace(0, 5, num=500)
fig, ax = plt.subplots(1, 1)
f = sawtooth(A, T, t)
saw_tooth_lines = ax.plot(t, f, color='k')
n = 2
f_approx = sawtooth_approximation(n, A, T, t)
approx_lines = ax.plot(t, f_approx)
leg = ax.legend(['true', 'approx, n = {}'.format(n)])
# zoom in a bit on the interestin... | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
Apply the sawtooth to the quarter carNow that you know the Fourier series coefficients. Calculate them for a suitable number of terms and simulate them with the `sys.periodic_base_displacing_response()` function.Your code should look something like:```pythondef fourier_coeffs(A, T, N): write your code herea0, an, ... | def fourier_coeffs(A, T, N):
n = np.arange(1, N+1)
an = A *(8 * (-1)**n - 8) / 2 / np.pi**2 / n**2
return 0, an, np.zeros_like(an)
a0, an, bn = fourier_coeffs(0.01, 2 * np.pi / wb, 100)
traj = sys.periodic_base_displacing_response(a0, an, bn, wb, 20.0)
traj.plot(subplots=True)
sys.animate_configuration(fp... | _____no_output_____ | MIT | notebooks/07/07_vertical_vibration_of_a_quarter_car.ipynb | gbrault/resonance |
Define the data_directory of preprocessed data | data_directory = "C:/Users/kwokp/OneDrive/Desktop/Study/zzz_application project/Final/data_after_preprocessing.csv" | _____no_output_____ | MIT | Codes/2.2 Remove redundant words - SVM,KNN,Kmeans_v2.ipynb | matchlesswei/application_project_nlp_company_description |
We devide the data into 3 groups:* Group 1: full data* Group 2: data with four large categories which have more than 1000 companies each* Group 3: seven categories of data, number of companies in each category is same but small In the function selectGroup, giving 1, 2 or 3 as input parameter to selet the relevant data... | # read the data from directory, then select the group
# of data we want to process.
def selectGroup(directory, group_nr):
data = pd.read_csv(directory, sep='\t')
if group_nr == 1:
return data
if group_nr == 2:
df_healthcare_group=data[data['Category'] == 'HEALTHCARE GROUP'].sample(n=1041,re... | _____no_output_____ | MIT | Codes/2.2 Remove redundant words - SVM,KNN,Kmeans_v2.ipynb | matchlesswei/application_project_nlp_company_description |
List Occurence of words in Top 50 Keywords in Categories | #visualize top_n words with occurence
def visulaze_topwords_occurence(top_n, word_list, occurence_list):
objects = word_list
y_pos = np.arange(len(word_list))
performance = occurence_list
plt.figure(figsize=(10,24))
plt.barh(y_pos, performance, align='center', alpha=0.5)
plt.yticks(y_pos, objec... | _____no_output_____ | MIT | Codes/2.2 Remove redundant words - SVM,KNN,Kmeans_v2.ipynb | matchlesswei/application_project_nlp_company_description |
We remove the redundunt words which appears in multiple category . Main steps are as follows:1. select the group of data to do the test2. generate TF-IDF score matrix3. get the top 50 words in each category4. find the words which appears in more than one category's top-50 words, set them as stopwords5. remove these st... | #get the data, remove the frequent words which appear in more than one category, and update the tf-idf score matrix
data = selectGroup(data_directory, 1)
score_matrix, feature_extraction = tf_idf_func(data['clean'], 8000)
sortedDict = get_top_keywords_with_frequence(50, score_matrix, data, feature_extraction)
_, _, fre... |
Cluster BUSINESS & FINANCIAL SERVICES
learn,agreement,need,insurance,media,experience,financial,companies,clients,marketing
Cluster CONSUMER GOODS GROUP
read,sites,address,brand,organic,home,shipping,ingredients,foods,food
Cluster CONSUMER SERVICES GROUP
experience,media,sites,world,address,parties,people,day,agreem... | MIT | Codes/2.2 Remove redundant words - SVM,KNN,Kmeans_v2.ipynb | matchlesswei/application_project_nlp_company_description |
Split the data 80% for training and 20% for testing | df_final = df_score_valid[df_score_valid.columns.difference(['Keep', 'Category'])] #remove columns'Keep' and 'Category'
df_category = df_score_valid['Category'].reset_index(drop=True)
msk = np.random.rand(len(df_final)) < 0.8
train_x = np.nan_to_num(df_final[msk])
test_x = np.nan_to_num(df_final[~msk])
train_y = df_c... | _____no_output_____ | MIT | Codes/2.2 Remove redundant words - SVM,KNN,Kmeans_v2.ipynb | matchlesswei/application_project_nlp_company_description |
Perform Linear SVM | from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
#use svm classifier to classify TF-IDF of each website
def linear_svc_classifier(train_x, train_y, test_x, test_y):
print("start svm")
classifier_svm = svm.LinearSVC()
classifier_svm.fit(train_x, train_y)
predictions = ... | start svm
[[145 4 24 2 3 5 66]
[ 4 28 16 0 3 7 5]
[ 24 7 115 1 8 2 30]
[ 6 0 0 21 0 2 3]
[ 8 5 6 1 135 4 15]
[ 15 3 6 3 1 45 9]
[ 68 4 32 1 6 9 225]]
precision recall f1-score support
BUSINESS &... | MIT | Codes/2.2 Remove redundant words - SVM,KNN,Kmeans_v2.ipynb | matchlesswei/application_project_nlp_company_description |
Perform KNN with 5 Neighbours | from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
#use knn classifier to classify TF-IDF of each website
def knn_classifier(x_train, y_train, x_test, y_test):
print("start knn")
modelknn = KNeighborsClassifier(n_neighbors=5)
modelknn.fit(x_train, y_train)
predictions =... | start knn
[[153 7 22 4 5 4 54]
[ 8 25 19 1 3 2 5]
[ 33 17 89 1 12 2 33]
[ 10 1 0 16 2 0 3]
[ 18 5 8 0 133 3 7]
[ 21 4 4 4 2 34 13]
[106 6 40 2 8 7 176]]
precision recall f1-score support
BUSINESS &... | MIT | Codes/2.2 Remove redundant words - SVM,KNN,Kmeans_v2.ipynb | matchlesswei/application_project_nlp_company_description |
Perform K means and Plot SSE, PCA and TSNE | from sklearn.cluster import MiniBatchKMeans
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
import matplotlib.cm as cm
import itertools
#Find the optimal clusters from 2 to maximum of clusters of data group, plot respective SSE.
def fi... | Fit 2 clusters
Fit 4 clusters
Fit 6 clusters
Cluster 0
devices,storage,application,performance,networks,infrastructure,enterprise,solution,wireless,network
Cluster 1
reserved,click,read,learn,world,copyright,need,home,wordpress,domain
Cluster 2
dr,healthcare,treatment,cancer,care,health,patient,medical,clinical,pati... | MIT | Codes/2.2 Remove redundant words - SVM,KNN,Kmeans_v2.ipynb | matchlesswei/application_project_nlp_company_description |
Carpetplots | import opengrid as og
from opengrid.library import plotting as og_plot
import pandas as pd
from joule import meta, filter_meta
plt = og.plot_style()
#%matplotlib notebook
#%matplotlib notebook
for building in meta['RecordNumber'].unique():
ts = pd.read_pickle('data/Electricity_{}.pkl'.format(building)).sum(axis=1)*... | _____no_output_____ | Apache-2.0 | Carpet.ipynb | saroele/jouleboulevard |
Zbozinek TD, Perez OD, Wise T, Fanselow M, & Mobbs D | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from theano import scan
import theano.tensor as T
import pymc3 as pm
import theano
import seaborn as sns
import os, sys, subprocess | _____no_output_____ | Apache-2.0 | modeling/modeling code/Experiment_2_Direct_Associations.ipynb | tzbozinek/2nd-order-occasion-setting |
Load Data | data = pd.read_csv(os.path.join('../data/', "2nd_POS_Modeling_Data_Direct_Associations.csv"))
data['DV'] = ((data['DV'].values - 1) / 2) - 1
observed_R = data.pivot(columns = 'ID', index = 'trialseq', values = 'DV').values[:, np.newaxis, :] #values.T transposes the data, so you can make trials the first dimension or p... | _____no_output_____ | Apache-2.0 | modeling/modeling code/Experiment_2_Direct_Associations.ipynb | tzbozinek/2nd-order-occasion-setting |
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