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4,200 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step2: Object reconstruction from a cloud of points in 2D using $\alpha$-shapes and Vietoris-Rips complexes
Step4: Now construct $\alpha$-shapes and Vietoris-Rips complex from this cloud of... | Python Code:
import dionysus
import math
from random import random
from matplotlib import pyplot
def generate_circle(n, radius, max_noise):
Generate n points on a sphere with the center in the point (0,0)
with the given radius.
Noise is added so that the distance from
the generated point to ... |
4,201 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Word Embeddings
Learning Objectives
You will learn
Step1: This notebook uses TF2.x.
Please check your tensorflow version using the cell below.
Step2: Download the IMDb Dataset
You will use... | Python Code:
# Use the chown command to change the ownership of repository to user.
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
import io
import os
import re
import shutil
import string
import tensorflow as tf
from datetime import datetime
from tensorflow.keras import Model, Sequential
from tenso... |
4,202 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Key Requirements for the iRF scikit-learn implementation
The following is a documentation of the main requirements for the iRF implementation
Pseudocode iRF implementation
Step 0
Step1: Ste... | Python Code:
# Setup
%matplotlib inline
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.datasets import load_iris
from sklearn import... |
4,203 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
TFLearn Subject Verb Agreement Error Detection 2
This notebook is based off the original fragment detection notebook, but specific to detection of participle phrase fragments.
As our trainin... | Python Code:
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
import spacy
import re
from textstat.textstat import textstat
from pattern.en import lexeme, tenses
from pattern.en import pluralize, singularize
import sqlite3
import hashlib
nlp = spacy.load('en_core_w... |
4,204 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
On this notebook the initial steps towards solving the capstone project will be taken. Some data gathering and others...
Step1: Getting the data
Step2: So, Google has a limit of 15 years o... | Python Code:
import yahoo_finance
import requests
import datetime
def print_unix_timestamp_date(timestamp):
print(
datetime.datetime.fromtimestamp(
int(timestamp)
).strftime('%Y-%m-%d %H:%M:%S')
)
print_unix_timestamp_date("1420077600")
print_unix_timestamp_date("1496113200")
EXAMPLE... |
4,205 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Day 1
Step1: Question 1
Find the two entries that sum to 2020 and then multiply those two numbers together.
Step2: Question 2
What is the product of the three entries that sum to 2020? | Python Code:
input_f = './input.txt'
# Read expenses
expenses = set()
with open(input_f, 'r') as fd:
for line in fd:
expenses.add(int(line.strip()))
Explanation: Day 1
End of explanation
# Find 2 expenses that add up to 2020 and get their product
stop = 0
for exp1 in expenses:
for exp2 in expenses:
... |
4,206 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Regression Week 1
Step1: Load house sales data
Dataset is from house sales in King County, the region where the city of Seattle, WA is located.
Step2: Split data into training and testing
... | Python Code:
import graphlab
Explanation: Regression Week 1: Simple Linear Regression
In this notebook we will use data on house sales in King County to predict house prices using simple (one input) linear regression. You will:
* Use graphlab SArray and SFrame functions to compute important summary statistics
* Write a... |
4,207 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Load in the stopwords file. These are common words which we wish to exclude when performing comparisons (a, an, the, etc). Every word is separated by a new line.
Step1: Load in the data fro... | Python Code:
stopWordsFile = "en.txt"
with open(stopWordsFile) as f:
stoplist = [x.strip('\n') for x in f.readlines()]
Explanation: Load in the stopwords file. These are common words which we wish to exclude when performing comparisons (a, an, the, etc). Every word is separated by a new line.
End of explanation
# h... |
4,208 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Taller de Python - Estadística en Física Experimental - 1er día
Esta presentación/notebook está disponible
Step1: Aquí hemos guardado en un espacio de memoria llamado por nosotros "x" la in... | Python Code:
x = 5
y = 'Hola mundo!'
z = [1,2,3]
Explanation: Taller de Python - Estadística en Física Experimental - 1er día
Esta presentación/notebook está disponible:
Repositorio Github FIFA BsAs (para descargarlo, usen el botón raw o hagan un fork del repositorio)
Página web de talleres FIFA BsAs
Programar ¿con qué... |
4,209 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Delay Embedding and the MFPT
Here, we give an example script, showing the effect of Delay Embedding on a Brownian motion on the Muller-Brown potential, projeted onto its y-axis. This script... | Python Code:
import matplotlib.pyplot as plt
import numpy as np
import pyedgar
from pyedgar.data_manipulation import tlist_to_flat, flat_to_tlist, delay_embed, lift_function
%matplotlib inline
Explanation: Delay Embedding and the MFPT
Here, we give an example script, showing the effect of Delay Embedding on a Brownian ... |
4,210 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
LAB 4c
Step1: Set your bucket
Step2: Verify CSV files exist
In the seventh lab of this series 1b_prepare_data_babyweight, we sampled from BigQuery our train, eval, and test CSV files. Veri... | Python Code:
import datetime
import os
import shutil
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
print(tf.__version__)
Explanation: LAB 4c: Create Keras Wide and Deep model.
Learning Objectives
Set CSV Columns, label column, and column defaults
Make dataset of features and label from CSV... |
4,211 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
A Detailed RBC Model Example
Consider the equilibrium conditions for a basic RBC model without labor
Step1: Initializing the model in linearsolve
To initialize the model, we need to first s... | Python Code:
# Import numpy, pandas, linearsolve, matplotlib.pyplot
import numpy as np
import pandas as pd
import linearsolve as ls
import matplotlib.pyplot as plt
plt.style.use('classic')
%matplotlib inline
Explanation: A Detailed RBC Model Example
Consider the equilibrium conditions for a basic RBC model without labo... |
4,212 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Machine Learning Engineer Nanodegree
Introduction and Foundations
Project 0
Step1: From a sample of the RMS Titanic data, we can see the various features present for each passenger on the s... | Python Code:
import numpy as np
import pandas as pd
# RMS Titanic data visualization code
from titanic_visualizations import survival_stats
from IPython.display import display
%matplotlib inline
# Load the dataset
in_file = 'titanic_data.csv'
full_data = pd.read_csv(in_file)
# Print the first few entries of the RMS Ti... |
4,213 | Given the following text description, write Python code to implement the functionality described.
Description:
Program to find the Excenters of a Triangle
Python3 program for the above approach ; Function to calculate the distance between a pair of points ; Function to calculate the coordinates of the excenters of a tr... | Python Code:
from math import sqrt
def distance(m , n , p , q ) :
return(sqrt(pow(n - m , 2 ) + pow(q - p , 2 ) * 1.0 ) )
def Excenters(x1 , y1 , x2 , y2 , x3 , y3 ) :
a = distance(x2 , x3 , y2 , y3 )
b = distance(x3 , x1 , y3 , y1 )
c = distance(x1 , x2 , y1 , y2 )
excenter =[[ 0 , 0 ] for i in range... |
4,214 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sentiment analysis of match thread
To run this yourself. Two things have to be done manually
Step1: Some parameters
These need to be changed every match
Step2: More parameters
These parame... | Python Code:
import praw
import datetime
import pandas as pd
import nltk.sentiment.vader
import matplotlib.pyplot as plt
# Import all relevant packages
from bs4 import BeautifulSoup
from selenium import webdriver
import numpy as np
import os
Explanation: Sentiment analysis of match thread
To run this yourself. Two thin... |
4,215 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Keras debugging tips
Author
Step1: Now, rather than using it in a end-to-end model directly, let's try to call the layer on
some test data
Step2: We get the following
Step3: Now our code ... | Python Code:
import tensorflow as tf
from tensorflow.keras import layers
class MyAntirectifier(layers.Layer):
def build(self, input_shape):
output_dim = input_shape[-1]
self.kernel = self.add_weight(
shape=(output_dim * 2, output_dim),
initializer="he_normal",
nam... |
4,216 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
PDF is garbage
In this example, we are looking for a link to some source code
Step1: PDF is garbage, continued
If we remove line breaks to fix URLs that have been wrapped, we discover
that... | Python Code:
urlre = re.compile( '(?P<url>https?://[^\s]+)' )
for page in doc :
print urlre.findall( page )
Explanation: PDF is garbage
In this example, we are looking for a link to some source code :
http://prodege.jgi-psf.org//downloads/src
However, in the PDF, the URL is line wrapped, so the src is lost.
End of ... |
4,217 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Topic Modeling with MALLET
We'd like to test how Taylor Salo integrated MALLET into NeuroSynth, and whether that integration works in a docker container.
First, let's import some dependencie... | Python Code:
from bs4 import BeautifulSoup
import pandas as pd
with open('../neurosynth/tests/data/yarkoni_pubmed.xml') as infile:
xml_file = infile.read()
soup = BeautifulSoup(xml_file, 'lxml')
try:
assert type(soup) == BeautifulSoup
except AssertionError:
print('Check file type! Must be HTML or XML.')
tit... |
4,218 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
CollateX and XML, Part 2
David J. Birnbaum (djbpitt@gmail.com, http
Step1: The WitnessSet class represents al... | Python Code:
from collatex import *
from lxml import etree
import json,re
Explanation: CollateX and XML, Part 2
David J. Birnbaum (djbpitt@gmail.com, http://www.obdurodon.org), 2015-06-29
This example collates a single line of XML from fou... |
4,219 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2020 The TensorFlow Hub Authors.
Step1: <table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step2: You will use the AdamW optimizer from t... | Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# dist... |
4,220 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Working With Sessions
Import the LArray library
Step1: Three Kinds Of Sessions
They are three ways to group objects in LArray
Step2: CheckedSession
The syntax to define a checked-session i... | Python Code:
%xmode Minimal
from larray import *
Explanation: Working With Sessions
Import the LArray library:
End of explanation
# define some scalars, axes and arrays
variant = 'baseline'
country = Axis('country=Belgium,France,Germany')
gender = Axis('gender=Male,Female')
time = Axis('time=2013..2017')
population = z... |
4,221 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This notebook is part of the clifford documentation
Step1: We'll create copies of the point and line reflected in the circle, using $X = C\hat X\tilde C$, where $\hat X$ is the grade involu... | Python Code:
from clifford.g2c import *
point = up(2*e1+e2)
line = up(3*e1 + 2*e2) ^ up(3*e1 - 2*e2) ^ einf
circle = up(e1) ^ up(-e1 + 2*e2) ^ up(-e1 - 2*e2)
Explanation: This notebook is part of the clifford documentation: https://clifford.readthedocs.io/.
Visualization tools
In this example we will look at some exter... |
4,222 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Parsing tsv files and populating the database
Step1: Inspecting the first few lines of the file, we get a feel for this data schema.
Mongo Considerations
Step2: |Number|Name| Name | Positi... | Python Code:
osu_roster_filepath = '../data/osu_roster.csv'
Explanation: Parsing tsv files and populating the database
End of explanation
!head {osu_roster_filepath}
Explanation: Inspecting the first few lines of the file, we get a feel for this data schema.
Mongo Considerations:
- can specify categories for validation... |
4,223 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
NER using Data Programming
Project Mars Target Encyclopedia
This notebook does not explain much, however, the exaplanations are found in the original notebook(s) https
Step2: Load all data ... | Python Code:
%load_ext autoreload
%autoreload 2
%matplotlib inline
from snorkel import SnorkelSession
import os
import numpy as np
import re, string
import codecs
# Open Session
session = SnorkelSession()
Explanation: NER using Data Programming
Project Mars Target Encyclopedia
This notebook does not explain much, howev... |
4,224 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Two-Level
Step2: We'll just check that the pulse area is what we want.
Step3: Solve the Problem
Step4: Plot Output
Step5: Analysis
The $6 \pi$ sech pulse breaks up into three $2 \... | Python Code:
import numpy as np
SECH_FWHM_CONV = 1./2.6339157938
t_width = 1.0*SECH_FWHM_CONV # [τ]
print('t_width', t_width)
mb_solve_json =
{
"atom": {
"fields": [
{
"coupled_levels": [[0, 1]],
"rabi_freq_t_args": {
"n_pi": 6.0,
"centre": 0.0,
"width": %f
... |
4,225 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Spectral Line Data Cubes in Astronomy - Part 1
In this notebook we will introduce spectral line data cubes in astronomy. They are a convenient way to store many spectra at points in the sky.... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
Explanation: Spectral Line Data Cubes in Astronomy - Part 1
In this notebook we will introduce spectral line data cubes in astronomy. They are a convenient way to store many spectra at points in the sky. Much like having a spectrum at every pixel in a CCD.... |
4,226 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2018 The TensorFlow Authors.
Step1: テンソルと演算
<table class="tfo-notebook-buttons" align="left">
<td> <a target="_blank" href="https
Step2: テンソル
テンソルは多次元の配列です。NumPy ndarray オブ... | Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# dist... |
4,227 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Using deep features to build an image classifier
Fire up GraphLab Create
Step1: Load a common image analysis dataset
We will use a popular benchmark dataset in computer vision called CIFAR-... | Python Code:
import graphlab
Explanation: Using deep features to build an image classifier
Fire up GraphLab Create
End of explanation
image_train = graphlab.SFrame('image_train_data/')
image_test = graphlab.SFrame('image_test_data/')
Explanation: Load a common image analysis dataset
We will use a popular benchmark data... |
4,228 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Matérn Spectral Mixture (MSM) kernel
Gaussian process priors for pitch detection in polyphonic music
Learning kernels in frequency domain
Written by Pablo A. Alvarado, Centre for Digital Mus... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (16, 4)
import numpy as np
import scipy as sp
import scipy.io as sio
import scipy.io.wavfile as wav
from scipy import signal
from scipy.fftpack import fft
import gpflow
import GPitch
sf, y = wav.rea... |
4,229 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
练习 1：仿照求 ∑mi=1i+∑ni=1i+∑ki=1i∑i=1mi+∑i=1ni+∑i=1ki 的完整代码，写程序，可求m!+n!+k!
Step1: 练习 2：写函数可返回1-1/3+1/5-1/7...的前n项的和。在主程序中，分别令n=1000及100000，打印4倍该函数的和。
Step2: 练习 3：将task3中的练习1及练习4改写为函数，并进行调用。
St... | Python Code:
def product_sum(end):
i = 1
total_n = 1
while i < end:
i += 1
total_n *= i
return total_n
m = int(input("请输入第1个整数，以回车结束:"))
n = int(input("请输入第2个整数，以回车结束:"))
k = int(input("请输入第3个整数，以回车结束:"))
print("最终的和是：",product_sum(m)+product_sum(n)+product_sum(k))
Explanation: 练习 1... |
4,230 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The focus of this notebook is refactoring a loop that
- gets user input
- quits if that input matches some sentinel value
- processes the user input
The interesting part starts around cell #... | Python Code:
from functools import partial
def convert(s):
converters = (int, float)
for converter in converters:
try:
value = converter(s)
except ValueError:
pass
else:
return value
return s
def process_input(s):
value = convert(... |
4,231 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Set up data
We're working with the movielens data, which contains one rating per row, like this
Step1: Just for display purposes, let's read in the movie names too.
Step2: We update the mo... | Python Code:
ratings = pd.read_csv(path+'ratings.csv')
ratings.head()
len(ratings)
Explanation: Set up data
We're working with the movielens data, which contains one rating per row, like this:
End of explanation
movie_names = pd.read_csv(path+'movies.csv').set_index('movieId')['title'].to_dict()
users = ratings.userId.... |
4,232 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Working with HPE IMC API for Custom Views
In this notebook, we will be covering the basics of using the pyhpimc python module to access the RESTFUL interface ( eAPI ) of the HPE IMC Network ... | Python Code:
import csv
import time
from pyhpeimc.auth import *
from pyhpeimc.plat.groups import *
from pyhpeimc.version import *
2+34
Explanation: Working with HPE IMC API for Custom Views
In this notebook, we will be covering the basics of using the pyhpimc python module to access the RESTFUL interface ( eAPI ) of th... |
4,233 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Let's play iPython and BASH a bit
count number of paths in $PATH
Step1: which is the same as the following command in BASH shell
Step2: change the language environment
Step3: look for fil... | Python Code:
path=!echo $PATH
print path
path[0].split(":")
print len(path[0].split(":"))
Explanation: Let's play iPython and BASH a bit
count number of paths in $PATH:
End of explanation
!echo $PATH|tr ":" " "|wc -w
Explanation: which is the same as the following command in BASH shell:
End of explanation
!locale
!expo... |
4,234 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
graded = 8/10
Homework 6
Step1: Problem set #2
Step2: Problem set #3
Step3: Problem set #4
Step4: Problem set #5
Step5: Specifying a field other than name, area or elevation for the sor... | Python Code:
import requests
data = requests.get('http://localhost:5000/lakes').json()
print(len(data), "lakes")
for item in data[:10]:
print(item['name'], "- elevation:", item['elevation'], "m / area:", item['area'], "km^2 / type:", item['type'])
Explanation: graded = 8/10
Homework 6: Web Applications
For this hom... |
4,235 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Logistic Regression with Differential Privacy
We start by importing the required libraries and modules and collecting the data that we need from the Adult dataset.
Step1: Let's also collect... | Python Code:
import diffprivlib.models as dp
import numpy as np
from sklearn.linear_model import LogisticRegression
X_train = np.loadtxt("https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data",
usecols=(0, 4, 10, 11, 12), delimiter=", ")
y_train = np.loadtxt("https://archive... |
4,236 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Testing the trained weight matrices (not in an ensemble)
Step1: Load the weight matrices from the training
Step2: Visualize the digit from one hot representation through the activity weigh... | Python Code:
import nengo
import numpy as np
import cPickle
import matplotlib.pyplot as plt
from matplotlib import pylab
import matplotlib.animation as animation
from scipy import linalg
%matplotlib inline
import scipy.ndimage
Explanation: Testing the trained weight matrices (not in an ensemble)
End of explanation
#Wei... |
4,237 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sebastian Raschka
last updated
Step1: Using the code above, we created two $3\times20$ datasets - one dataset for each class $\omega_1$ and $\omega_2$ -
where each column can be pictured a... | Python Code:
import numpy as np
np.random.seed(0)
mu_vec1 = np.array([0, 0, 0])
cov_mat1 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
class1_sample = np.random.multivariate_normal(mu_vec1, cov_mat1, 20).T
assert class1_sample.shape == (3, 20), "The matrix has not the dimensions 3x20"
mu_vec2 = np.array([1, 1, 1])
cov_... |
4,238 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Using third-party Native Libraries
Sometimes, the functionnality you need are onmy available in third-party native libraries. There's still an opportunity to use them from within Pythran, us... | Python Code:
import pythran
%load_ext pythran.magic
%%pythran
#pythran export pythran_cbrt(float64(float64), float64)
def pythran_cbrt(libm_cbrt, val):
return libm_cbrt(val)
Explanation: Using third-party Native Libraries
Sometimes, the functionnality you need are onmy available in third-party native libraries. Th... |
4,239 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Image Captioning
To perform image captioning we are going to apply an approach similar to the work described in references [1],[2], and [3]. The approach applied here uses a recurrent neural... | Python Code:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import inspect
import time
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.framework import dtypes
#import reader
import collections
imp... |
4,240 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
REPL Basics
<a href="http
Step1: Persistent Storage
NOTE
Step2: Help
To get help for the various classes and their respective methods, run
Step3: To get help on a specific method in that ... | Python Code:
import chip.native
import pkgutil
module = pkgutil.get_loader('chip.ChipReplStartup')
%run {module.path}
Explanation: REPL Basics
<a href="http://35.236.121.59/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fproject-chip%2Fconnectedhomeip&urlpath=lab%2Ftree%2Fconnectedhomeip%2Fdocs%2Fguides%2Fre... |
4,241 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Q1
Step1: astropy convolution
How do you convolve fast?
see, e.g., http
Step2: Speed of DFT
Step3: faster fftw
Step4: Q3
Install a module, then keep editing it.
python setup.py develop
U... | Python Code:
x = StringIO.StringIO()
arr = np.arange(10)
np.savetxt(x,arr, header='test', comments="")
x.seek(0)
print(x.read())
with open('file.txt','w') as f:
f.write(x.getvalue())
%%bash
cat file.txt
Explanation: Q1:
Saving a table to text with a header with no preceding "#"
Also, demo StringIO
End of explanatio... |
4,242 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
如何爬取Facebook粉絲頁資料 (comments) ？
基本上是透過 Facebook Graph API 去取得粉絲頁的資料,但是使用 Facebook Graph API 還需要取得權限,有兩種方法
Step1: 第一步 - 要先取得應用程式的帳號,密碼 (app_id, app_secret)
第二步 - 輸入要分析的粉絲團的 id
[教學]如何申請建立 Fac... | Python Code:
# 載入python 套件
import requests
import datetime
import time
import pandas as pd
Explanation: 如何爬取Facebook粉絲頁資料 (comments) ？
基本上是透過 Facebook Graph API 去取得粉絲頁的資料,但是使用 Facebook Graph API 還需要取得權限,有兩種方法 :
第一種是取得 Access Token
第二種是建立 Facebook App的應用程式,用該應用程式的帳號,密碼當作權限
兩者的差別在於第一種會有時效限制,必須每隔一段時間去更新Access Token,才能使用
A... |
4,243 | Given the following text description, write Python code to implement the functionality described.
Description:
Area of the biggest possible rhombus that can be inscribed in a rectangle
Function to find the area of the biggest rhombus ; the length and breadth cannot be negative ; area of the rhombus ; Driver code
| Python Code:
def rhombusarea(l , b ) :
if(l < 0 or b < 0 ) :
return - 1
return(l * b ) / 2
if __name__== ' __main __' :
l = 16
b = 6
print(rhombusarea(l , b ) )
|
4,244 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Exercício 01
Step1: Exercício 02
Step2: Exercício 03
Step3: Exercício 04 | Python Code:
G2 = nx.barabasi_albert_graph(6,3)
nx.draw_shell(G2)
pos = nx.shell_layout(G2)
labels = dict( enumerate(G2.nodes()) )
nx.draw_networkx_labels(G2,pos,labels,font_size=16);
print "Dist. media: ", nx.average_shortest_path_length(G2)
print "Diametro: ", nx.diameter(G2)
print "Coef. Agrupamento médio: ", nx.ave... |
4,245 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Definition(s)
The Karatsuba algorithm is a fast multiplication algorithm.
It reduces the multiplication of two n-digit numbers to at most ${\displaystyle n^{\log _{2}3}\approx n^{1.585}}$ s... | Python Code:
import numpy as np # used for generating random numbers
Explanation: Definition(s)
The Karatsuba algorithm is a fast multiplication algorithm.
It reduces the multiplication of two n-digit numbers to at most ${\displaystyle n^{\log _{2}3}\approx n^{1.585}}$ single-digit multiplications in general.
End of ... |
4,246 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Two Topics Coupled example
Import Python built-in functions we need to run and plot the game
Step1: Set up inline matplotlib
Step2: Import Game Modules From a Given Path
User have to edit ... | Python Code:
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import matplotlib.image as mpimg
from matplotlib import rcParams
import seaborn as sb
Explanation: Two Topics Coupled example
Import Python built-in functions... |
4,247 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
机器学习纳米学位
非监督学习
项目 3
Step1: 分析数据
在这部分，你将开始分析数据，通过可视化和代码来理解每一个特征和其他特征的联系。你会看到关于数据集的统计描述，考虑每一个属性的相关性，然后从数据集中选择若干个样本数据点，你将在整个项目中一直跟踪研究这几个数据点。
运行下面的代码单元给出数据集的一个统计描述。注意这个数据集包含了6个重要的产品类型：'Fresh', ... | Python Code:
# 检查你的Python版本
from sys import version_info
if version_info.major != 3:
raise Exception('请使用Python 3.x 来完成此项目')
# 引入这个项目需要的库
import numpy as np
import pandas as pd
import visuals as vs
from IPython.display import display # 使得我们可以对DataFrame使用display()函数
# 设置以内联的形式显示matplotlib绘制的图片（在notebook中显示更美观）
%matp... |
4,248 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
How to solve H(div) PDEs in practice?
This document explores the current, easily accessible, state of the art for solving an $H(\rm div) \times L^2$ formulation of Poisson's problem or equiv... | Python Code:
# Import useful libraries
from dolfin import *
import numpy
import pylab
# Plot inline in this notebook
%matplotlib inline
# Set basic optimization parameters for FEniCS
parameters["form_compiler"]["representation"] = "uflacs"
parameters["form_compiler"]["cpp_optimize"] = True
#parameters["plotting_backend... |
4,249 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
NoSQL (Neo4j) (sesión 7)
Esta hoja muestra cómo acceder a bases de datos Neo4j y también a conectar la salida con Jupyter.
Se puede utilizar el propio interfaz de Neo4j también en la direcci... | Python Code:
from pprint import pprint as pp
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline
matplotlib.style.use('ggplot')
Explanation: NoSQL (Neo4j) (sesión 7)
Esta hoja muestra cómo acceder a bases de datos Neo4j y también a conectar la salida con Jupyter.
Se puede utilizar e... |
4,250 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
2
Step1: 4
Step2: 5
Step3: 6 | Python Code:
f = open("dq_unisex_names.csv", "r")
data = f.read()
print(data)
Explanation: 2: Unisex names
3: Read the file into string
Instructions
Use the open() function to return a File object with the parameters:
r for read mode
dq_unisex_names.csv for the file name
Then use the read() method of the File object to... |
4,251 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<!--BOOK_INFORMATION-->
<img align="left" style="padding-right
Step1: As we have seen several times throughout this section, the simplest colorbar can be created with the plt.colorbar funct... | Python Code:
import matplotlib.pyplot as plt
plt.style.use('classic')
%matplotlib inline
import numpy as np
Explanation: <!--BOOK_INFORMATION-->
<img align="left" style="padding-right:10px;" src="figures/PDSH-cover-small.png">
This notebook contains an excerpt from the Python Data Science Handbook by Jake VanderPlas; t... |
4,252 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Chapter 2
Step1: In this case instead loading a geo_data object directly, we will create one. The main atributes we need to pass are
Step2: You can visualize the points in 3D (work in prog... | Python Code:
# These two lines are necessary only if gempy is not installed
import sys, os
sys.path.append("../")
# Importing gempy
import gempy as gp
# Embedding matplotlib figures into the notebooks
%matplotlib inline
# Aux imports
import numpy as np
Explanation: Chapter 2: A real example. Importing data and setting ... |
4,253 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Examples and Exercises from Think Stats, 2nd Edition
http
Step1: Examples
One more time, I'll load the data from the NSFG.
Step2: And compute the distribution of birth weight for first bab... | Python Code:
from __future__ import print_function, division
%matplotlib inline
import numpy as np
import nsfg
import first
import thinkstats2
import thinkplot
Explanation: Examples and Exercises from Think Stats, 2nd Edition
http://thinkstats2.com
Copyright 2016 Allen B. Downey
MIT License: https://opensource.org/lice... |
4,254 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Exercise 8 | Anomaly Detection
Step1: Part 1
Step2: Part 2
Step3: Visualize the fit.
Step4: Part 3
Step5: Best epsilon and F1 found using cross-validation (F1 should be about 0.899e-5)
... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import scipy.io
from scipy.stats import multivariate_normal
%matplotlib inline
#%qtconsole
Explanation: Exercise 8 | Anomaly Detection
End of explanation
ex7data1 = scipy.io.loadmat('ex8data1.mat')
X = ex7data1['X']
Xval = ex7data1['Xval']
yval = ex7data1[... |
4,255 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Benchmarking Performance and Scaling of Python Clustering Algorithms
There are a host of different clustering algorithms and implementations thereof for Python. The performance and scaling c... | Python Code:
import hdbscan
import debacl
import fastcluster
import sklearn.cluster
import scipy.cluster
import sklearn.datasets
import numpy as np
import pandas as pd
import time
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
sns.set_context('poster')
sns.set_palette('Paired', 10)
sns.set_col... |
4,256 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Chapter 7
Step1: Here, range() will return a number of integers, starting from zero, up to (but not including) the number which we pass as an argument to the function. Using range() is of c... | Python Code:
for i in range(10):
print(i)
Explanation: Chapter 7: More on Loops
In the previous chapters we have often discussed the powerful concept of looping in Python. Using loops, we can easily repeat certain actions when coding. With for-loops, for instance, it is really easy to visit the items in a list in a... |
4,257 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Efficient Computation of Powers
The function power takes two natural numbers $m$ and $n$ and computes $m^n$. Our first implementation is inefficient and takes $n-1$ multiplication to comput... | Python Code:
def power(m, n):
r = 1
for i in range(n):
r *= m
return r
power(2, 3), power(3, 2)
%%time
p = power(3, 500000)
p
Explanation: Efficient Computation of Powers
The function power takes two natural numbers $m$ and $n$ and computes $m^n$. Our first implementation is inefficient and takes $... |
4,258 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lecture 13
Step1: With NumPy arrays, all the same functionality you know and love from lists is still there.
Step2: These operations all work whether you're using Python lists or NumPy arr... | Python Code:
li = ["this", "is", "a", "list"]
print(li)
print(li[1:3]) # Print element 1 (inclusive) to 3 (exclusive)
print(li[2:]) # Print element 2 and everything after that
print(li[:-1]) # Print everything BEFORE element -1 (the last one)
Explanation: Lecture 13: Array Indexing, Slicing, and Broadcasting
CSCI 1... |
4,259 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Inspect Raw Netcdf
Playing around with efficient ways to merge and view netcdf data from the tower. This ipython notebook depends on the python script of the same name.
Step1: Using the xra... | Python Code:
usr = 'Julia'
FILEDIR = 'C:/Users/%s/Dropbox (PE)/KenyaLab/Data/Tower/TowerData/'%usr
NETCDFLOC = FILEDIR + 'raw_netcdf_output/'
DATALOC = 'F:/towerdata/'
Explanation: Inspect Raw Netcdf
Playing around with efficient ways to merge and view netcdf data from the tower. This ipython notebook depends on the py... |
4,260 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Bayesian Parametric Regression
Notebook version
Step1: A quick note on the mathematical notation
In this notebook we will make extensive use of probability distributions. In general, we wil... | Python Code:
# Import some libraries that will be necessary for working with data and displaying plots
# To visualize plots in the notebook
%matplotlib inline
from IPython import display
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import scipy.io # To read matlab files
import pylab
imp... |
4,261 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction To Probabalistic Graph Models
Scott Hendrickson
2016-Aug-19
Requirements
Step1: Why is this formalism a useful probabalistic problem solving tool?
This tool can model a much ge... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import networkx as nx
G=nx.DiGraph()
G.add_edge('sex','height',weight=0.6)
nx.draw_networkx(G, node_color='y',node_size=2000, width=3)
plt.axis('off')
plt.show()
Explanation: Introduction To Probabalistic Graph Models
Scott Hendrickson
2016-Aug-19
Requirem... |
4,262 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Ok, we've had a little peek at our dataset, lets prep it for our model.
Step1: Prep is done, time for the model.
Step2: We've defined the cost and accuracy functions, time to train our mod... | Python Code:
randinds = np.random.permutation(len(digits.target))
# shuffle the values
from sklearn.utils import shuffle
data, targets = shuffle(digits.data, digits.target, random_state=0)
# scale the data
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler().fit(data)
data_scaled = scaler.transfor... |
4,263 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Compute and visualize ERDS maps
This example calculates and displays ERDS maps of event-related EEG data. ERDS
(sometimes also written as ERD/ERS) is short for event-related
desynchronizatio... | Python Code:
# Authors: Clemens Brunner <clemens.brunner@gmail.com>
# Felix Klotzsche <klotzsche@cbs.mpg.de>
#
# License: BSD-3-Clause
Explanation: Compute and visualize ERDS maps
This example calculates and displays ERDS maps of event-related EEG data. ERDS
(sometimes also written as ERD/ERS) is short for eve... |
4,264 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
https
Step1: Request 2
Step2: Request 3
Step3: Request 4
Step4: On a side note | Python Code:
fullbase = requests.compat.urljoin(baseurl, endpoint_datatypes)
r = requests.get(
fullbase,
headers=custom_headers,
# params={'limit':1000},
params={'limit':1000, 'datasetid':"NORMAL_DLY"},
)
r.headers
r.text
json.loads(r.text)
Explanation: https://www.ncdc.noaa.gov/cdo-web/api/v2/data?data... |
4,265 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Neural Network Part2
Step1: Normalization
Q1. Apply l2_normalize to x.
Step2: Q2. Calculate the mean and variance of x based on the sufficient statistics.
Step3: Q3. Calculate the mean an... | Python Code:
from __future__ import print_function
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
%matplotlib inline
from datetime import date
date.today()
author = "kyubyong. https://github.com/Kyubyong/tensorflow-exercises"
tf.__version__
np.__version__
Explanation: Neural Network Part2
En... |
4,266 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Catching errors and unit tests
In this tutorial are few examples how catch error and how to perform unit tests in Python.
When you code in Python, keep in mind
Step1: An example of correct ... | Python Code:
def sum_together1(a, b):
return a + b
Explanation: Catching errors and unit tests
In this tutorial are few examples how catch error and how to perform unit tests in Python.
When you code in Python, keep in mind:
Errors should never pass silently. Unless explicitly silenced. (<a href="https://www.python... |
4,267 | Given the following text description, write Python code to implement the functionality described.
Description:
Number of triangles that can be formed with given N points
Python3 implementation of the above approach ; This function returns the required number of triangles ; Hash Map to store the frequency of slope corre... | Python Code:
from collections import defaultdict
from math import gcd
def countTriangles(P , N ) :
mp = defaultdict(lambda : 0 )
ans = 0
for i in range(0 , N ) :
mp . clear()
for j in range(i + 1 , N ) :
X = P[i ][0 ] - P[j ][0 ]
Y = P[i ][1 ] - P[j ][1 ]
g = gcd(X , Y )
X //= g
Y //= g
mp ... |
4,268 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
분류(classification) 성능 평가
분류 문제는 회귀 분석과 달리 모수에 대한 t-검정, 신뢰 구간(confidence interval) 추정 등이 쉽지 않기 때문에 이를 보완하기 위해 다양한 성능 평가 기준이 필요하다.
Scikit-Learn 에서 지원하는 분류 성능 평가 명령
sklearn.metrics 서브 패키지
confu... | Python Code:
from sklearn.metrics import confusion_matrix
y_true = [2, 0, 2, 2, 0, 1]
y_pred = [0, 0, 2, 2, 0, 2]
confusion_matrix(y_true, y_pred)
y_true = ["cat", "ant", "cat", "cat", "ant", "bird"]
y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"]
confusion_matrix(y_true, y_pred, labels=["ant", "bird", "cat"])
Expl... |
4,269 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Buildings / Addresses
I want to understand how buildings and addresses are represented in OSM data.
Required reading
Step1: Nodes tagged as buildings / with addresses
For the Isle of Wight ... | Python Code:
import osmdigest.digest as digest
Explanation: Buildings / Addresses
I want to understand how buildings and addresses are represented in OSM data.
Required reading: http://wiki.openstreetmap.org/wiki/Addresses
End of explanation
import os
#filename = os.path.join("//media", "disk", "OSM_Data", "isle-of-wig... |
4,270 | Given the following text description, write Python code to implement the functionality described.
Description:
Count of subarrays which forms a permutation from given Array elements
Function returns the required count ; Store the indices of the elements present in A [ ] . ; Store the maximum and minimum index of the el... | Python Code:
def PermuteTheArray(A , n ) :
arr =[0 ] * n
for i in range(n ) :
arr[A[i ] - 1 ] = i
mini = n
maxi = 0
count = 0
for i in range(n ) :
mini = min(mini , arr[i ] )
maxi = max(maxi , arr[i ] )
if(maxi - mini == i ) :
count += 1
return count
if __name__== "__main __":
A... |
4,271 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2019 DeepMind Technologies Limited.
Step1: Environments
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step3: Stack Before Writing
Th... | Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# dist... |
4,272 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Iris Dataset
From Wikipedia
Step1: read_html
Wikipedia has the same dataset as a html table at https
Step2: Plotting
Let's use pandas to plot the sepal_length vs the petal_length.
Step3: ... | Python Code:
import pandas as pd
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
df = pd.read_csv(url,names=['sepal_length',
'sepal_width',
'petal_length',
'petal_width',
'spe... |
4,273 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
통계적 사고 (2판) 연습문제 (thinkstats2.com, think-stat.xwmooc.org)<br>
Allen Downey / 이광춘(xwMOOC)
Step1: 연습문제 5.1
BRFSS 데이터셋에서 (5.4절 참조), 신장 분포는 대략 남성에 대해 모수 µ = 178 cm, σ = 7.7cm을 갖는 정규분포이며, 여성에 대해... | Python Code:
from __future__ import print_function, division
import thinkstats2
import thinkplot
%matplotlib inline
Explanation: 통계적 사고 (2판) 연습문제 (thinkstats2.com, think-stat.xwmooc.org)<br>
Allen Downey / 이광춘(xwMOOC)
End of explanation
import scipy.stats
Explanation: 연습문제 5.1
BRFSS 데이터셋에서 (5.4절 참조), 신장 분포는 대략 남성에 대해 모... |
4,274 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Machine Learning Engineer Nanodegree
Model Evaluation & Validation
Project 1
Step1: Data Exploration
In this first section of this project, you will make a cursory investigation about the B... | Python Code:
# Import libraries necessary for this project
import numpy as np
import pandas as pd
import visuals as vs # Supplementary code
from sklearn.cross_validation import ShuffleSplit
# Pretty display for notebooks
%matplotlib inline
# Load the Boston housing dataset
data = pd.read_csv('housing.csv')
prices = dat... |
4,275 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2019 The TensorFlow Authors.
Step1: 映画レビューのテキスト分類
<table class="tfo-notebook-buttons" align="left">
<td> <a target="_blank" href="https
Step2: 感情分析
このノートブックでは、映画レビューのテキストを使... | Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# dist... |
4,276 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
3D Shape Classification with Sublevelset Filtrations
In this module, we will explore how TDA can be used to classify 3D shapes. We will begine by clustering triangle meshes of humans in dif... | Python Code:
import numpy as np
%matplotlib notebook
import scipy.io as sio
from scipy import sparse
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import sys
sys.path.append("pyhks")
from HKS import *
from GeomUtils import *
from ripser import ripser
from persim import plot_diagrams, wassers... |
4,277 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Create datasets for the Content-based Filter
This notebook builds the data we will use for creating our content based model. We'll collect the data via a collection of SQL queries from the p... | Python Code:
import os
import tensorflow as tf
import numpy as np
from google.cloud import bigquery
PROJECT = 'cloud-training-demos' # REPLACE WITH YOUR PROJECT ID
BUCKET = 'cloud-training-demos-ml' # REPLACE WITH YOUR BUCKET NAME
REGION = 'us-central1' # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
# do not chang... |
4,278 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Intro to pychord
Create a Chord
Step1: Transpose a Chord
Step2: Get Component Notes
Step3: Find Chords
Step4: Chord Progressions
Step5: Create a Chord from Note Index in a Scale
Step6: ... | Python Code:
c = mus.Chord("Am7")
print(c.info())
Explanation: Intro to pychord
Create a Chord
End of explanation
c = mus.Chord("C")
c.transpose(2)
c
c = mus.Chord("Dm/G")
c.transpose(3)
c
Explanation: Transpose a Chord
End of explanation
c = mus.Chord("Dm7")
c.components()
Explanation: Get Component Notes
End of expla... |
4,279 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href='http
Step1: Data
Seaborn comes with built-in data sets!
Step2: distplot
The distplot shows the distribution of a univariate set of observations.
Step3: To remove the kde layer an... | Python Code:
import seaborn as sns
%matplotlib inline
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
Distribution Plots
Let's discuss some plots that allow us to visualize the distribution of a data set. These plots are:
distplot
jointplot
pairplot
rugplot
kdeplot
Imports
... |
4,280 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Performing Clean-up and Analysis on Native Ad Data Scraped "From Around the Web"
Step1: Data Load and Cleaning
Step2: As a side note, the headlines from zergnet all have some newlines we n... | Python Code:
import pandas as pd
from datetime import datetime
import dateutil
import matplotlib.pyplot as plt
from IPython.core.display import display, HTML
import re
from urllib.parse import urlparse
import json
Explanation: Performing Clean-up and Analysis on Native Ad Data Scraped "From Around the Web"
End of expla... |
4,281 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Machine Learning Engineer Nanodegree
Introduction and Foundations
Project 0
Step1: From a sample of the RMS Titanic data, we can see the various features present for each passenger on the s... | Python Code:
import numpy as np
import pandas as pd
# RMS Titanic data visualization code
from titanic_visualizations import survival_stats
from IPython.display import display
%matplotlib inline
# Load the dataset
in_file = 'titanic_data.csv'
full_data = pd.read_csv(in_file)
# Print the first few entries of the RMS Ti... |
4,282 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Using sklearn's Iris Dataset with neon
Tony Reina<br>
28 JUNE 2017
Here's an example of how we can load one of the standard sklearn datasets into a neon model. We'll be using the iris datase... | Python Code:
from sklearn import datasets
iris = datasets.load_iris()
X = iris.data
Y = iris.target
nClasses = len(iris.target_names) # Setosa, Versicolour, and Virginica iris species
Explanation: Using sklearn's Iris Dataset with neon
Tony Reina<br>
28 JUNE 2017
Here's an example of how we can load one of the stand... |
4,283 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Addition Similarity
Step1: Question
Step2: Top 10 most similar additions
Step3: 10 Least Similar additions
Step4: Similarity of a specific combo
Step5: But is that good or bad? How does... | Python Code:
# Import libraries
import numpy as np
import pandas as pd
# Import the data
import WTBLoad
wtb = WTBLoad.load()
Explanation: Addition Similarity
End of explanation
import math
# Square the difference of each row, and then return the mean of the column.
# This is the average difference between the two.
# I... |
4,284 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lab 6.2 - Using a pre-trained model with Keras
In this section of the lab, we will load the model we trained in the previous section, along with the training data and mapping dictionaries, a... | Python Code:
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import LSTM
from keras.callbacks import ModelCheckpoint
from keras.utils import np_utils
import sys
import re
import pickle
Explanation: Lab 6.2 - Using a pre-trained mod... |
4,285 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Computing argmax in Python
Which fruit is the most frequent in this basket?
Step1: Returns a tuple, let's get its first element.
Step2: Most common element
Which item appears most times in... | Python Code:
basket = [("apple", 12), ("pear", 3), ("plum", 14)]
max(basket, key=lambda pair: pair[1])
Explanation: Computing argmax in Python
Which fruit is the most frequent in this basket?
End of explanation
max(basket, key=lambda pair: pair[1])[0]
Explanation: Returns a tuple, let's get its first element.
End of ex... |
4,286 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Modular neural nets
In the previous exercise, we computed the loss and gradient for a two-layer neural network in a single monolithic function. This isn't very difficult for a small t... | Python Code:
# As usual, a bit of setup
import numpy as np
import matplotlib.pyplot as plt
from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient
from cs231n.layers import *
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.... |
4,287 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Dimensionality of the inputs to the filter
One of the main strengths of PyMC3 is its dependence on Theano. Theano allows to compute arithmetic operations on arbitrary tensors. This might not... | Python Code:
import numpy as np
import theano
import theano.tensor as tt
import kalman
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style("whitegrid")
%matplotlib inline
# True values
T = 500 # Time steps
sigma2_eps0 = 3 # Variance of the observatio... |
4,288 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Part 6 - Data Retrieval Functions
Step1: Data Retrieval
globus_download
If you want to access the raw data underlying entries in MDF, you can use globus_download() and provide the results f... | Python Code:
from mdf_forge.forge import Forge
mdf = Forge()
Explanation: Part 6 - Data Retrieval Functions
End of explanation
# NBVAL_SKIP
# Running this example will save a file in the current directory.
res = mdf.search("dft.converged:true AND mdf.resource_type:record", limit=10)
mdf.globus_download(res)
Explanation... |
4,289 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Basic damage detection in Wikipedia
This notebook demonstrates the basic contruction of a vandalism classification system using the revscoring library that we have developed specifically for... | Python Code:
# Magical ipython notebook stuff puts the result of this command into a variable
revids_f = !wget http://quarry.wmflabs.org/run/65415/output/0/tsv?download=true -qO-
revids = [int(line) for line in revids_f[1:]]
len(revids)
Explanation: Basic damage detection in Wikipedia
This notebook demonstrates the ba... |
4,290 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Python Machine Learning 2nd Edition by Sebastian Raschka, Packt Publishing Ltd. 2017
Code Repository
Step1: The use of watermark is optional. You can install this IPython extension via "pip... | Python Code:
%load_ext watermark
%watermark -a "Sebastian Raschka" -u -d -v -p numpy,pandas,sklearn,nltk
Explanation: Python Machine Learning 2nd Edition by Sebastian Raschka, Packt Publishing Ltd. 2017
Code Repository: https://github.com/rasbt/python-machine-learning-book-2nd-edition
Code License: MIT License
Python M... |
4,291 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1>GarrissonWow</h1>
Script to get world of warcraft data and display it in a website.
Step1: inpd character name input ut realm an.
Step2: If faction is alliance change CSS to BLUE backg... | Python Code:
import battlenet
import dominate
from dominate.tags import *
import json
import arrow
import requests
import datetime
from battlenet import Character
from battlenet import Realm
#Realm.to_json()
realm = Realm(battlenet.UNITED_STATES, "jubei'thos")
realm
print realm.is_online()
print realm.to_json()
rejs =... |
4,292 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
NumPy
Numpy é um pacote fundamental para programação científica com Python. Ele traz consigo uma variedade de operações matemáticas, principalmente referente à operações algébricas com dados... | Python Code:
import numpy as np
Explanation: NumPy
Numpy é um pacote fundamental para programação científica com Python. Ele traz consigo uma variedade de operações matemáticas, principalmente referente à operações algébricas com dados N-dimensionais!
End of explanation
a = np.array([1, 2, 3])
print(repr(a), a.shape, e... |
4,293 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sliders Example
This is an example of interactive iPython workbook that uses widgets to meaningfully interact with visualization.
Step4: 2D Rank Features | Python Code:
# Imports
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from collections import OrderedDict
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import Imputer
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squ... |
4,294 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Spatiotemporal permutation F-test on full sensor data
Tests for differential evoked responses in at least
one condition using a permutation clustering test.
The FieldTrip neighbor templates ... | Python Code:
# Authors: Denis Engemann <denis.engemann@gmail.com>
# Jona Sassenhagen <jona.sassenhagen@gmail.com>
# Alex Rockhill <aprockhill@mailbox.org>
# Stefan Appelhoff <stefan.appelhoff@mailbox.org>
#
# License: BSD-3-Clause
import numpy as np
import matplotlib.pyplot as plt
from mpl_to... |
4,295 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Urban Networks II
Overview of today's topics
Step1: 1. Model a study site
First, we will identify a study site, model its street network, and calculate some simple indicators.
Step2: 2. Si... | Python Code:
import geopandas as gpd
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import osmnx as ox
import pandana
import pandas as pd
from shapely.geometry import Point
# consistent randomization
np.random.seed(0)
# configure OSMnx
cache_folder = '../../data/cache2'
ox.config(log_console=T... |
4,296 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Quick start guide
Installation
Stable
Fri can be installed via the Python Package Index (PyPI).
If you have pip installed just execute the command
pip install fri
to get the newest stable ve... | Python Code:
import numpy as np
# fixed Seed for demonstration
STATE = np.random.RandomState(123)
from fri import genClassificationData
Explanation: Quick start guide
Installation
Stable
Fri can be installed via the Python Package Index (PyPI).
If you have pip installed just execute the command
pip install fri
to get t... |
4,297 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Getting started with TensorFlow (Eager Mode)
Learning Objectives
- Understand difference between Tensorflow's two modes
Step1: Eager Execution
Step2: Adding Two Tensors
The value of the ... | Python Code:
import tensorflow as tf
print(tf.__version__)
Explanation: Getting started with TensorFlow (Eager Mode)
Learning Objectives
- Understand difference between Tensorflow's two modes: Eager Execution and Graph Execution
- Practice defining and performing basic operations on constant Tensors
- Use Tensorf... |
4,298 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright © 2019 The TensorFlow Authors.
Step1: TensorFlow Model Analysis
An Example of a Key TFX Library
This example colab notebook illustrates how TensorFlow Model Analysis (TFMA) c... | Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# dist... |
4,299 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tabular core
Basic function to preprocess tabular data before assembling it in a DataLoaders.
Initial preprocessing
Step1: For example if we have a series of dates we can then generate feat... | Python Code:
#|export
def make_date(df, date_field):
"Make sure `df[date_field]` is of the right date type."
field_dtype = df[date_field].dtype
if isinstance(field_dtype, pd.core.dtypes.dtypes.DatetimeTZDtype):
field_dtype = np.datetime64
if not np.issubdtype(field_dtype, np.datetime64):
... |
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