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7,300 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
NBA Free throw analysis
Now let's see some of these methods in action on real world data.
I'm not a basketball guru by any means, but I thought it would be fun to see whether we can find pla... | Python Code:
from __future__ import division
import pandas as pd
import numpy as np
import scipy as sp
import scipy.stats
import toyplot as tp
Explanation: NBA Free throw analysis
Now let's see some of these methods in action on real world data.
I'm not a basketball guru by any means, but I thought it would be fun to s... |
7,301 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Short intro to the SCT library of AutoGraph
Work in progress, use with care and expect changes.
The pyct module packages the source code transformation APIs used by AutoGraph.
This tutorial ... | Python Code:
!pip install tf-nightly
Explanation: Short intro to the SCT library of AutoGraph
Work in progress, use with care and expect changes.
The pyct module packages the source code transformation APIs used by AutoGraph.
This tutorial is just a preview - there is no PIP package yet, and the API has not been finali... |
7,302 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sucesión de Fibonacci
La sucesión de Fibonacci se usa para modelar crecimiento de poblaciones de bichos. Por ejemplo
Step1: Polinomio característico
Como $\lambda$ es un valor propio de $A$... | Python Code:
# importamos bibliotecas cómputo de matrices
import numpy as np
A = np.matrix([[1, 1],
[1, 0]])
# para k=3 A se multiplica por sí misma tres veces:
A*A*A
u0 = [1,
0]
# u3
np.dot(A*A*A,
u0)
Explanation: Sucesión de Fibonacci
La sucesión de Fibonacci se usa para modelar crecimient... |
7,303 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Functions
As in any other language Python allows for the definition of functions.
Functions are a set of sequence of instructions that receive well define inputs
and produce some outputs.
To... | Python Code:
def square(a):
return a*a
print(square(1), square(2), square(5))
square('a') # This should give an error
def minimum(a, b):
if a<b:
return a
elif b<a:
return b
else:
return a
minimum(4,5)
minimum(4,0)
Explanation: Functions
As in any other language Python allows for ... |
7,304 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Use different base estimators for optimization
Sigurd Carlen, September 2019.
Reformatted by Holger Nahrstaedt 2020
.. currentmodule
Step1: Toy example
Let assume the following noisy functi... | Python Code:
print(__doc__)
import numpy as np
np.random.seed(1234)
import matplotlib.pyplot as plt
Explanation: Use different base estimators for optimization
Sigurd Carlen, September 2019.
Reformatted by Holger Nahrstaedt 2020
.. currentmodule:: skopt
To use different base_estimator or create a regressor with differe... |
7,305 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
5. Model the Solution
Preprocessing to get the tidy dataframe
Step1: Question 3
Step2: PRINCIPLE
Step3: PRINCIPLE
Step4: PRINCIPLE
Step5: Question 4
Step6: Now we can fit an ARIMA mode... | Python Code:
# Import the library we need, which is Pandas and Matplotlib
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Set some parameters to get good visuals - style to ggplot and size to 15,10
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (15, 10)
# Read the c... |
7,306 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Explore the uncertainity in the resistance of a sample circuit
http
Step1: For this simple circuit the questions are
1. what is the end-to-end resistance and its uncertainity?
1. What are ... | Python Code:
Image("res4.gif")
Explanation: Explore the uncertainity in the resistance of a sample circuit
http://www.electronics-tutorials.ws/resistor/res_5.html
End of explanation
import numpy as np
import matplotlib.pyplot as plt
import pymc3 as pm
import pandas as pd
import seaborn as sns
sns.set()
%matplotlib inli... |
7,307 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
LAB 3a
Step1: Verify tables exist
Run the following cells to verify that we previously created the dataset and data tables. If not, go back to lab 1b_prepare_data_babyweight to create them.... | Python Code:
%%bash
sudo pip freeze | grep google-cloud-bigquery==1.6.1 || \
sudo pip install google-cloud-bigquery==1.6.1
Explanation: LAB 3a: BigQuery ML Model Baseline.
Learning Objectives
Create baseline model with BQML
Evaluate baseline model
Calculate RMSE of baseline model
Introduction
In this notebook, we will... |
7,308 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Finding data story
Goals
Find a data story
Narrow down fields of dataset to explore
Imports and helper functions
Step3: Current visualization ideas
Show map and as user searches highlight o... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import sqlite3
import pandas as pd
import seaborn as sns
sns.set_style("white")
conn = sqlite3.connect('../data/output/database.sqlite')
c = conn.cursor()
def execute(sql):
'''
Executes a SQL command on the 'c' cursor and returns the results
''... |
7,309 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Skip-gram word2vec
In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about emb... | Python Code:
!pip install tqdm
import time
import numpy as np
import tensorflow as tf
import utils
Explanation: Skip-gram word2vec
In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for u... |
7,310 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Ocean
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify d... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'snu', 'sandbox-2', 'ocean')
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: SNU
Source ID: SANDBOX-2
Topic: Ocean
Sub-Topics: Timestepping Framework, Adve... |
7,311 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Inheritance
Inheritance means extending the properties of one class by another. Inheritance implies code reusability, because of which client classes do not need to implement everything from... | Python Code:
class Person:
# Constructor
def __init__(self, name, age):
self.name = name
self.age = age
def __str__(self):
return 'name = {}\nage = {}'.format(self.name,self.age)
# Inherited or Sub class
class Employee(Person):
def __init__(self, name, age, em... |
7,312 | 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: Scatter plots
I'll start with the data from the BRFSS again.
Step2: The following function selects a random subset of a Dat... | Python Code:
from __future__ import print_function, division
%matplotlib inline
import numpy as np
import brfss
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/licenses/MIT
End... |
7,313 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Landice
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'noaa-gfdl', 'gfdl-am4', 'landice')
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: NOAA-GFDL
Source ID: GFDL-AM4
Topic: Landice
Sub-Topics: Glaciers, Ic... |
7,314 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
4 ways to print in GAlgebra
Step1: Printing in plain text
Step2: Enhanced Console Printing
If called Eprint() upfront, print() will do Enhanced Console Printing(colored printing with ANSI ... | Python Code:
from sympy import *
from galgebra.ga import Ga
from galgebra.printer import Eprint, Format, xpdf
cga3d = Ga(r'e_1 e_2 e_3 e e_{0}',g='1 0 0 0 0,0 1 0 0 0,0 0 1 0 0,0 0 0 0 -1,0 0 0 -1 0')
Explanation: 4 ways to print in GAlgebra
End of explanation
print(cga3d.I())
Explanation: Printing in plain text
End of... |
7,315 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Title
Step1: Create Text Data
Step2: Create Bag Of Words
Step3: Create Target Vector
Step4: Train Multinomial Naive Bayes Classifier
Step5: Create Previously Unseen Observation
Step6: ... | Python Code:
# Load libraries
import numpy as np
from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import CountVectorizer
Explanation: Title: Multinomial Naive Bayes Classifier
Slug: multinomial_naive_bayes_classifier
Summary: How to train a Multinomial naive bayes classifer in Sciki... |
7,316 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Configurar las credenciales para acceder al API de Twitter
Step1: Esta es la molona librería que vamos a utilizar
Step2: 1 . Recoger tweets a partir de un id
Step3: 2. Recoger tweets de u... | Python Code:
config = ConfigParser()
config.read(join(pardir,'src','credentials.ini'))
APP_KEY = config['twitter']['app_key']
APP_SECRET = config['twitter']['app_secret']
OAUTH_TOKEN = config['twitter']['oauth_token']
OAUTH_TOKEN_SECRET = config['twitter']['oauth_token_secret']
from twitter import oauth, Twitter, Twi... |
7,317 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ANÁLISIS ESTADÍSTICO DE DATOS
Step1: PARA RECORDAR
Distribución binomial
Se denomina proceso de Bernoulli aquel experimento que consiste en repetir n veces una prueba, cada una independient... | Python Code:
dado = np.array([5, 3, 3, 2, 5, 1, 2, 3, 6, 2, 1, 3, 6, 6, 2, 2, 5, 6, 4, 2, 1, 3, 4, 2, 2, 5, 3, 3,
2, 2, 2, 1, 6, 2, 2, 6, 1, 3, 3, 3, 4, 4, 6, 6, 1, 2, 2, 6, 1, 4, 2, 5, 3, 6, 6, 3,
5, 2, 2, 4, 2, 2, 4, 4, 3, 3, 1, 2, 6, 1, 3, 3, 5, 4, 6, 6, 4, 2, 5, 6, 1, 4, 5, 4, 3, 5,... |
7,318 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Groupby
El metodo groupby nos permite agrupar informacion y aplicar funciones de agregacion
Step1: Ahora ya podemos usar la funcion .groupby() para agrupar la informacion en base a los nomb... | Python Code:
#%% librerias
import pandas as pd
# Crear un dataFrame
data = {'Company':['GOOG','GOOG','MSFT','MSFT','FB','FB'],
'Person':['Sam','Charlie','Amy','Vanessa','Carl','Sarah'],
'Sales':[200,120,340,124,243,350]}
df = pd.DataFrame(data)
df
Explanation: Groupby
El metodo groupby nos permite agrupar... |
7,319 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<div align="center"><img width="50%" src="https
Step1: You can also run a cell with Ctrl+Enter or Shift+Enter. Experiment a bit with that.
Tab Completion
One of the most useful things about... | Python Code:
import pandas as pd
print("Hi! This is a cell. Click on it and press the ▶ button above to run it")
Explanation: <div align="center"><img width="50%" src="https://raw.githubusercontent.com/jupyter/jupyter/master/docs/source/_static/_images/jupyter.png"></div>
Jupyter Notebook
This notebook was adapted from... |
7,320 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
State Space Discretization
From the previous notebook you probably end up thinking about the fact that the states and action we were dealing with on the Frozen Lake environment were discrete... | Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tempfile
import base64
import pprint
import json
import sys
import gym
import io
from gym import wrappers
from subprocess import check_output
from IPython.display import HTML
Explanation: State Space Discretization
From the previ... |
7,321 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2019 The TensorFlow Authors.
Step1: Dogs vs Cats Image Classification Without Image Augmentation
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" hr... | 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... |
7,322 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
All API's
Step1: After writing a code that returns a result, now automating that for the various dates using a function
Step2: 2) What are all the different book categories the NYT ranked ... | Python Code:
#API Key: 0c3ba2a8848c44eea6a3443a17e57448
import requests
bestseller_response = requests.get('http://api.nytimes.com/svc/books/v2/lists/2009-05-10/hardcover-fiction?api-key=0c3ba2a8848c44eea6a3443a17e57448')
bestseller_data = bestseller_response.json()
print("The type of bestseller_data is:", type(bestsel... |
7,323 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Before we get started, a couple of reminders to keep in mind when using iPython notebooks
Step1: Fixing Data Types
Step2: Note when running the above cells that we are actively changing th... | Python Code:
import unicodecsv
## Longer version of code (replaced with shorter, equivalent version below)
# enrollments = []
# f = open('enrollments.csv', 'rb')
# reader = unicodecsv.DictReader(f)
# for row in reader:
# enrollments.append(row)
# f.close()
def open_file(filename):
with open(filename, 'rb') as f... |
7,324 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Nearest Neighbors
When exploring a large set of documents -- such as Wikipedia, news articles, StackOverflow, etc. -- it can be useful to get a list of related material. To find relevant doc... | Python Code:
import graphlab
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
Explanation: Nearest Neighbors
When exploring a large set of documents -- such as Wikipedia, news articles, StackOverflow, etc. -- it can be useful to get a list of related material. To find relevant documents you typical... |
7,325 |
Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
import a gpt2 tokenizer to tokenize my text dataset
| Python Code::
from transformers import GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
|
7,326 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Use BlackJAX with TFP
BlackJAX can take any log-probability function as long as it is compatible with JAX's JIT. In this notebook we show how we can use tensorflow-probability as a modeling ... | Python Code:
import jax
import jax.numpy as jnp
import numpy as np
from tensorflow_probability.substrates import jax as tfp
tfd = tfp.distributions
import blackjax
Explanation: Use BlackJAX with TFP
BlackJAX can take any log-probability function as long as it is compatible with JAX's JIT. In this notebook we show how w... |
7,327 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Machine Learning with Python
Speaker
Step1: $$ \hat{\theta} = (\mathbf{X}^T \cdot \mathbf{X})^{-1} \cdot \mathbf{X}^T \cdot \mathbf{y} $$
Step2: which is the same result.
However ...
Norma... | Python Code:
# prepare some data
import numpy as np
np.random.seed(42) # only if you want reproduceable result
X = 2 * np.random.rand(100, 1)
y = 4 + 3 * X + np.random.randn(100, 1)
%matplotlib inline
import matplotlib.pyplot as plt
plt.scatter(X, y)
# calculate inverse of the matrix X using inv() function
# from NumP... |
7,328 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Dinámica del Amor
El modelo describe el estado emotivo de Laura, Petrarca y la Inspración de Petrarca, al paso del tiempo.
<table>
<tr>
<td>
<img src="https
Step1: T... | Python Code:
# Para hacer experimentos numéricos importamos numpy
import numpy as np
#import pandas as pd
# y biblioteca para plotear
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
# cómputo simbólico con sympy
from sympy import *
init_printing()
Explanation: Dinámica del Amor
El modelo describe e... |
7,329 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<IMG SRC="https
Step1: Step 2. Reading the time series
The next step is to import the time series data. Three series are used in this example; the observed groundwater head, the rainfall an... | Python Code:
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import pastas as ps
ps.set_log_level("ERROR")
%matplotlib inline
# This notebook has been developed using Pastas version 0.9.9 and Python 3.7
print("Pastas version: {}".format(ps.__version__))
print("Pandas version: {}".format... |
7,330 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
NumPy
Numerical Python
Provides an efficient way to store and manipulate arrays.
Numpy is all about VECTORIZATION. Mental model is different than regular python and works with
Step1: Examp... | Python Code:
import random
class RandomWalker(object):
def __init__(self):
self.position = 0
def walk(self, n):
self.position = 0
for i in range(n):
yield self.position
self.position += 2*random.randint(0,1) - 1
Explan... |
7,331 | Given the following text description, write Python code to implement the functionality described.
Description:
Program for sum of cosh ( x ) series upto Nth term
function to return the factorial of a number ; function to return the Sum of the series ; Driver code
| Python Code:
def fact(n ) :
i , fac = 1 , 1
for i in range(1 , n + 1 ) :
fac = fac * i
return fac
def log_Expansion(x , n ) :
Sum = 0
i = 0
for i in range(n ) :
Sum = Sum + pow(x , 2 * i ) / fact(2 * i )
return Sum
x = 1
n = 10
print(log_Expansion(x , n ) )
|
7,332 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2021 The TensorFlow Authors.
Step1: TensorFlow Lite Metadata Writer API
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step2: Create ... | 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... |
7,333 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Face Generation
In this project, you'll use generative adversarial networks to generate new images of faces.
Get the Data
You'll be using two datasets in this project
Step3: Explore ... | Python Code:
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
DON'T MODIFY ANYTHING IN THIS CELL
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Explanation: Face Generation
In this project, you'll use generative adversa... |
7,334 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Brewing Logistic Regression then Going Deeper
While Caffe is made for deep networks it can likewise represent "shallow" models like logistic regression for classification. We'll do simple lo... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import os
os.chdir('..')
import sys
sys.path.insert(0, './python')
import caffe
import os
import h5py
import shutil
import tempfile
import sklearn
import sklearn.datasets
import sklearn.linear_model
import pandas as pd
Explanation: Brewi... |
7,335 | Given the following text description, write Python code to implement the functionality described.
Description:
Minimum lines to cover all points
Utility method to get gcd of a and b ; method returns reduced form of dy / dx as a pair ; get sign of result ; method returns minimum number of lines to cover all points where... | Python Code:
def gcd(a , b ) :
if(b == 0 ) :
return a
return gcd(b , a % b )
def getReducedForm(dy , dx ) :
g = gcd(abs(dy ) , abs(dx ) )
sign =(dy < 0 ) ^(dx < 0 )
if(sign ) :
return(- abs(dy ) // g , abs(dx ) // g )
else :
return(abs(dy ) // g , abs(dx ) // g )
def minLinesToCover... |
7,336 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
확률론적 언어 모형
확률론적 언어 모형(Probabilistic Language Model)은 $m$개의 단어 $w_1, w_2, \ldots, w_m$ 열(word sequence)이 주어졌을 때 문장으로써 성립될 확률 $P(w_1, w_2, \ldots, w_m)$ 을 출력함으로써 이 단어 열이 실제로 현실에서 사용될 수 있는 문장(s... | Python Code:
from nltk.corpus import movie_reviews
# 문서를 문장으로 분리
sentences = list(movie_reviews.sents())
import random
# 섞는다.
random.seed(1)
random.shuffle(sentences)
sentences[0]
Explanation: 확률론적 언어 모형
확률론적 언어 모형(Probabilistic Language Model)은 $m$개의 단어 $w_1, w_2, \ldots, w_m$ 열(word sequence)이 주어졌을 때 문장으로써 성립될 확률 $P(... |
7,337 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Contextual Bandits (incomplete)
Step1: Query by Committee
Step2: Stochastic Gradient Descent
Step3: Random selection of data points at each iteration.
Step4: SVM with Random Sampling
Ste... | Python Code:
import numpy as np
import pandas as pd
import pickle
import seaborn as sns
from pandas import DataFrame, Index
from sklearn import metrics
from sklearn.linear_model import SGDClassifier
from sklearn.svm import SVC
from sklearn.kernel_approximation import RBFSampler, Nystroem
from sklearn.linear_model impor... |
7,338 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Repeatable splitting
Learrning Objectives
* explore the impact of different ways of creating train/valid/test splits
Overview
Repeatability is important in machine learning. If you do the s... | Python Code:
from google.cloud import bigquery
Explanation: Repeatable splitting
Learrning Objectives
* explore the impact of different ways of creating train/valid/test splits
Overview
Repeatability is important in machine learning. If you do the same thing now and 5 minutes from now and get different answers, then i... |
7,339 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<center> Doing Math with Python </center>
<center>
<p> <b>Amit Saha</b>
<p>May 30, PyCon US 2016
<p>Portland, Oregon
</center>
## About me
- Software Engineer at [Freelancer.com](https
Step1... | Python Code:
# Create graphs from algebraic expressions
from sympy import Symbol, plot
x = Symbol('x')
p = plot(2*x**2 + 2*x + 2)
# Solve equations
from sympy import solve, Symbol
x = Symbol('x')
solve(2*x + 1)
# Limits
from sympy import Symbol, Limit, sin
x = Symbol('x')
Limit(sin(x)/x, x, 0).doit()
# Derivative
from ... |
7,340 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
First, we'll download the dataset to our local machine. The data consists of characters rendered in a variety of fonts on a 28x28 image. The labels are limited to 'A' through 'J' (10 classes... | Python Code:
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import tarfile
from IPython.display import display, Image
from scipy import ndimage
from... |
7,341 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2020 The TensorFlow Authors.
Step1: Integrating MinDiff with MinDiffModel
<div class="devsite-table-wrapper"><table class="tfo-notebook-buttons" align="left">
<td><a target="_bl... | 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... |
7,342 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
2 samples permutation test on source data with spatio-temporal clustering
Tests if the source space data are significantly different between
2 groups of subjects (simulated here using one su... | Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)
import os.path as op
import numpy as np
from scipy import stats as stats
import mne
from mne import spatial_src_connectivity
from mne.stats import spatio_tempor... |
7,343 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Application
Step1: Download prediction files
We release four groups of predictions
Step2: Finally, load the occupations data from the U.S. Bureau of Labor Statistics, which we'll use to co... | Python Code:
#@title Import libraries and multibootstrap code
import re
import os
import numpy as np
import pandas as pd
import sklearn.metrics
import scipy.stats
from tqdm.notebook import tqdm # for progress indicator
import multibootstrap
#@title Import and configure plotting libraries
import matplotlib
from matplot... |
7,344 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Solving convection-coupled melting with adaptive mixed finite elements
This Jupyter notebook shows how to solve the convection-coupled melting benchmark with the general approach of [5], usi... | Python Code:
import fenics
Explanation: Solving convection-coupled melting with adaptive mixed finite elements
This Jupyter notebook shows how to solve the convection-coupled melting benchmark with the general approach of [5], using mixed finite elements in FEniCS with goal-oriented adaptive mesh refinement (AMR) as pr... |
7,345 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Collaborative-Filtering-for-Implicit-Feedback-Datasets" data-toc-modified-id... | Python Code:
# code for loading the format for the notebook
import os
# path : store the current path to convert back to it later
path = os.getcwd()
os.chdir(os.path.join('..', 'notebook_format'))
from formats import load_style
load_style(css_style = 'custom2.css', plot_style = False)
os.chdir(path)
# 1. magic to print... |
7,346 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
07- Feature Selection
by Alejandro Correa Bahnsen
version 0.2, May 2016
Part of the class Machine Learning for Security Informatics
This notebook is licensed under a Creative Commons Attribu... | Python Code:
import pandas as pd
import numpy as np
import zipfile
with zipfile.ZipFile('../datasets/titanic.csv.zip', 'r') as z:
f = z.open('titanic.csv')
titanic = pd.read_csv(f, sep=',', index_col=0)
titanic.head()
titanic.Age.fillna(titanic.Age.median(), inplace=True)
titanic.loc[titanic.Embarked.isnull(), ... |
7,347 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Priklad vyber autributov pomocou filtra a ukazka toho, preco PCA nie je vyber atributov
Step1: Skusme najskor priklad toho ako by sme z nejakeho datasetu vyberali najdolezitejsie atributy p... | Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn
plt.rcParams['figure.figsize'] = 9, 6
Explanation: Priklad vyber autributov pomocou filtra a ukazka toho, preco PCA nie je vyber atributov
End of explanation
from sklearn import datasets, svm
from sklea... |
7,348 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Example of the $k$-point sampling for TBtrans.
Step2: Run these two executables | Python Code:
chain = sisl.Geometry([0]*3, sisl.Atom(1, R=1.), sc=[1, 1, 10])
chain.set_nsc([3, 3, 1])
# Transport along y-direction
chain = chain.tile(20, 0)
He = sisl.Hamiltonian(chain)
He.construct(([0.1, 1.1], [0, -1]))
Hd = He.tile(20, 1)
He.write('ELEC.nc')
Hd.write('DEVICE.nc')
with open('RUN.fdf', 'w') as f:
... |
7,349 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Filtering of Data in Insights Parsers and Rules
In this tutorial we will investigate filters in insights-core, what they are, how they affect your components and how you can use them ... | Python Code:
Some imports used by all of the code in this tutorial
import sys
sys.path.insert(0, "../..")
from __future__ import print_function
import os
from insights import run
from insights.specs import SpecSet
from insights.core import IniConfigFile
from insights.core.plugins import parser, rule, make_fail
from i... |
7,350 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
last lesson we wrote code to plot some values from our inflammation data.
but we have a dozen we want to do same for
how to repeat things?
Step1: This is a bad appoach b/c
Step2: uses a fo... | Python Code:
#example task: print each character in a word
#one way to do is use a series of print statements
word = 'lead'
print(word[0])
print(word[1])
print(word[2])
print(word[3])
Explanation: last lesson we wrote code to plot some values from our inflammation data.
but we have a dozen we want to do same for
how to... |
7,351 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
3A.mr - Random Walk with Restart (système de recommandations)
Si la méthode de factorisation de matrices est la méthode la plus connue pour faire des recommandations, ce n'est pas la seule. ... | Python Code:
import numpy as np
from numpy.linalg import det
P = np.matrix ( [[ 0,0.5,0,0.5],[0.5,0,0.5,0],[1./3,1./3,0,1./3],[0.1,0.9,0,0]])
P
c = 0.15
I = np.identity(4)
e = np.matrix( [[ 0., 1., 0., 0. ]]).T
pi = ((I-P.T*(1-c))).I * e * c
pi
Explanation: 3A.mr - Random Walk with Restart (système de recommandations)
... |
7,352 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Handwritten Number Recognition with TFLearn and MNIST
In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-9.
This kind of neural network is used in a ... | Python Code:
# Import Numpy, TensorFlow, TFLearn, and MNIST data
import numpy as np
import tensorflow as tf
import tflearn
import tflearn.datasets.mnist as mnist
Explanation: Handwritten Number Recognition with TFLearn and MNIST
In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-... |
7,353 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Chapter 13
Examples and Exercises from Think Stats, 2nd Edition
http
Step1: Survival analysis
If we have an unbiased sample of complete lifetimes, we can compute the survival function from ... | Python Code:
from os.path import basename, exists
def download(url):
filename = basename(url)
if not exists(filename):
from urllib.request import urlretrieve
local, _ = urlretrieve(url, filename)
print("Downloaded " + local)
download("https://github.com/AllenDowney/ThinkStats2/raw/master... |
7,354 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Variational Equations
For a complete introduction to variational equations, please read the paper by Rein and Tamayo (2016).
For this tutorial, we work with a two planet system. We vary the ... | Python Code:
import rebound
import numpy as np
%matplotlib inline
import matplotlib;
import matplotlib.pyplot as plt
Explanation: Variational Equations
For a complete introduction to variational equations, please read the paper by Rein and Tamayo (2016).
For this tutorial, we work with a two planet system. We vary the ... |
7,355 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Simple script to transform exported GeoModeller 3-D regular grid into MOOSE grid
Note
Step3: Load exported GeoModeller file
We use here simply the export functionality of GeoModeller (note
... | Python Code:
# some basic imports:
import numpy as np
import os
import matplotlib.pyplot as plt
# for 3-D visualisation:
import ipyvolume.pylab as p3
Explanation: Simple script to transform exported GeoModeller 3-D regular grid into MOOSE grid
Note: we don't create a proper FE mesh here, but simply perform a "mapping" ... |
7,356 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sebastian Raschka
last updated
Step1: <br>
<br>
<a name='plotting_data'></a>
Plotting the sample data
To get an intuitive idea of how our data looks like, let us visualize it in a simple s... | Python Code:
import numpy as np
np.random.seed(123456)
# Generate 100 random patterns for class1
mu_vec1 = np.array([[0],[0]])
cov_mat1 = np.array([[3,0],[0,3]])
x1_samples = np.random.multivariate_normal(mu_vec1.ravel(), cov_mat1, 100)
# Generate 100 random patterns for class2
mu_vec2 = np.array([[9],[0]])
cov_mat2 = ... |
7,357 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example reading THREDDS NCSS as point in Python
Step1: First generate a sample RESTful URL using the NCSS Web Form
Step2: Once you have a sample URL, it's easy to create a different URL pr... | Python Code:
%matplotlib inline
import pandas as pd
Explanation: Example reading THREDDS NCSS as point in Python
End of explanation
url = 'http://data.ncof.co.uk/thredds/ncss/METOFFICE-NWS-AF-WAV-HOURLY?var=VHM0&var=VHM0_SW1&var=VHM0_WW&latitude=61.15&longitude=-9.5&time_start=2017-06-02T00%3A00%3A00Z&time_end=2017-06-... |
7,358 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Chapter 21
Modeling and Simulation in Python
Copyright 2021 Allen Downey
License
Step1: In the previous chapter we simulated a penny falling in a vacuum, that
is, without air resistance. Bu... | Python Code:
# install Pint if necessary
try:
import pint
except ImportError:
!pip install pint
# download modsim.py if necessary
from os.path import exists
filename = 'modsim.py'
if not exists(filename):
from urllib.request import urlretrieve
url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/... |
7,359 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
K-Means Clustering Example
Let's make some fake data that includes people clustered by income and age, randomly
Step1: We'll use k-means to rediscover these clusters in unsupervised learnin... | Python Code:
from numpy import random, array
#Create fake income/age clusters for N people in k clusters
def createClusteredData(N, k):
random.seed(10)
pointsPerCluster = float(N)/k
X = []
for i in range (k):
incomeCentroid = random.uniform(20000.0, 200000.0)
ageCentroid = random.uniform... |
7,360 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Python Flight Mechanics Engine
This installs pyfme to be run in this online notebook
Step1: Aircraft
In order to perform a simulation, the first thing we need is an aircraft
Step2: Aircraf... | Python Code:
!pip install git+https://github.com/AeroPython/PyFME.git
Explanation: Python Flight Mechanics Engine
This installs pyfme to be run in this online notebook
End of explanation
from pyfme.aircrafts import Cessna172
aircraft = Cessna172()
Explanation: Aircraft
In order to perform a simulation, the first thing ... |
7,361 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Post Training Integer Quantization
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step1: Train and export the model
Step2: For the example, we ... | Python Code:
! pip uninstall -y tensorflow
! pip install -U tf-nightly
import tensorflow as tf
tf.enable_eager_execution()
! git clone --depth 1 https://github.com/tensorflow/models
import sys
import os
if sys.version_info.major >= 3:
import pathlib
else:
import pathlib2 as pathlib
# Add `models` to the python ... |
7,362 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Predicting crime in San Francisco with machine learning (Kaggle 2015)
Notice
Step1: Raw data
Step2: View composing & feature engineering
Step3: Garbage Collection
Step4: Draw samples
Ste... | Python Code:
import datetime
import gc
import zipfile
import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn as sk
from pandas.tseries.holiday import USFederalHolidayCalendar
from sklearn.cross_validation import KFold, cross_val_score
from sklearn.ensemble import RandomFore... |
7,363 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
server/udp/podCommands.js - from node.js /JavaScript to Python (object)
Step1: The reactGS folder "mimics" the actual react-groundstation github repository, only copying the file directory ... | Python Code:
# find out where we are on the file directory
import os, sys
print( os.getcwd())
print( os.listdir(os.getcwd()))
Explanation: server/udp/podCommands.js - from node.js /JavaScript to Python (object)
End of explanation
wherepodCommandsis = os.getcwd()+'/reactGS/server/udp/'
print(wherepodCommandsis)
Explana... |
7,364 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Chapter 2
Modeling and Simulation in Python
Copyright 2021 Allen Downey
License
Step1: This chapter presents a simple model of a bike share system and
demonstrates the features of Python we... | Python Code:
# install Pint if necessary
try:
import pint
except ImportError:
!pip install pint
# download modsim.py if necessary
from os.path import exists
filename = 'modsim.py'
if not exists(filename):
from urllib.request import urlretrieve
url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/... |
7,365 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href='http
Step1: Train Test Split
Step2: Estimators
Let's show you how to use the simpler Estimator interface!
Step3: Feature Columns
Step4: Input Function
Step5: Model Evaluation
... | Python Code:
import pandas as pd
df = pd.read_csv('iris.csv')
df.head()
df.columns = ['sepal_length','sepal_width','petal_length','petal_width','target']
X = df.drop('target',axis=1)
y = df['target'].apply(int)
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
Tensorflow with... |
7,366 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
In the last notebook, I showed you how easy it is to connect jQAssistant/neo4j with Python Pandas/py2neo. In this notebook, I show you a (at first glance) simple analysis of the... | Python Code:
import py2neo
import pandas as pd
import matplotlib.pyplot as plt
# display graphics directly in the notebook
%matplotlib inline
Explanation: Introduction
In the last notebook, I showed you how easy it is to connect jQAssistant/neo4j with Python Pandas/py2neo. In this notebook, I show you a (at first glanc... |
7,367 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Input from the keyboard
In Pytho, the keyboard is the default input device.
<a id='input'></a>
1. Input from the code
Step1: <a id='prompt'></a>
2. Input from the OS prompt
Step2: Another ... | Python Code:
a = input('Please, enter something: ')
print('You entered:', a)
Explanation: Input from the keyboard
In Pytho, the keyboard is the default input device.
<a id='input'></a>
1. Input from the code
End of explanation
!cat argparse_example.py
!python argparse_example.py -h
!python argparse_example.py -i abc
Ex... |
7,368 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Searching for packages
As explained in "Uploading a Package", packages are managed using registries. There is a one local registry on your machine, and potentially many remote registries els... | Python Code:
import quilt3
# list local packages
list(quilt3.list_packages())
# list remote packages
list(quilt3.list_packages("s3://quilt-example"))
Explanation: Searching for packages
As explained in "Uploading a Package", packages are managed using registries. There is a one local registry on your machine, and poten... |
7,369 | 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: Statistical Analysis and Data Exploration
In this first section of the project, you will quickly investig... | Python Code:
# Importing a few necessary libraries
# python standard library
import warnings
# third party
import numpy as np
import numpy
import matplotlib.pyplot as pl
from matplotlib import pylab
import pandas
import seaborn
from sklearn import datasets
from sklearn.tree import DecisionTreeRegressor
# Make matplotli... |
7,370 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
WAve Models (WAM) Usage Example
WAM wave models are most widely used wave models in the world.
This notebook illustrates ways of using WAM models data via Planet OS Datahub API, including
S... | Python Code:
%matplotlib notebook
import urllib.request
import numpy as np
import simplejson as json
import pandas as pd
from netCDF4 import Dataset, date2num, num2date
import ipywidgets as widgets
from IPython.display import display, clear_output
import dateutil.parser
import matplotlib.pyplot as plt
from mpl_toolkits... |
7,371 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Changes in religious affiliation and attendance
Analysis based on data from the CIRP Freshman Survey
Copyright Allen Downey
MIT License
Step1: Read the data. Note
Step2: Compute time vari... | Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from utils import decorate, savefig
import statsmodels.formula.api as smf
import warnings
warnings.filterwarnings('error')
Explanation: Changes in religious affiliation and attendance
Analysis based on data from the C... |
7,372 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Apply logistic regression to categorize whether a county had high mortality rate due to contamination
1. Import the necessary packages to read in the data, plot, and create a logistic regres... | Python Code:
import pandas as pd
%matplotlib inline
import numpy as np
from sklearn.linear_model import LogisticRegression
Explanation: Apply logistic regression to categorize whether a county had high mortality rate due to contamination
1. Import the necessary packages to read in the data, plot, and create a logistic ... |
7,373 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Table of Contents
<p><div class="lev1 toc-item"><a href="#Générer-des-fausses-citations-latines-du-Roi-Loth,-avec-Python,-Wikiquote-et-des-chaînes-de-Markov" data-toc-modified-id="Générer-de... | Python Code:
citation = citation_aleatoire(italic=True)
display(Markdown("> {}".format(citation)))
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Générer-des-fausses-citations-latines-du-Roi-Loth,-avec-Python,-Wikiquote-et-des-chaînes-de-Markov" data-toc-modified-id="Générer-des-fausses-citation... |
7,374 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Chapter 14
Step1: Python also has a module called types, which has the definitions of the basic types of the interpreter.
Example
Step2: Through introspection, it is possible to determine ... | Python Code:
trospection or reflection is the ability of software to identify and report their own internal structures, such as types, variabl# Getting some information
# about global objects in the program
from types import ModuleType
def info(n_obj):
# Create a referênce to the object
obj = globals()[n_obj]
... |
7,375 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Полезные практики, неочевидные моменты
Неоднозная грамматика
Есть примитивная рекурсивная грамматика
Step1: Число вариантов быстро растёт. Для строки "a x 10", парсер переберёт 89 разборов.... | Python Code:
from yargy.parser import prepare_trees
from yargy import Parser, or_, rule
A = or_(
rule('a'),
rule('a', 'a')
)
B = A.repeatable()
display(B.normalized.as_bnf)
parser = Parser(B)
matches = parser.extract('a a a')
for match in matches:
# кроме 3-х полных разборов, парсёр найдёт ещё 7 частичн... |
7,376 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
BigQuery Dataset
Create and permission a dataset in BigQuery.
License
Copyright 2020 Google LLC,
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file exc... | Python Code:
!pip install git+https://github.com/google/starthinker
Explanation: BigQuery Dataset
Create and permission a dataset in BigQuery.
License
Copyright 2020 Google LLC,
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtai... |
7,377 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pôle Emploi Agencies
Date
Step1: OK, this sounds pretty cool. However there are a lot of fields (63) so let's check them vertically
Step2: OK, it's a bit easier to read, but not enough to ... | Python Code:
import os
from os import path
import pandas as pd
import seaborn as _
DATA_FOLDER = os.getenv('DATA_FOLDER')
agencies = pd.read_csv(path.join(DATA_FOLDER, 'pole-emploi-agencies.csv'))
agencies.head()
Explanation: Pôle Emploi Agencies
Date: 2017-11-19
Author: Pascal pascal@bayesimpact.org
In Pôle emploi Ope... |
7,378 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Composite Glyph
One or more actual glyphs that have been grouped together to represent some set of data, which respond to some standardized set of graphics operations.
In a chart, a composit... | Python Code:
bar = BarGlyph(label='a', values=[1])
bar.data
Explanation: Composite Glyph
One or more actual glyphs that have been grouped together to represent some set of data, which respond to some standardized set of graphics operations.
In a chart, a composite glyph is generated for each group of data. For example,... |
7,379 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: TV Script Generation
In this project, you'll generate your own Simpsons TV scripts using RNNs. You'll be using part of the Simpsons dataset of scripts from 27 seasons. The Neural Ne... | Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
Explanation: TV Script Generation
In this project, you'll generate your own Simpsons TV script... |
7,380 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Title
Step1: Create dataframe
Step2: Create a pivot table of group means, by company and regiment
Step3: Create a pivot table of group score counts, by company and regimensts | Python Code:
import pandas as pd
Explanation: Title: Pivot Tables In Pandas
Slug: pandas_pivot_tables
Summary: Pivot Tables In Pandas
Date: 2016-05-01 12:00
Category: Python
Tags: Data Wrangling
Authors: Chris Albon
import modules
End of explanation
raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', '... |
7,381 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Skewness and symmetry
By Evgenia "Jenny" Nitishinskaya and Delaney Granizo-Mackenzie
Part of the Quantopian Lecture Series
Step1: A distribution which is not symmetric is called <i>skewed</... | Python Code:
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
# Plot a normal distribution with mean = 0 and standard deviation = 2
xs = np.linspace(-6,6, 300)
normal = stats.norm.pdf(xs)
plt.plot(xs, normal);
Explanation: Skewness and symmetry
By Evgenia "Jenny" Nitishinskaya and Delaney ... |
7,382 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Text Classification using TensorFlow and Google Cloud - Part 4
This bigquery-public-data
Step1: Importing libraries
Step2: 1. Define Metadata
Step3: 2. Define Input Function
Step4: 3. Cr... | Python Code:
import os
class Params:
pass
# Set to run on GCP
Params.GCP_PROJECT_ID = 'ksalama-gcp-playground'
Params.REGION = 'europe-west1'
Params.BUCKET = 'ksalama-gcs-cloudml'
Params.PLATFORM = 'local' # local | GCP
Params.DATA_DIR = 'data/news' if Params.PLATFORM == 'local' else 'gs://{}/data/news'.format(Par... |
7,383 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Structure and Angular Momentum
Step1: Adding Structure
So far we've looked at simple 2 and 3 level systems, but to accurately model a physical system we may need to consider complex structu... | Python Code:
import numpy as np
Explanation: Structure and Angular Momentum
End of explanation
print(np.sqrt(1/6/3))
print(np.sqrt(1/2/3))
Explanation: Adding Structure
So far we've looked at simple 2 and 3 level systems, but to accurately model a physical system we may need to consider complex structures. For example,... |
7,384 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Scroll down the the analysis section to see the actual analysis
Step1: Loading files
Step2: Analysis
Step3: Some thing seems to be up with the month of September in this data
Step4: This... | Python Code:
1+1
import pandas as pd
import numpy as np
import glob
import os
Explanation: Scroll down the the analysis section to see the actual analysis
End of explanation
path=os.path.expanduser("~/Documents/TaxiTripData/2013taxi_trip_data/")
files=glob.glob(path+'*.csv.zip.gz')
countlist=[]
cunk=100000
for i in fil... |
7,385 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Predicting sentiment from product reviews
Fire up GraphLab Create
Step1: Read some product review data
Loading reviews for a set of baby products.
Step2: Let's explore this data together
D... | Python Code:
import graphlab
Explanation: Predicting sentiment from product reviews
Fire up GraphLab Create
End of explanation
products = graphlab.SFrame('amazon_baby.gl/')
Explanation: Read some product review data
Loading reviews for a set of baby products.
End of explanation
products.head()
Explanation: Let's explor... |
7,386 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Earth temperature over time
Is global temperature rising? How much? This is a question of burning importance in today's world!
Data about global temperatures are available from several sourc... | Python Code:
from IPython.display import YouTubeVideo
YouTubeVideo('gGOzHVUQCw0')
Explanation: Earth temperature over time
Is global temperature rising? How much? This is a question of burning importance in today's world!
Data about global temperatures are available from several sources: NASA, the National Climatic Dat... |
7,387 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Speci... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cams', 'sandbox-3', 'ocnbgchem')
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: CAMS
Source ID: SANDBOX-3
Topic: Ocnbgchem
Sub-Topics: Tracers.
Prop... |
7,388 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Simulating CSTR
Here a CSTR model simulation is demoed with pure surface phase mechanism focussed on Ammonia dehydrogenation. The mechanims consists of 18 species and 7 reactions. In convent... | Python Code:
import os
import matplotlib as mpl
mpl.rcParams['figure.dpi'] = 500
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
Explanation: Simulating CSTR
Here a CSTR model simulation is demoed with pure surface phase mechanism... |
7,389 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Tuning-Spark-Partitions" data-toc-modified-id="Tuning-Spark-Partitions-1"><s... | Python Code:
# code for loading the format for the notebook
import os
# path : store the current path to convert back to it later
path = os.getcwd()
os.chdir(os.path.join('..', 'notebook_format'))
from formats import load_style
load_style(plot_style=False)
os.chdir(path)
# 1. magic to print version
# 2. magic so that t... |
7,390 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<img align="left" src="imgs/logo.jpg" width="50px" style="margin-right
Step1: Loading the Corpus
Next, we load and pre-process the corpus of documents.
Configuring a DocPreprocessor
We'll s... | Python Code:
%load_ext autoreload
%autoreload 2
%matplotlib inline
import os
# Connect to the database backend and initalize a Snorkel session
from lib.init import *
# Here, we just set how many documents we'll process for automatic testing- you can safely ignore this!
n_docs = 1000 if 'CI' in os.environ else 2591
Expl... |
7,391 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
healpy tutorial
See the Jupyter Notebook version of this tutorial at https
Step1: NSIDE and ordering
Maps are simply numpy arrays, where each array element refers to a location in the sky a... | Python Code:
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import healpy as hp
Explanation: healpy tutorial
See the Jupyter Notebook version of this tutorial at https://github.com/healpy/healpy/blob/master/doc/healpy_tutorial.ipynb
See a executed version of the notebook with embedded plots at ht... |
7,392 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's work by building a network for sentiment analysis on the movie review data. Instead of a network written with ... | Python Code:
import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
Explanation: Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's work by building a network for sentiment analysis on the movie review data. Instead of a n... |
7,393 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Atmos
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify d... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'ec-earth3-gris', 'atmos')
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: EC-EARTH-CONSORTIUM
Source ID: EC-EARTH3-GRIS
Topic: Atmo... |
7,394 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lecture 8
Step1: In this example, we switched the ordering of the arguments between the two function calls; consequently, the ordering of the arguments inside the function were also flipped... | Python Code:
def pet_names(name1, name2):
print("Pet 1: ", name1)
print("Pet 2: ", name2)
pet1 = "King"
pet2 = "Reginald"
pet_names(pet1, pet2) # pet1 variable, then pet2 variable
pet_names(pet2, pet1) # notice we've switched the order in which they're passed to the function
Explanation: Lecture 8: Functions ... |
7,395 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Assignment 5
This assignment has weighting $1.5$.
Model tuning and evaluation
Step1: Dataset
We will use the Wisconsin breast cancer dataset for the following questions
Step2: K-fold valid... | Python Code:
# Added version check for recent scikit-learn 0.18 checks
from distutils.version import LooseVersion as Version
from sklearn import __version__ as sklearn_version
Explanation: Assignment 5
This assignment has weighting $1.5$.
Model tuning and evaluation
End of explanation
import pandas as pd
wdbc_source = ... |
7,396 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This notebook will use a simple model to predict ratings for a user, anime pair
Step1: Let's plot the objective and see how it decreases.
Step2: So by 50 iterations, the model hits a bend ... | Python Code:
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline
matplotlib.style.use('seaborn')
from animerec.data import get_data
users, anime = get_data()
from sklearn.model_selection import train_test_split
train, test = train_test_split(users, test_size = 0.1) #let's split up the dataset into a tr... |
7,397 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a id='top'> </a>
Author
Step1: Cosmic-ray composition clustering
Table of contents
Define analysis free parameters
Data preprocessing
Fitting random forest
Fraction correctly identified
Sp... | Python Code:
%load_ext watermark
%watermark -u -d -v -p numpy,matplotlib,scipy,pandas,sklearn,mlxtend
Explanation: <a id='top'> </a>
Author: James Bourbeau
End of explanation
import sys
sys.path.append('/home/jbourbeau/cr-composition')
print('Added to PYTHONPATH')
%matplotlib inline
from __future__ import division, pri... |
7,398 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Drive LEDs with the Raspberry Pi GPIO pins
This notebook will walk you through using the Raspberry Pi General Purpose Input/Output (GPIO) pins to make a LED light burn.
The GPIO pins are the... | Python Code:
#load GPIO library
import RPi.GPIO as GPIO
#Set BCM (Broadcom) mode for the pin numbering
GPIO.setmode(GPIO.BCM)
Explanation: Drive LEDs with the Raspberry Pi GPIO pins
This notebook will walk you through using the Raspberry Pi General Purpose Input/Output (GPIO) pins to make a LED light burn.
The GPIO pin... |
7,399 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Использование глубокого обучения в NLP
Смотрите в этой серии
Step1: Познакомимся с данными
Бывший kaggle-конкурс про выявление нежелательного контента.
Описание конкурса есть тут - https
St... | Python Code:
low_RAM_mode = True
very_low_RAM = False #если у вас меньше 3GB оперативки, включите оба флага
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Использование глубокого обучения в NLP
Смотрите в этой серии:
* Простые способы работать с текстом, bag of ... |
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