Unnamed: 0 int64 0 16k | text_prompt stringlengths 110 62.1k | code_prompt stringlengths 37 152k |
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4,900 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
CrowdTruth for Sparse Multiple Choice Tasks
Step1: Declaring a pre-processing configuration
The pre-processing configuration defines how to interpret the raw crowdsourcing input. To do this... | Python Code:
import pandas as pd
test_data = pd.read_csv("../data/relex-sparse-multiple-choice.csv")
test_data.head()
Explanation: CrowdTruth for Sparse Multiple Choice Tasks: Relation Extraction
In this tutorial, we will apply CrowdTruth metrics to a sparse multiple choice crowdsourcing task for Relation Extraction fr... |
4,901 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Blind Source Separation with the Shogun Machine Learning Toolbox
By Kevin Hughes
This notebook illustrates <a href="http
Step1: Next we're going to need a way to play the audio files we're ... | Python Code:
import numpy as np
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
from scipy.io import wavfile
from scipy.signal import resample
import shogun as sg
def load_wav(filename,samplerate=44100):
# load file
rate, data = wavfile.read(filename)
# convert stereo to mono
... |
4,902 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Setting things up
Let's load the data and give it a quick look.
Step1: Checking out correlations
Let's start looking at how variables in our dataset relate to each other so we know what to ... | Python Code:
df = pd.read_csv('data/apib12tx.csv')
df.describe()
Explanation: Setting things up
Let's load the data and give it a quick look.
End of explanation
df.corr()
Explanation: Checking out correlations
Let's start looking at how variables in our dataset relate to each other so we know what to expect when we sta... |
4,903 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
CDR EDA
First, import relevant libraries
Step1: Then, load the data (takes a few moments)
Step2: This create a calls-per-person frequency distribution, which is the first thing we want to ... | Python Code:
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
Explanation: CDR EDA
First, import relevant libraries:
End of explanation
# Load data
df = pd.read_csv("./aws-data/firence_foreigners_3days_past_future.csv", header=No... |
4,904 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Factorization
Factorization is the process of restating an expression as the product of two expressions (in other words, expressions multiplied together).
For example, you can make the value... | Python Code:
from random import randint
x = randint(1,100)
y = randint(1,100)
(2*x*y**2)*(-3*x*y) == -6*x**2*y**3
Explanation: Factorization
Factorization is the process of restating an expression as the product of two expressions (in other words, expressions multiplied together).
For example, you can make the value 16... |
4,905 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Aerosol
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', 'mri', 'sandbox-1', 'aerosol')
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: MRI
Source ID: SANDBOX-1
Topic: Aerosol
Sub-Topics: Transport, Emissions, ... |
4,906 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pore Scale Models
Pore scale models are one of the more important facets of OpenPNM, but they can be a bit confusing at first, since they work 'behind-the-scenes'.
They offer 3 main advantag... | Python Code:
import numpy as np
np.random.seed(0)
import openpnm as op
%config InlineBackend.figure_formats = ['svg']
pn = op.network.Cubic(shape=[5, 5, 1], spacing=1e-4)
geo = op.geometry.SpheresAndCylinders(network=pn, pores=pn.Ps, throats=pn.Ts)
Explanation: Pore Scale Models
Pore scale models are one of the more im... |
4,907 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction to Python and Natural Language Technologies
Lecture 5
Decorators and packaging
March 7, 2018
Let's create a greeter function
takes another function as a parameter
greets the cal... | Python Code:
def greeter(func):
print("Hello")
func()
def say_something():
print("Let's learn some Python.")
greeter(say_something)
# greeter(12)
Explanation: Introduction to Python and Natural Language Technologies
Lecture 5
Decorators and packaging
March 7, 2018
Let's create a greeter function
t... |
4,908 | 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:]
text[:100]
Explanation: TV Script Generation
In this project, you'll generate your own Simpson... |
4,909 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Content under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2018 by D. Koehn, notebook style sheet by L.A. Barba, N.C. Clementi
Step1: Performance optim... | Python Code:
# Execute this cell to load the notebook's style sheet, then ignore it
from IPython.core.display import HTML
css_file = '../../style/custom.css'
HTML(open(css_file, "r").read())
Explanation: Content under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2018 by D. Koehn, n... |
4,910 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
데이터 분석의 소개
데이터 분석이란 용어는 상당히 광범위한 용어이므로 여기에서는 통계적 분석과 머신 러닝이라는 두가지 세부 영역에 국한하여 데이터 분석을 설명하도록 한다.
데이터 분석이란
데이터 분석이란 어떤 데이터가 주어졌을 때
데이터 간의 관계를 파악하거나
파악된 관계를 사용하여 원하는 데이터를 만들어 내는 과정
으로 볼 수 있다.
... | Python Code:
from sklearn.datasets import load_digits
digits = load_digits()
plt.imshow(digits.images[0], interpolation='nearest');
plt.grid(False)
digits.images[0]
from sklearn.datasets import fetch_20newsgroups
news = fetch_20newsgroups()
print(news.data[0])
from sklearn.feature_extraction.text import TfidfVectorizer... |
4,911 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Principal Component Analysis in Shogun
By Abhijeet Kislay (GitHub ID
Step1: Some Formal Background (Skip if you just want code examples)
PCA is a useful statistical technique that has found... | Python Code:
%pylab inline
%matplotlib inline
# import all shogun classes
from modshogun import *
Explanation: Principal Component Analysis in Shogun
By Abhijeet Kislay (GitHub ID: <a href='https://github.com/kislayabhi'>kislayabhi</a>)
This notebook is about finding Principal Components (<a href="http://en.wikipedia.o... |
4,912 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
To build an automaton, simply call translate() with a formula, and a list of options to characterize the automaton you want (those options have the same name as the long options name of the ... | Python Code:
a = spot.translate('(a U b) & GFc & GFd', 'BA', 'complete'); a
Explanation: To build an automaton, simply call translate() with a formula, and a list of options to characterize the automaton you want (those options have the same name as the long options name of the ltl2tgba tool, and they can be abbreviate... |
4,913 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example 1
Step1: Then, create the cell object using the LFPy.Cell
class, specifying the morphology file.
The passive mechanisms
are not switched on by default.
Step2: Then, align apical d... | Python Code:
import matplotlib.pyplot as plt
import numpy as np
import LFPy
Explanation: Example 1: Post-synaptic response of a single synapse
This is an example of LFPy running in an Jupyter notebook. To run through this example code and produce output, press <shift-Enter> in each code block below.
First step is... |
4,914 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step2: Some Bayesian AB testing.
Thanks to my overly talented friend Maciej Kula for this example.
We'll reproduce some of his work from a blog post, and learn how to do AB testing with PyM... | Python Code:
def generate_data(no_samples,
treatment_proportion=0.1,
treatment_mu=1.2,
control_mu=1.0,
sigma=0.4):
Generate sample data from the experiment.
rnd = np.random.RandomState(seed=12345)
treatment = rnd.binomial(1, t... |
4,915 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
内容索引
通用函数
创建通用函数 --- frompyfunc工厂函数
通用函数的方法 --- reduce函数、accumulate函数、reduceat函数、outer函数
数组的除法运算 --- divide函数、true_divide函数、floor_divide函数
数组的模运算 --- mod函数、remainder函数、fmod函数
位操作函数和比较函数 ---
... | Python Code:
import numpy as np
Explanation: 内容索引
通用函数
创建通用函数 --- frompyfunc工厂函数
通用函数的方法 --- reduce函数、accumulate函数、reduceat函数、outer函数
数组的除法运算 --- divide函数、true_divide函数、floor_divide函数
数组的模运算 --- mod函数、remainder函数、fmod函数
位操作函数和比较函数 ---
End of explanation
# 定义一个Python函数
def pyFunc(a):
result = np.zeros_like(a)
# ... |
4,916 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Chapter 1 - Data Structures and Algorithms
1.1 Unpacking a Sequence into Separate Variables
Step4: 1.2 Unpacking Elements from Iterables of Arbitrary Length
Step6: Discussion
Step8: 1.3 K... | Python Code:
p = (4, 5, 6, 7)
x, y, z, w = p # x -> 4
data = ['ACME', 50, 91.1, (2012, 12, 21)]
name, _, price, date = data # name -> 'ACME', data -> (2012, 12, 21)
s = 'Hello'
a, b, c, d, e = s # a -> H
p = (4, 5)
x, y, z = p # "ValueError"
Explanation: Chapter 1 - Data Structures and Algorithms
1.1 Unpacking a Seque... |
4,917 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Loading a single rate
Step1: the original reaclib source
Step2: evaluate the rate at a given temperature (in K)
Step3: a human readable string describing the rate, and the nuclei involved... | Python Code:
r = reaclib.Rate("reaclib-rates/c13-pg-n14-nacr")
Explanation: Loading a single rate
End of explanation
print(r.original_source)
Explanation: the original reaclib source
End of explanation
r.eval(1.e9)
Explanation: evaluate the rate at a given temperature (in K)
End of explanation
print(r)
print(r.reactant... |
4,918 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
TRAPpy
Step1: Interactive Line Plotting of Data Frames
Interactive Line Plots Supports the same API as the LinePlot but provide an interactive plot that can be zoomed by clicking and draggi... | Python Code:
import sys,os
sys.path.append("..")
import numpy.random
import pandas as pd
import shutil
import tempfile
import trappy
trace_thermal = "./trace.txt"
trace_sched = "../tests/raw_trace.dat"
TEMP_BASE = "/tmp"
def setup_thermal():
tDir = tempfile.mkdtemp(dir="/tmp", prefix="trappy_doc", suffix = ".tempDi... |
4,919 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Recommendations on GCP with TensorFlow and WALS with Cloud Composer
This lab is adapted from the original solution created by lukmanr
This project deploys a solution for a recommendation se... | Python Code:
%%bash
pip install sh --upgrade pip # needed to execute shell scripts later
Explanation: Recommendations on GCP with TensorFlow and WALS with Cloud Composer
This lab is adapted from the original solution created by lukmanr
This project deploys a solution for a recommendation service on GCP, using the WALS... |
4,920 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Download and get info from all EIA-923 Excel files
This setup downloads all the zip files, extracts the contents, and identifies the correct header row in the correct file. I'm only g... | Python Code:
%matplotlib inline
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
import glob
import numpy as np
import requests
from bs4 import BeautifulSoup
from urllib import urlretrieve
import zipfile
import fnmatch
url = 'https://www.eia.gov/electricity/data/eia923'
r = requests.g... |
4,921 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Day 11 - pre-class assignment
Goals for today's pre-class assignment
Use random number generators to create a sequence of random floats and integers
Create a function and use it to do someth... | Python Code:
# Imports the functionality that we need to display YouTube videos in a Jupyter Notebook.
# You need to run this cell before you run ANY of the YouTube videos.
from IPython.display import YouTubeVideo
# WATCH THE VIDEO IN FULL-SCREEN MODE
YouTubeVideo("fF841G53fGo",width=640,height=360) # random number... |
4,922 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Orientation with IKPy
In this Notebook, we'll demonstrate inverse kinematics on orientation on a baxter robot
Step1: Inverse Kinematics with Orientation
Step2: Mastering orientation
Orient... | Python Code:
# Some necessary imports
import numpy as np
from ikpy.chain import Chain
from ikpy.utils import plot
# Optional: support for 3D plotting in the NB
%matplotlib widget
# turn this off, if you don't need it
# First, let's import the baxter chains
baxter_left_arm_chain = Chain.from_json_file("../resources/baxt... |
4,923 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Boosting a decision stump
The goal of this notebook is to implement your own boosting module.
Brace yourselves! This is going to be a fun and challenging assignment.
Use SFrames to do some f... | Python Code:
import numpy as np
import pandas as pd
import json
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Boosting a decision stump
The goal of this notebook is to implement your own boosting module.
Brace yourselves! This is going to be a fun and challenging assignment.
Use SFrames to do some fea... |
4,924 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Wayne H Nixalo - 13 June 2017
Practical Deep Learning I
Lesson 5 - RNNs, NLP
Code along of char-rnn.ipynb
Step1: Setup
We haven't really looked into the detail of how this works yet - so th... | Python Code:
import theano
%matplotlib inline
import os, sys
sys.path.insert(1, os.path.join('utils'))
import utils; reload(utils)
from utils import *
from __future__ import print_function, division
from keras.layers import TimeDistributed, Activation
# https://keras.io/layers/wrappers/
# [Doc:TimeDistributed] this wra... |
4,925 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Working Dir
Step1: Filename
Step2: Output Prefix
Step3: Others
Step4: Parse CUE
Step5: Covert Files | Python Code:
WORKING_DIR = u"/path/to/folder/to/music"
Explanation: Working Dir: It's supposed that your commands are run under this folder.
End of explanation
FILENAME_PREFIX = u"filename_without_ext"
FILENAME_EXTENSION = u"wav"
Explanation: Filename: This is the filename prefix. For example, if your files are CDImage... |
4,926 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Item cold-start
Step1: Let's examine the data
Step2: The training and test set are divided chronologically
Step3: As a means of sanity checking, let's calculate the model's AUC on the tra... | Python Code:
import numpy as np
from lightfm.datasets import fetch_stackexchange
data = fetch_stackexchange('crossvalidated',
test_set_fraction=0.1,
indicator_features=False,
tag_features=True)
train = data['train']
test = data['test']
Exp... |
4,927 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Report03 - Nathan Yee
This notebook contains report03 for computational baysian statistics fall 2016
MIT License
Step2: The sock problem
Created by Yuzhong Huang
There are two drawers of so... | Python Code:
from __future__ import print_function, division
% matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import math
import numpy as np
from thinkbayes2 import Pmf, Cdf, Suite, Joint
import thinkplot
Explanation: Report03 - Nathan Yee
This notebook contains report03 for computational baysian s... |
4,928 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1>**ReadMe**</h1>
An important part in the profitability of a credit card product is the issuer's ability to detect and deny fraud. Purchase fraud can cost as much as 0.10% of purchase vol... | Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.gridspec as gridspec
import xgboost as xgb
from sklearn.model_selection import train_test_split
transactions = pd.read_csv('creditcard.csv')
Explanation: <h1>**ReadMe**</h1>
An important part in ... |
4,929 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This is a utility notebook/script that goes through and writes all of the possible combinations of solutions to npz files.
Hyperbolic/Parabolic
Retrograde/Direct
CM Frame/M Frame
Equal Mass ... | Python Code:
import numpy as np
import seaborn as sns
from scipy.integrate import odeint
from solve import *
M = 1e1
S = 1e1
Rmin = 25
e = 7 #Eccentricity
r_array = np.array([.2,.3,.4,.5,.6])*Rmin
N_array = np.array([12,18,24,30,36])
steps = 1e3
t = np.linspace(0,.4,steps) #Timescale of 1 billion years
atol=1e-6
rtol=1... |
4,930 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Exercises
Step1: Exercise 1
Step2: b. Spearman Rank Correlation
Find the Spearman rank correlation coefficient for the relationship between x and y using the stats.rankdata function and th... | Python Code:
import numpy as np
import pandas as pd
import scipy.stats as stats
import matplotlib.pyplot as plt
import math
Explanation: Exercises: Spearman Rank Correlation
Lecture Link
This exercise notebook refers to this lecture. Please use the lecture for explanations and sample code.
https://www.quantopian.com/le... |
4,931 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Advanced
Step1: As always, let's do imports and initialize a logger and a new Bundle. See Building a System for more details.
Step2: And we'll attach some dummy datasets. See Datasets fo... | Python Code:
!pip install -I "phoebe>=2.0,<2.1"
Explanation: Advanced: Alternate Backends
Setup
Let's first make sure we have the latest version of PHOEBE 2.0 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
import ph... |
4,932 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Вычисление элементарных функций
Вычисление значения функции на данном аргументе является одной из важнейших задач численных методов.
Несмотря на то, что вы уже огромное число раз вычисляли з... | Python Code:
y=np.linspace(-2,3,100)
x=np.exp(y)
plt.plot(x,y)
plt.xlabel('$x$')
plt.ylabel('$y=\ln x$')
plt.show()
Explanation: Вычисление элементарных функций
Вычисление значения функции на данном аргументе является одной из важнейших задач численных методов.
Несмотря на то, что вы уже огромное число раз вычисляли зн... |
4,933 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Running a model and loading the simulation it produces
Very simple example with the stationary dynamic system defined in test_lake.yaml. Basically it represents a stationary reservoir whose ... | Python Code:
from subprocess import Popen, PIPE, STDOUT
p = Popen(["../pydmmt/pydmmt.py", "test_lake.yml"], stdin=PIPE, stdout=PIPE, stderr=STDOUT)
output = p.communicate(".3".encode('utf-8'))[0]
# trim the '\n' newline char
print(output[:-1].decode('utf-8'))
Explanation: Running a model and loading the simulation it p... |
4,934 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
3. How to Setup the Initial Condition
Here, we explain the basics of World classes. In E-Cell4, six types of World classes are supported now
Step1: 3.1. Common APIs of World
Even though Wor... | Python Code:
from ecell4_base.core import *
Explanation: 3. How to Setup the Initial Condition
Here, we explain the basics of World classes. In E-Cell4, six types of World classes are supported now: spatiocyte.SpatiocyteWorld, egfrd.EGFRDWorld, bd.BDWorld, meso.MesoscopicWorld, gillespie.GillespieWorld, and ode.ODEWorl... |
4,935 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Rømer and Light Travel Time Effects (ltte)
Setup
Let's first make sure we have the latest version of PHOEBE 2.1 installed. (You can comment out this line if you don't use pip for your instal... | Python Code:
!pip install -I "phoebe>=2.1,<2.2"
Explanation: Rømer and Light Travel Time Effects (ltte)
Setup
Let's first make sure we have the latest version of PHOEBE 2.1 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explana... |
4,936 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction to Programming
Step1: Question
Step2: Passing values to functions
Step3: Conclusion
Step4: Initialization of variables within function definition
Step5: * operator
1. Unpa... | Python Code:
#Example_1: return keyword
def straight_line(slope,intercept,x):
"Computes straight line y value"
y = slope*x + intercept
return y
print("y =",straight_line(1,0,5)) #Actual Parameters
print("y =",straight_line(0,3,10))
#By default, arguments have a positional behaviour
#Each of the parameters h... |
4,937 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Hough Transform
Step1: Hough transform combined with a polygonal mask
Notice that lines are more well-defined | Python Code:
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
# convert to grayscale and smooth with a Gaussian
img = mpimg.imread('testimg.jpg')
gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
kernel_size = 5
blurred = cv2.GaussianBlur(gray_img, (kernel_size, kernel_size)... |
4,938 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Prepare babyweight dataset.
Learning Objectives
Setup up the environment
Preprocess natality dataset
Augment natality dataset
Create the train and eval tables in BigQuery
Export data from Bi... | Python Code:
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
!pip install --user google-cloud-bigquery==1.25.0
Explanation: Prepare babyweight dataset.
Learning Objectives
Setup up the environment
Preprocess natality dataset
Augment natality dataset
Create the train and eval tables in BigQuery
Export... |
4,939 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
FastText Model
Introduces Gensim's fastText model and demonstrates its use on the Lee Corpus.
Step1: Here, we'll learn to work with fastText library for training word-embedding
models, savi... | Python Code:
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
Explanation: FastText Model
Introduces Gensim's fastText model and demonstrates its use on the Lee Corpus.
End of explanation
from pprint import pprint as print
from gensim.models.fasttext import Fast... |
4,940 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Head model and forward computation
The aim of this tutorial is to be a getting started for forward computation.
For more extensive details and presentation of the general
concepts for forwar... | Python Code:
import os.path as op
import mne
from mne.datasets import sample
data_path = sample.data_path()
# the raw file containing the channel location + types
sample_dir = op.join(data_path, 'MEG', 'sample',)
raw_fname = op.join(sample_dir, 'sample_audvis_raw.fif')
# The paths to Freesurfer reconstructions
subjects... |
4,941 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Enter Team Member Names here (double click to edit)
Step1: Question 1
Step2: Question 3
Step3: <a id="svm_using"></a>
<a href="#top">Back to Top</a>
Using Linear SVMs
Exercise 1
Step4: O... | Python Code:
# fetch the images for the dataset
# this will take a long time the first run because it needs to download
# after the first time, the dataset will be save to your disk (in sklearn package somewhere)
# if this does not run, you may need additional libraries installed on your system (install at your own ri... |
4,942 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Creación de las imágenes georreferenciadas
El resultado final de la selección de puntos en NightCitiesISS y su correspondencia con las coordenadas, permite crear un fichero en formato GeoTIF... | Python Code:
import urllib2
import json
import asciitable
import time
import Image
idISS = 'ISS030-E-211378'
dirImagenesISS = 'images/'
dirGeoTIFF = 'geotiff/'
dirPuntosQGIS = 'puntosQGIS/'
dirPuntosGlobal = 'puntosGlobal/'
dirScriptsGDAL = 'scriptsGdal/'
def getKey(item):
return item[0]
hayProxy = False
# cargo las t... |
4,943 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2020 The TensorFlow Authors.
Step1: Load and preprocess images
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step2: Download the flo... | 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... |
4,944 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1>Содержание<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Форматирование" data-toc-modified-id="Форматирование-1">Форматирование</a></span><u... | Python Code:
# Правильно
if 1 == 3:
print(1)
if 2 == 3:
print(2)
# Неверно
if 1 == 3:
print(1)
if 2 == 3:
print(2)
Explanation: <h1>Содержание<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Форматирование" data-toc-modified-id="Форматирование-... |
4,945 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Advanced SQLAlchemy Queries
Step1: Using MySql
Import the create_engine function from the sqlalchemy library.
Create an engine to the census database by concatenating the following strings ... | Python Code:
# import
Explanation: Advanced SQLAlchemy Queries
End of explanation
# # Import create_engine function
# from sqlalchemy import create_engine
# # Create an engine to the census database
# engine = create_engine('mysql+pymysql://student:datacamp@courses.csrrinzqubik.us-east-1.rds.amazonaws.com:3306/census')... |
4,946 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The Info data structure
This tutorial describes the
Step1: As seen in the introductory tutorial <tut-overview>, when a
Step2: However, it is not strictly necessary to load the
Step... | Python Code:
import os
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_filt-0-40_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file)
Explanation: The Info data structure
This... |
4,947 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Seaice
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', 'nasa-giss', 'sandbox-3', 'seaice')
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: NASA-GISS
Source ID: SANDBOX-3
Topic: Seaice
Sub-Topics: Dynamics, The... |
4,948 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Model Architectures
Step1: We also set up the backend and load the data.
Step2: Now its your turn! Set up the branch nodes and layer structure above. Some tips
Step3: Now let's fit our mo... | Python Code:
from neon.callbacks.callbacks import Callbacks
from neon.initializers import Gaussian
from neon.layers import GeneralizedCost, Affine, BranchNode, Multicost, SingleOutputTree
from neon.models import Model
from neon.optimizers import GradientDescentMomentum
from neon.transforms import Rectlin, Logistic, Sof... |
4,949 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Dans ce notebook, nous allons essayer de faire du text mining pour récuper des versions locales des programmes des présidentielles 2017 des candidats suivants
Step1: François Fillon
Le pro... | Python Code:
from bs4 import BeautifulSoup
import requests
import re
import pandas as pd
from ipywidgets import interact
def make_df_from_props_sources(props_sources):
"Makes a big dataframe from props_sources."
dfs = []
for key in props_sources:
df = pd.DataFrame(props_sources[key], columns=['propo... |
4,950 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
PyTorch Implementation
Simple version
가장 일반적인 파이토치 구현 방법으로 케라스된 예제 1-1을 변환한다.
Step1: Detail version with monitoring variables
파이토치로 변환된 코드가 제대로 동작하는지 중간 중간을 모니터링 한다.
Step2: Compatible vers... | Python Code:
import torch
import numpy as np
x = np.array([0, 1, 2, 3, 4]).astype('float32').reshape(-1,1)
y = x * 2 + 1
class Model(torch.nn.Module):
def __init__(self):
super(Model,self).__init__()
self.layer = torch.nn.Linear(1,1)
def forward(self, x):
return self.layer(x)
model = Mo... |
4,951 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Model checking and diagnostics
Convergence Diagnostics
Valid inferences from sequences of MCMC samples are based on the
assumption that the samples are derived from the true posterior
distri... | Python Code:
%matplotlib inline
from pymc.examples import gelman_bioassay
from pymc import MCMC, Matplot, Metropolis
import seaborn as sns; sns.set_context('notebook')
M = MCMC(gelman_bioassay)
M.use_step_method(Metropolis, M.alpha, scale=0.001)
M.sample(1000, tune_interval=1000)
Matplot.plot(M.alpha)
Explanation: Mode... |
4,952 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Homework #4
These problem sets focus on list comprehensions, string operations and regular expressions.
Problem set #1
Step1: In the following cell, complete the code with an expression tha... | Python Code:
numbers_str = '496,258,332,550,506,699,7,985,171,581,436,804,736,528,65,855,68,279,721,120'
Explanation: Homework #4
These problem sets focus on list comprehensions, string operations and regular expressions.
Problem set #1: List slices and list comprehensions
Let's start with some data. The following cell... |
4,953 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Exporting Epochs to Pandas DataFrames
This tutorial shows how to export the data in
Step1: Next we'll load a list of events from file, map them to condition names with
an event dictionary,... | Python Code:
import os
import matplotlib.pyplot as plt
import seaborn as sns
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_filt-0-40_raw.fif')
raw = mne.io.read_raw_fif(sample_da... |
4,954 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Ejemplo de word2vec con gensim
En la siguiente celda, importamos las librerías necesarias y configuramos los mensajes de los logs.
Step1: Entrenamiento de un modelo
Implemento una clase Cor... | Python Code:
import gensim, logging, os
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
Explanation: Ejemplo de word2vec con gensim
En la siguiente celda, importamos las librerías necesarias y configuramos los mensajes de los logs.
End of explanation
class Corpus(object):
... |
4,955 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
What is an efficient way of splitting a column into multiple rows using dask dataframe? For example, let's say I have a csv file which I read using dask to produce the following das... | Problem:
import pandas as pd
df = pd.DataFrame([["A", "Z,Y"], ["B", "X"], ["C", "W,U,V"]], index=[1,2,3], columns=['var1', 'var2'])
def g(df):
return df.drop('var2', axis=1).join(df.var2.str.split(',', expand=True).stack().
reset_index(drop=True, level=1).rename('var2'))
resu... |
4,956 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
..# Activ Spyder - Captura de página do ActivUfrj
* This file is part of program Activ Spyder
* Copyright © 2022 Carlo Oliveira carlo@nce.... | Python Code:
import pandas as pd
df = pd.read_json("../author_data.json")
df.info()
df
Explanation: ..# Activ Spyder - Captura de página do ActivUfrj
* This file is part of program Activ Spyder
* Copyright © 2022 Carlo Oliveira carlo@nce.ufrj.b... |
4,957 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Programming hands on
習うより慣れろ！(narau yori narero) more practice, less learning; practice makes perfect.
a + b
input
Step1: a+b modified (1)
Add grand total at the end of line.
input
Step2:... | Python Code:
# write answer
Explanation: Programming hands on
習うより慣れろ！(narau yori narero) more practice, less learning; practice makes perfect.
a + b
input: each line contains 2 integer
output: print sum of the 2 integer
input: input_ab.txt, output: standard out
```
sample input:
1 1
100 150
123 321
11112222 22223333... |
4,958 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2018 The TensorFlow Hub Authors.
Licensed under the Apache License, Version 2.0 (the "License");
Step3: 使用膨胀 3D CNN 进行动作识别
<table class="tfo-notebook-buttons" align="left">
<td>... | Python Code:
# Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... |
4,959 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Transfer Learning
Most of the time you won't want to train a whole convolutional network yourself. Modern ConvNets training on huge datasets like ImageNet take weeks on multiple GPUs. Instea... | Python Code:
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
vgg_dir = 'tensorflow_vgg/'
# Make sure vgg exists
if not isdir(vgg_dir):
raise Exception("VGG directory doesn't exist!")
class DLProgress(tqdm):
last_block = 0
def hook(self, block_num=1, block_size=... |
4,960 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Class Session 4 Exercise
Step1: Now, define a function that returns the index numbers of the neighbors of a vertex i, when the
graph is stored in adjacency matrix format. So your function... | Python Code:
import numpy as np
import igraph
import timeit
import itertools
Explanation: Class Session 4 Exercise:
Comparing asymptotic running time for enumerating neighbors of all vertices in a graph
We will measure the running time for enumerating the neighbor vertices for three different data structures for repres... |
4,961 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Lecture 7
Software design, documentation, and testing
Design of a program
From the Practice of Programming
Step3: Documenting Invariants
An invariant is something that is true at som... | Python Code:
def quad_roots(a=1.0, b=2.0, c=0.0):
Returns the roots of a quadratic equation: ax^2 + bx + c = 0.
INPUTS
=======
a: float, optional, default value is 1
Coefficient of quadratic term
b: float, optional, default value is 2
Coefficient of linear term
c: float, optio... |
4,962 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Parse_data_to_tfrecord_library Test
This file consists several function test for the functions in the Parse_data_to_tfrecord_lib file.
Step1: Test function img_to_example()
Step2: Function... | Python Code:
from parse_data_to_tfrecord_lib import img_to_example, read_tfrecord, generate_tfexamples_from_detections, batch_read_write_tfrecords
from PIL import Image # used to read images from directory
import tensorflow as tf
import os
import io
import IPython.display as display
import numpy as np
tf.enable_eager_... |
4,963 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<img src=images/continuum_analytics_b&w.png align="left" width="15%" style="margin-right
Step1: Exercise
Step2: Exercise | Python Code:
# Import the functions from your file
# Create your plots with your new functions
# Test the visualizations in the notebook
from bokeh.plotting import show, output_notebook
# Show climate map
# Show legend
# Show timeseries
Explanation: <img src=images/continuum_analytics_b&w.png align="left" width="15%" s... |
4,964 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Showcase of various CogStat analyses
Below you can see a few examples what analyses are perfomed for a specific task in CogStat. Note that the specific analyses that are applied depend on th... | Python Code:
%matplotlib inline
import os
import warnings
warnings.filterwarnings('ignore')
from cogstat import cogstat as cs
print(cs.__version__)
cs_dir, dummy_filename = os.path.split(cs.__file__) # We use this for the demo data
Explanation: Showcase of various CogStat analyses
Below you can see a few examples what... |
4,965 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Train nodule detector with LUNA16 dataset
Step1: Analyse input data
Let us import annotations
Step2: Lets take a look at some images
Step3: Classes are heaviliy unbalanced, hardly 0.2% pe... | Python Code:
INPUT_DIR = '../../input/'
OUTPUT_DIR = '../../output/lung-cancer/01/'
IMAGE_DIMS = (50,50,50,1)
%matplotlib inline
import numpy as np
import pandas as pd
import h5py
import matplotlib.pyplot as plt
import sklearn
import os
import glob
from modules.logging import logger
import modules.utils as utils
from m... |
4,966 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Automatically find the center of the speckle pattern and most intense rings
Step1: Two examples to demonstarte automatically find the center of the speckle pattern and most intense 4 rings
... | Python Code:
import skbeam.core.roi as roi
import numpy as np
import matplotlib.pyplot as plt
%matplotlib notebook
x = np.linspace(-5,5,200)
X,Y = np.meshgrid(x,x)
Z = 100*np.cos(np.sqrt(x**2 + Y**2))**2 + 50
center, image, radii = roi.auto_find_center_rings(Z, sigma=20, no_rings=5)
fig, ax = plt.subplots()
ax.scatter(... |
4,967 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data Generation
Step1: Solution
Step2: Use matching indices
Instead of iterating through indices, one can use them directly to parallelize the operations with Numpy.
Step3: Use a library
... | Python Code:
np.random.seed(10)
p, q = (np.random.rand(i, 2) for i in (4, 5))
p_big, q_big = (np.random.rand(i, 80) for i in (100, 120))
print(p, "\n\n", q)
Explanation: Data Generation
End of explanation
def naive(p, q):
''' fill your code in here...
'''
Explanation: Solution
End of explanation
rows, cols = np... |
4,968 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial Brief
This tutorial is an introduction to Python 3. This should give you the set of pythonic skills that you will need to proceed with this tutorial series.
If you don't have the Ju... | Python Code:
1+2
1+1
1+2
Explanation: Tutorial Brief
This tutorial is an introduction to Python 3. This should give you the set of pythonic skills that you will need to proceed with this tutorial series.
If you don't have the Jupyter installed, shame on you. No just kidding you can follow this tutorial using an online ... |
4,969 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
No Show Appointments Analysis
<a id='intro'></a>
Introduction
Purpose To perform a Data analysis on a sample Dataset of No-show Appointments
This Dataset contains the records of the patients... | Python Code:
# Render plots inline
%matplotlib inline
# Import Libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# Set style for all graphs
sns.set(style="whitegrid")
# Read in the Dataset, creat dataframe
appointment_data = pd.read_csv('noshow.csv')
# Print the firs... |
4,970 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
How to Compare LDA Models
Demonstrates how you can visualize and compare trained topic models.
Step2: First, clean up the 20 Newsgroups dataset. We will use it to fit LDA.
Step3: Second, f... | Python Code:
# sphinx_gallery_thumbnail_number = 2
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
Explanation: How to Compare LDA Models
Demonstrates how you can visualize and compare trained topic models.
End of explanation
from string import punctuation
from... |
4,971 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Problem
The number of shoes sold by an e-commerce company during the first three months(12 weeks) of the year were
Step1: On average, the sales after optimization is more than the sales bef... | Python Code:
import numpy as np
import seaborn as sns
sns.set(color_codes=True)
%matplotlib inline
#Load the data
before_opt = np.array([23, 21, 19, 24, 35, 17, 18, 24, 33, 27, 21, 23])
after_opt = np.array([31, 28, 19, 24, 32, 27, 16, 41, 23, 32, 29, 33])
before_opt.mean()
after_opt.mean()
observed_difference = after_... |
4,972 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2019 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License");
Step1: 使用 tf.data 加载 pandas dataframes
<table class="tfo-notebook-buttons" align="left... | 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... |
4,973 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Advent of Code 2017
December 4th
To ensure security, a valid passphrase must contain no duplicate words.
For example
Step1: I'll assume the input is a Joy sequence of sequences of integers.... | Python Code:
from notebook_preamble import J, V, define
Explanation: Advent of Code 2017
December 4th
To ensure security, a valid passphrase must contain no duplicate words.
For example:
aa bb cc dd ee is valid.
aa bb cc dd aa is not valid - the word aa appears more than once.
aa bb cc dd aaa is valid - aa and aaa coun... |
4,974 | Given the following text description, write Python code to implement the functionality described.
Description:
Number of ways to arrange a word such that no vowels occur together
Function to check if a character is vowel or consonent ; Function to calculate factorial of a number ; Calculating no of ways for arranging v... | Python Code:
def isVowel(ch ) :
if(ch == ' a ' or ch == ' e ' or ch == ' i ' or ch == ' o ' or ch == ' u ' ) :
return True
else :
return False
def fact(n ) :
if(n < 2 ) :
return 1
return n * fact(n - 1 )
def only_vowels(freq ) :
denom = 1
cnt_vwl = 0
for itr in freq :
if(isVow... |
4,975 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example with real audio recordings
The iterations are dropped in contrast to the offline version. To use past observations the correlation matrix and the correlation vector are calculated re... | Python Code:
channels = 8
sampling_rate = 16000
delay = 3
alpha=0.9999
taps = 10
frequency_bins = stft_options['size'] // 2 + 1
Explanation: Example with real audio recordings
The iterations are dropped in contrast to the offline version. To use past observations the correlation matrix and the correlation vector are ca... |
4,976 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
qp Demo
Alex Malz & Phil Marshall
In this notebook we use the qp module to approximate some simple, standard, 1-D PDFs using sets of quantiles, samples, and histograms, and assess their rela... | Python Code:
import numpy as np
import scipy.stats as sps
import scipy.interpolate as spi
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
%matplotlib inline
import qp
Explanation: qp Demo
Alex Malz & Phil Marshall
In this notebook we use the qp module to approximate some simple, standard, 1-D PD... |
4,977 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Stats practice
Testing for normality
Step1: Make probability plots
Step2: Interesting. Normal distribution follows the quantiles well and has the highest $R^2$ value, but both the uniform ... | Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
from random import normalvariate, uniform, weibullvariate
# Make several sets of data; one randomly sampled
# from a normal distribution and others that aren't.
n = 100
d_norm = [normalvariate(0,1) for x in range(n)]
d_unif = [uniform(0,1) for x in r... |
4,978 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Modelado de un sistema con ipython
Para el correcto funcionamiento del extrusor de filamento, es necesario regular correctamente la temperatura a la que está el cañon. Por ello se usará un s... | Python Code:
#Importamos las librerías utilizadas
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pylab as plt
#Mostramos las versiones usadas de cada librerías
print ("Numpy v{}".format(np.__version__))
print ("Pandas v{}".format(pd.__version__))
print ("Seaborn v{}".format(sns.__version... |
4,979 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
EEG forward operator with a template MRI
This tutorial explains how to compute the forward operator from EEG data
using the standard template MRI subject fsaverage.
.. caution
Step1: Load t... | Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Joan Massich <mailsik@gmail.com>
#
# License: BSD Style.
import os.path as op
import mne
from mne.datasets import eegbci
from mne.datasets import fetch_fsaverage
# Download fsaverage files
fs_dir = fetch_fsaverage(verbose=True)
subjects... |
4,980 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Topic Modelling
Author
Step1: 1. Corpus acquisition.
In this notebook we will explore some tools for text processing and analysis and two topic modeling algorithms available from Python too... | Python Code:
# %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
# import pylab
# Required imports
from wikitools import wiki
from wikitools import category
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import gensim
imp... |
4,981 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Usage example
To showcase the use of this toolkit, we first create a simple learning task, and then learn an OOM model using spectral learning.
We start by importing the toolkit and initiali... | Python Code:
import tom
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
rand = tom.Random(1234567)
Explanation: Usage example
To showcase the use of this toolkit, we first create a simple learning task, and then learn an OOM model using spectral learning.
We start by importing the toolkit and init... |
4,982 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Gradient Boosting From Scratch
Let's implement gradient boosting from scratch.
Step1: Exploration
Let explore the data before building a model. The goal is to predict the median value of ow... | Python Code:
from __future__ import print_function
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
from sklearn.tree import DecisionTreeRegressor
from tensorflow.keras.datasets import boston_housing
np.random.seed(0)
plt.rcParams['figure.figsize'] = (8.0, 5.0)
plt.rcPar... |
4,983 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Custom Jupyter Widgets
The Hello World Example of the Cookie Cutter
The widget framework is built on top of the Comm framework (short for communication). The Comm framework is a framework t... | Python Code:
import ipywidgets as widgets
from traitlets import Unicode
class HelloWidget(widgets.DOMWidget):
_view_name = Unicode('HelloView').tag(sync=True)
_view_module = Unicode('hello').tag(sync=True)
Explanation: Custom Jupyter Widgets
The Hello World Example of the Cookie Cutter
The widget framework is b... |
4,984 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
MNIST Image Classification with TensorFlow on Cloud AI Platform
This notebook demonstrates how to implement different image models on MNIST using the tf.keras API.
Learning objectives
Unders... | Python Code:
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# Here we'll show the currently installed version of TensorFlow
import tensorflow as tf
print(tf.__version__)
from datetime import datetime
import os
PROJECT = "your-project-id-here" # REPLACE WITH YOUR PROJECT ID
BUCKET = "your-bucket-id-... |
4,985 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sebastian Raschka, 2015
Python Machine Learning
Chapter 13 - Parallelizing Neural Network Training with Theano
Note that the optional watermark extension is a small IPython notebook plugin t... | Python Code:
%load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,matplotlib,theano,keras
# to install watermark just uncomment the following line:
#%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py
Explanation: Sebastian Raschka, 2015
Python Machine Learning
Chapter ... |
4,986 | 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... |
4,987 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
First TensorFlow Graphs
In this notebook, we execute elementary TensorFlow computational graphs.
Load dependencies
Step1: Simple arithmetic
Step2: Simple array arithmetic | Python Code:
import numpy as np
import tensorflow as tf
Explanation: First TensorFlow Graphs
In this notebook, we execute elementary TensorFlow computational graphs.
Load dependencies
End of explanation
x1 = tf.placeholder(tf.float32)
x2 = tf.placeholder(tf.float32)
sum_op = tf.add(x1, x2)
product_op = tf.multiply(x1, ... |
4,988 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lecture 2
Step1: Mass-spring-damper system
The differential equation that governs an unforced, single degree-of-freedom mass-spring-damper system is
$$
m \frac{d^{2}y}{dt^{2}} + \lambda \fr... | Python Code:
from sympy import *
# This initialises pretty printing
init_printing()
from IPython.display import display
# This command makes plots appear inside the browser window
%matplotlib inline
Explanation: Lecture 2: second-order ordinary differential equations
We now look at solving second-order ordinary differe... |
4,989 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Regression Week 3
Step1: Next we're going to write a polynomial function that takes an SArray and a maximal degree and returns an SFrame with columns containing the SArray to all the powers... | Python Code:
import graphlab
Explanation: Regression Week 3: Assessing Fit (polynomial regression)
In this notebook you will compare different regression models in order to assess which model fits best. We will be using polynomial regression as a means to examine this topic. In particular you will:
* Write a function t... |
4,990 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Image Classification
In this project, you'll classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. You'll preprocess the images... | Python Code:
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
# Use Floyd's cifar-10 dataset if present
floyd_cifa... |
4,991 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
3. Data preparation
Step1: 3.1 Select Data
Outputs
Step2: 3.3 Construct Data
Outputs | Python Code:
import nltk
import pandas as pd
import math
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import gridspec
from sklearn import datasets, linear_model
import numpy as np
from numbers import Number
from sklearn import preprocessing
def correlation_matrix(df,figsize=(15,1... |
4,992 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Land
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify do... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nerc', 'sandbox-3', 'land')
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: NERC
Source ID: SANDBOX-3
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energ... |
4,993 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Today's meeting opened the topic of building interactive figures in Python. This notebook will show an example of using ipywidgets module, more specifically interact() function. The full doc... | Python Code:
import warnings
warnings.filterwarnings('ignore')
Explanation: Today's meeting opened the topic of building interactive figures in Python. This notebook will show an example of using ipywidgets module, more specifically interact() function. The full documentation can be found on the ipywidgets website, but... |
4,994 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Integration Exercise 1
Imports
Step2: Trapezoidal rule
The trapezoidal rule generates a numerical approximation to the 1d integral
Step3: Now use scipy.integrate.quad to integrate the f an... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy import integrate
Explanation: Integration Exercise 1
Imports
End of explanation
def trapz(f, a, b, N):
Integrate the function f(x) over the range [a,b] with N points.
k = np.arange(1,N)
h = (b-a)/N
I = h*0.5*f(... |
4,995 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Live-updating multi-tau one-time correlation with synthetic and real data
Step1: First, let's demo with synthetic data.
The plot a few cells down should live update with the first value of ... | Python Code:
from skbeam.core.correlation import lazy_one_time
import numpy as np
import time as ttime
import matplotlib.pyplot as plt
%matplotlib notebook
Explanation: Live-updating multi-tau one-time correlation with synthetic and real data
End of explanation
num_levels = 5
num_bufs = 4 # must be even
xdim = 512
ydi... |
4,996 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
MatPlotLib Basics
Draw a line graph
Step1: Mutiple Plots on One Graph
Step2: Save it to a File
Step3: Adjust the Axes
Step4: Add a Grid
Step5: Change Line Types and Colors
Step6: Label... | Python Code:
%matplotlib inline
from scipy.stats import norm
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(-3, 3, 0.001)
plt.plot(x, norm.pdf(x))
plt.show()
Explanation: MatPlotLib Basics
Draw a line graph
End of explanation
plt.plot(x, norm.pdf(x))
plt.plot(x, norm.pdf(x, 1.0, 0.5))
plt.show()
Expla... |
4,997 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Basic Concepts
What is "learning from data"?
In general Learning from Data is a scientific discipline that is concerned with the design and development of algorithms that allow computers to ... | Python Code:
# numerical derivative at a point x
def f(x):
return x**2
def fin_dif(x,
f,
h = 0.00001):
'''
This method returns the derivative of f at x
by using the finite difference method
'''
return (f(x+h) - f(x))/h
x = 2.0
print "{:2.4f}".format(fin_dif(x,f))
Explan... |
4,998 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1A.e - Correction de l'interrogation écrite du 14 novembre 2014
coût algorithmique, calcul de séries mathématiques
Step1: Enoncé 1
Q1
Le code suivant produit une erreur. Corrigez le program... | Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
Explanation: 1A.e - Correction de l'interrogation écrite du 14 novembre 2014
coût algorithmique, calcul de séries mathématiques
End of explanation
nbs = [ 1, 5, 4, 7 ] #
for n in nbs: #
s += n #
Explanation: Enoncé 1... |
4,999 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
From raw data to dSPM on SPM Faces dataset
Runs a full pipeline using MNE-Python
Step1: Load and filter data, set up epochs
Step2: Visualize fields on MEG helmet
Step3: Look at the whiten... | Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne.datasets import spm_face
from mne.preprocessing import ICA, create_eog_epochs
from mne import io, combine_evoked
f... |
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