Unnamed: 0 int64 0 16k | text_prompt stringlengths 110 62.1k | code_prompt stringlengths 37 152k |
|---|---|---|
4,500 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Decomposition Example
First step, set the paths to where to find the motif dictionary and associated code. Note that these are available at http
Step1: Load some motifs from the motif datab... | Python Code:
motifdbcodepath = '/Users/simon/git/motifdb/code/utilities/'
motifdbpath = '/Users/simon/git/motifdb/motifs/'
Explanation: Decomposition Example
First step, set the paths to where to find the motif dictionary and associated code. Note that these are available at http://github.com/sdrogers/motifdb
End of ex... |
4,501 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1> Homework Set 6</h1>
Matt Buchovecky
Astro 283 / Fitz
Step1: <h2> Problem 1
Step7: To estimate the values of $(\alpha,\beta)$, we maximize the posterior function $p(\alpha,\beta\mid{D}... | Python Code:
import numpy as np
from scipy import optimize, special
from matplotlib import pyplot as plt
from astropy.io import fits
%matplotlib inline
Explanation: <h1> Homework Set 6</h1>
Matt Buchovecky
Astro 283 / Fitz
End of explanation
# open the data file and load data into a list of points
infile = open("./sa... |
4,502 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Atmoschem
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', 'ncc', 'sandbox-1', 'atmoschem')
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: NCC
Source ID: SANDBOX-1
Topic: Atmoschem
Sub-Topics: Transport, Emiss... |
4,503 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Counting Stars with NumPy
This example introduces some of the image processing capabilities available with NumPy and the SciPy ndimage package. More extensive documentation and tutorials ca... | Python Code:
import scipy.ndimage as ndi
import requests
from StringIO import StringIO
#Pick an image from the list above and fetch it with requests.get
#The default picture here is of M45 - the Pleiades Star Cluster.
response = requests.get("http://imgsrc.hubblesite.org/hu/db/images/hs-2004-20-a-large_web.jpg")
pic =... |
4,504 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
It is assumed that this notebook is in the same folder as the python-files randomGraphsNumerical.py, transitionMatrix.py, transitionMatrixDirected.py, AmplifierQ.py and Plot.py. Also there h... | Python Code:
popSize = 4 # This is the number of nodes in the network
update = 'BD' # Either 'BD' or 'DB' for Birth-death or death-Birth updating respectively
direction = 'undirected' # Either 'directed' or 'undirected' graphs are used
stepSize = 0.05 # Step size for the probability for each link in the network to be p... |
4,505 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Image Space Projection using Autoencoders
In this example we are going to autoencode the faces of the olivetti dataset and try to reconstruct them back.
Step1: http
Step3: We now need some... | Python Code:
%matplotlib inline
import matplotlib
import numpy as np
import pandas as pd
import scipy.io
import matplotlib.pyplot as plt
from IPython.display import Image, display
import h2o
from h2o.estimators.deeplearning import H2OAutoEncoderEstimator
h2o.init()
Explanation: Image Space Projection using Autoencoders... |
4,506 | 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 parts of this notebook are from Derivative Approximation by Finite Differences by David E... | 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 parts of this note... |
4,507 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Template for test
Step1: Controlling for Random Negatve vs Sans Random in Imbalanced Techniques using S, T, and Y Phosphorylation.
Included is N Phosphorylation however no benchmarks are av... | Python Code:
from pred import Predictor
from pred import sequence_vector
from pred import chemical_vector
Explanation: Template for test
End of explanation
par = ["pass", "ADASYN", "SMOTEENN", "random_under_sample", "ncl", "near_miss"]
for i in par:
print("y", i)
y = Predictor()
y.load_data(file="Data/Train... |
4,508 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
df_infl_ctry.rename(columns = dic)
tt = df_infl_ctry.copy()
tt['month'] = tt.index.month
tt['year'] = tt.index.year
melted_df = pd.melt(tt,id_vars=['month','year'])
melted_df.head()
Step1: ... | Python Code:
df_infl_ctry['min'] = df_infl_ctry.apply(min,axis=1)
df_infl_ctry['max'] = df_infl_ctry.apply(max,axis=1)
df_infl_ctry['mean'] = df_infl_ctry.apply(np.mean,axis=1)
df_infl_ctry['mode'] = df_infl_ctry.quantile(q=0.5, axis=1)
df_infl_ctry['10th'] = df_infl_ctry.quantile(q=0.10, axis=1)
df_infl_ctry['90th'] =... |
4,509 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Regular Expressions
Regular expressions are text matching patterns described with a formal syntax. You'll often hear regular expressions referred to as 'regex' or 'regexp' in conversation. R... | Python Code:
import re
# List of patterns to search for
patterns = [ 'term1', 'term2' ]
# Text to parse
text = 'This is a string with term1, but it does not have the other term.'
for pattern in patterns:
print 'Searching for "%s" in: \n"%s"' % (pattern, text),
#Check for match
if re.search(pattern, te... |
4,510 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<span style="color
Step1: Here the line (<span style="color
Step2: Replacing the first line with <span style="color
Step3: <img src="Tutorial7/MatplotlibQt.png" width="500" >
This GUI all... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
Explanation: <span style="color: #B40486">BASIC PYTHON FOR RESEARCHERS</span>
by Megat Harun Al Rashid bin Megat Ahmad
last updated: April 14, 2016
<span style="color: #29088A">7. Data Visualization and Plotting</span>
The <span style="color: #0000FF">$Mat... |
4,511 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
WEB_API Scrapper
Step1: I would like to get the Best Seller list for the Month of October 2015. First I signed up to the New York Times API, and afterwards received a key in seconds.
Step2:... | Python Code:
import urllib2
import json
import pandas as pd
Explanation: WEB_API Scrapper
End of explanation
url = urllib2.urlopen('http://api.nytimes.com/svc/books/v3/lists/2015-10-01/hardcover-fiction.json?callback=books&sort-by=rank&sort-order=DESC&api-key=efb1f6ff386ce33c0b913d44bce40fd8%3A10%3A73015082')
Explanati... |
4,512 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h2 align="center">点击下列图标在线运行HanLP</h2>
<div align="center">
<a href="https
Step1: 加载模型
HanLP的工作流程是先加载模型，模型的标示符存储在hanlp.pretrained这个包中，按照NLP任务归类。
Step2: 调用hanlp.load进行加载，模型会自动下载到本地缓存：
... | Python Code:
!pip install hanlp -U
Explanation: <h2 align="center">点击下列图标在线运行HanLP</h2>
<div align="center">
<a href="https://colab.research.google.com/github/hankcs/HanLP/blob/doc-zh/plugins/hanlp_demo/hanlp_demo/zh/srl_mtl.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" ... |
4,513 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<b>This notebook analyses senders, repliers and interactions.</b>
What it does
Step1: Let's compute and plot the top senders
Step2: Let's compute and plot the top repliers
Step3: Let's co... | Python Code:
%matplotlib inline
import bigbang.mailman as mailman
from bigbang.archive import load as load_archive
import bigbang.graph as graph
import bigbang.process as process
from bigbang.parse import get_date
from bigbang.archive import Archive
import bigbang.twopeople as twoppl
reload(process)
import pandas as pd... |
4,514 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Title
Step1: Create Matrix
Step2: Invert Matrix | Python Code:
# Load library
import numpy as np
Explanation: Title: Invert A Matrix
Slug: invert_a_matrix
Summary: How to invert a matrix in Python.
Date: 2017-09-03 12:00
Category: Machine Learning
Tags: Vectors Matrices Arrays
Authors: Chris Albon
Preliminaries
End of explanation
# Create matrix
matrix = np.arr... |
4,515 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Group Galaxy Catalog for the DR5 Gallery
The purpose of this notebook is to build a group catalog (using a simple friends-of-friends algorithm) from a diameter-limited (D25>5 arcsec) parent ... | Python Code:
import os
import numpy as np
import matplotlib.pyplot as plt
import astropy.units as u
from astropy.table import Table, Column
from astropy.coordinates import SkyCoord
import fitsio
from pydl.pydlutils.spheregroup import spheregroup
%matplotlib inline
LSLGAdir = os.getenv('LSLGA_DIR')
mindiameter = 0.25 # ... |
4,516 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Receiver Operating Characteristics (ROC)
Step1: 1. Binary classification
Step2: Accuracy, precision, recall
Step3: Note
Step4: The problem here is that we're not passing the correct seco... | Python Code:
%matplotlib inline
from IPython.display import Image
import numpy as np
import matplotlib.pyplot as plt
# some classification metrics
# more here:
# http://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics
from sklearn.metrics import (auc, roc_curve, roc_auc_score,
... |
4,517 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
TD 1
Step1: Partie 1
Un langage de programmation permet de décrire avec précision des opérations très simples sur des données. Comme tout langage, il a une grammaire et des mot-clés. La co... | Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
Explanation: TD 1 : Premiers pas en Python
End of explanation
x = 5
y = 10
z = x + y
print (z) # affiche z
Explanation: Partie 1
Un langage de programmation permet de décrire avec précision des opérations très simples sur des données. Comme... |
4,518 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Как помочь нам разобрать протокол?
Вот пример как это делается в простом случае.
Есть устройство на чипе HS1527 и к нему даже нашелся datasheet
Step1: Посмотрим на гистограммы длин импульсо... | Python Code:
# takes filename
# open file, read binary data
# returns numpy.array of impulses (positive integer)
# and pauses (negative integer)
def file_to_data(filename):
pic = open(filename, "rb")
data = []
while True:
buf = pic.read(4)
if not buf or len(buf) != 4:
break
... |
4,519 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Overview
Step1: Extract NN Features
Step2: Predicting Own Labels from Selected Images
within a folder (find class 1, class 0).
(split into test train)
get matrix of img X features X class... | Python Code:
import sys
import os
sys.path.append(os.getcwd()+'/../')
# our lib
from lib.resnet50 import ResNet50
from lib.imagenet_utils import preprocess_input, decode_predictions
#keras
from keras.preprocessing import image
from keras.models import Model
# sklearn
import sklearn
from sklearn.linear_model import Lo... |
4,520 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Theory and Practice of Visualization Exercise 1
Imports
Step1: Graphical excellence and integrity
Find a data-focused visualization on one of the following websites that is a positive examp... | Python Code:
from IPython.display import Image
Explanation: Theory and Practice of Visualization Exercise 1
Imports
End of explanation
# Add your filename and uncomment the following line:
Image(filename='good data viz.png')
Explanation: Graphical excellence and integrity
Find a data-focused visualization on one of the... |
4,521 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Coroutines
A coroutine is a type of subroutine which can have multiple entry and exit points. They're useful for a number of concurrency patterns.
Python introduced coroutines as "Enhanced G... | Python Code:
def print_if_error(error_is="error"):
while True:
line = yield
if line.startswith(error_is):
print(line)
Explanation: Coroutines
A coroutine is a type of subroutine which can have multiple entry and exit points. They're useful for a number of concurrency patterns.
Python int... |
4,522 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1. Kalman Filter applied to 1D Movement with constant velocity
Step1: Parameters
The system under consideration is an object traveling under constant velocity.
Its motion (in both time and ... | Python Code:
# allow use of python3 syntax
from __future__ import division, print_function, absolute_import
import numpy as np
# local script with often used
import kalman as k
# contents of local file kalman.py
# %load kalman.py
import numpy as np
import matplotlib.pyplot as plt
def kalman_predict( A, # transition m... |
4,523 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
python libs for all vis things
Step1: bokeh
Step2: bokeh + ipywidgets | Python Code:
%pylab inline
t = arange(0.0, 1.0, 0.01)
y1 = sin(2*pi*t)
y2 = sin(2*2*pi*t)
import pandas as pd
df = pd.DataFrame({'t': t, 'y1': y1, 'y2': y2})
df.head(10)
Explanation: python libs for all vis things
End of explanation
from bokeh.plotting import figure, output_notebook, show
# output inline with notebook
... |
4,524 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Performance Overview
Here, we will example the performance of FNGS as a function of time on several datasets. These investigations were performed on a 4 core machine (4 threads) with a 4.0 G... | Python Code:
%%script false
## disklog.sh
#!/bin/bash -e
# run this in the background with nohup ./disklog.sh > disk.txt &
#
while true; do
echo "$(du -s $1 | awk '{print $1}')"
sleep 30
done
##cpulog.sh
import psutil
import time
import argparse
def cpulog(outfile):
with open(outfile, 'w') as outf:
... |
4,525 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Fast brain decoding with random sampling and random projections
Andres HOYOS-IDROBO, Gael VAROQUAUX and Bertrand THIRION
PARIETAL TEAM, INRIA, CEA, University Paris-Saclay
Presented on
Step1... | Python Code:
%matplotlib inline
import numpy as np
import time
import matplotlib.pyplot as plt
from nilearn.plotting import plot_stat_map
from nilearn.input_data import NiftiMasker
Explanation: Fast brain decoding with random sampling and random projections
Andres HOYOS-IDROBO, Gael VAROQUAUX and Bertrand THIRION
PARIE... |
4,526 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial on the Analytical Advection kernel in Parcels
While Lagrangian Ocean Analysis has been around since at least the 1980s, the Blanke and Raynaud (1997) paper has really spurred the us... | Python Code:
%pylab inline
from parcels import FieldSet, ParticleSet, ScipyParticle, JITParticle, Variable
from parcels import AdvectionAnalytical, AdvectionRK4, plotTrajectoriesFile
import numpy as np
from datetime import timedelta as delta
import matplotlib.pyplot as plt
Explanation: Tutorial on the Analytical Advect... |
4,527 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
L'objectif est de décrire l'évolution des montants des accises de la TICPE depuis 1993
Import de modules généraux
Step1: Import de fonctions spécifiques à Openfisca Indirect Taxation
Step2:... | Python Code:
import seaborn
seaborn.set_palette(seaborn.color_palette("Set2", 12))
%matplotlib inline
Explanation: L'objectif est de décrire l'évolution des montants des accises de la TICPE depuis 1993
Import de modules généraux
End of explanation
from openfisca_france_indirect_taxation.examples.utils_example import gr... |
4,528 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1. Import the necessary packages to read in the data, plot, and create a linear regression model
Step1: 2. Read in the hanford.csv file
Step2: <img src="images/hanford_variables.png">
3. C... | Python Code:
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
import statsmodels.formula.api as smf
import numpy as np
import scipy as sp
Explanation: 1. Import the necessary packages to read in the data, plot, and create a linear regression model
End of explanation
df = pd.read_csv("hanford.csv... |
4,529 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Read in parquet files from pre-processing
Step1: To make templates
Step2: Add cluster labels to sentences and mentions (entities)
Step3: Get the size of each cluster
Step4: Get the distr... | Python Code:
# do the reading
templates = pd.read_parquet('data/processed_dfs/templates.parquet' )
sentences = pd.read_parquet('data/processed_dfs/sentences.parquet')
mentions = pd.read_parquet('data/processed_dfs/mentions.parquet')
umls = pd.read_parquet('data/processed_dfs/umls.parquet')
sentences.head()
mentions.hea... |
4,530 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
MNIST classification with Vowpal Wabbit
Step1: Train
I found some help with parameters here
Step2: Predict
-t
is for test file
-i
specifies the model file created earlier
-p
whe... | Python Code:
from __future__ import division
import re
import numpy as np
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
%matplotlib inline
#%qtconsole
Explanation: MNIST classification with Vowpal Wabbit
End of explanation
!rm train.vw.cache
!rm mnist_train.model
!vw -d data/mnist_train.v... |
4,531 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Using spaCy for Text Preprocessing
Date
Step1: 1. What is spaCy
spaCy is a free, open-source library for NLP in Python
Providing optimized pipelines for taking models to production, i.e., f... | Python Code:
# Common imports
import numpy as np
import pandas as pd
import zipfile as zp
from termcolor import colored
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
#To wrap long text lines
from IPython.display import HTML, display
def set_css... |
4,532 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Basic Pie Chart
Step1: Update Data
Step2: Display Values
Step3: Enable sort
Step4: Set different styles for selected slices
Step5: For more on piechart interactions, see the Mark Intera... | Python Code:
data = np.random.rand(3)
pie = Pie(sizes=data, display_labels="outside", labels=list(string.ascii_uppercase))
fig = Figure(marks=[pie], animation_duration=1000)
fig
Explanation: Basic Pie Chart
End of explanation
n = np.random.randint(1, 10)
pie.sizes = np.random.rand(n)
Explanation: Update Data
End of exp... |
4,533 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: Transforming an input to a known output
Step2: relation between input and output is linear
Step3: Defining the model to train
untrained single unit (neuron) also out... | Python Code:
# import and check version
import tensorflow as tf
# tf can be really verbose
tf.logging.set_verbosity(tf.logging.ERROR)
print(tf.__version__)
# a small sanity check, does tf seem to work ok?
sess = tf.Session()
hello = tf.constant('Hello TF!')
print(sess.run(hello))
sess.close()
Explanation: <a href="htt... |
4,534 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Práctica 2 - Cinemática directa y dinámica de manipuladores
Una vez obtenida la dinámica del manipulador, tenemos la necesidad de construir una función f para poder simular el comportamiento... | Python Code:
def f(t, x):
# Se importan funciones matematicas necesarias
from numpy import matrix, sin, cos
# Se desenvuelven las variables que componen al estado
q1, q2, q̇1, q̇2 = x
# Se definen constantes del sistema
g = 9.81
m1, m2, J1, J2 = 0.3, 0.2, 0.0005, 0.0002
l1, l2 = 0.4, 0.3... |
4,535 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
SAP Router
The following subsections show a graphical representation of the main protocol packets and how to generate them.
First we need to perform some setup to import the packet classes
S... | Python Code:
from pysap.SAPRouter import *
from IPython.display import display
Explanation: SAP Router
The following subsections show a graphical representation of the main protocol packets and how to generate them.
First we need to perform some setup to import the packet classes:
End of explanation
for command in rout... |
4,536 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
CRA prediction with Regression
Reference link.
Importing packages
This packages will be used to analysis data and train the models.
Step1: Reading data
This command shows a sample from data... | Python Code:
#enconding=utf8
import copy
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
from scipy import stats
from scipy.stats import skew
from scipy.stats.stats import pearsonr
%config InlineBackend.figure_format = 'retina' #set 'png' here when working... |
4,537 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step2: 10. Syntax — Lab exercises
Preparations
Introduction
In this lab, we are going to use the Python Natural Language Toolkit (nltk). It has an API that allows you to create, read, and pa... | Python Code:
import graphviz
import nltk
from nltk import Nonterminal
from nltk.parse.generate import generate
from nltk.tree import Tree
def does_tcl_work():
Checks if Tcl is installed and works (e.g. it won't on a headless server).
tree = nltk.tree.Tree('test', [])
try:
tree._repr_png_()
r... |
4,538 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Illustration of A-projection
How to deal with direction- and baseline-dependent delays when imaging using $A$-kernels.
Step1: Generate baseline coordinates for a short observation with the ... | Python Code:
%matplotlib inline
import sys
sys.path.append('../..')
from matplotlib import pylab
pylab.rcParams['figure.figsize'] = 10, 10
import functools
import numpy
import scipy
import scipy.special
import astropy
import astropy.units as u
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from... |
4,539 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
★ Monte Carlo Simulation To Calculate PI ★
Step1: Necesaary Function For Monte Carlo Simulation
Step2: Monte Carlo Simulation (with Minimal standard random number generator)
Step3: Monte ... | Python Code:
# Import modules
import time
import math
import numpy as np
import scipy
import matplotlib.pyplot as plt
Explanation: ★ Monte Carlo Simulation To Calculate PI ★
End of explanation
def linear_congruential_generator(x, a, b, m):
x = (a * x + b) % m
u = x / m
return u, x, a, b, m
def stdrand(x):
... |
4,540 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a id='top'></a>
Complex vibration modes
Complex vibration modes arise in experimental research and numerical simulations with non proportional damping. In such cases the eigenproblem is non... | Python Code:
import sys
import numpy as np
import scipy as sp
import matplotlib as mpl
print('System: {}'.format(sys.version))
print('numpy version: {}'.format(np.__version__))
print('scipy version: {}'.format(sp.__version__))
print('matplotlib version: {}'.format(mpl.__version__))
Explanation: <a id='top'></a>
Complex... |
4,541 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Predicting Babyweight Using BigQuery ML
Learning Objectives
- Explore the machine learning capabilities of BigQuery
- Learn how to train a linear regression model in BigQuery
- Examine the T... | Python Code:
PROJECT = 'cloud-training-demos' # Replace with your PROJECT
BUCKET = 'cloud-training-bucket' # Replace with your BUCKET
REGION = 'us-central1' # Choose an available region for Cloud MLE
import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT
os.environ['REGION'] = REGION
%%bas... |
4,542 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Implementation of time blocking for compression/serialization and de-serialization/de-compression of wavefields with Devito operators
Introduction
The goal of this tutorial is to prototype t... | Python Code:
# NBVAL_IGNORE_OUTPUT
# Install pyzfp package in the current Jupyter kernel
import sys
!{sys.executable} -m pip install blosc
import blosc
Explanation: Implementation of time blocking for compression/serialization and de-serialization/de-compression of wavefields with Devito operators
Introduction
The goal... |
4,543 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This page briefly goes over the regression metrics found in scikit-learn. The metrics are first calculated with NumPy and then calculated using the higher level functions available in sklear... | Python Code:
from sklearn.linear_model import LinearRegression
from sklearn.datasets import make_regression
import matplotlib.pyplot as plt
%matplotlib inline
#Generate data
regression_data, regression_values = make_regression(n_samples=100,n_features=1,n_informative=1,noise=10)
#Set X, y_true (and shift to quadrant 1)... |
4,544 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Convergence between teaching and doing quant finance with QuantSA
Quantitative finance is a broad term, here I am referring to solving pricing problems in the capital markets space (with all... | Python Code:
import clr # to be able to use the C# library
clr.AddReference("System.Collections")
clr.AddReference(r'C:\Dev\QuantSA\QuantSA\Valuation\bin\Debug\QuantSA.General.dll')
clr.AddReference(r'C:\Dev\QuantSA\QuantSA\Valuation\bin\Debug\QuantSA.Valuation.dll')
from System.Collections.Generic import List
from Qua... |
4,545 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Character-based LSTM
Grab all Chesterton texts from Gutenberg
Step1: Create the Training set
Build a training and test dataset. Take 40 characters and then save the 41st character. We will ... | Python Code:
from nltk.corpus import gutenberg
gutenberg.fileids()
text = ''
for txt in gutenberg.fileids():
if 'chesterton' in txt:
text += gutenberg.raw(txt).lower()
chars = sorted(list(set(text)))
char_indices = dict((c, i) for i, c in enumerate(chars))
indices_char = dict((i, c) for i, c in enu... |
4,546 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Загрузка данных и краткий обзор
Step1: Сравним численность людей, вернувших кредит, и не вернувших его.
Step2: Вспомогательные методы
Step3: Задание 1. Размер кредитного лимита (LIMIT_BAL... | Python Code:
data = pd.read_csv('credit_card_default_analysis.csv', index_col=0)
data.head()
data.shape
data.describe()
Explanation: Загрузка данных и краткий обзор
End of explanation
data.default.value_counts()
Explanation: Сравним численность людей, вернувших кредит, и не вернувших его.
End of explanation
def make_hi... |
4,547 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Intro analysis to the dataset <a class="tocSkip">
Providing a simple visualization and classic statistical data analysis.
Imports
Import dependencies
Step1: Import data
Step2: Cleaning
Dat... | Python Code:
import numpy as np
import pandas as pd
import matplotlib.style as style
style.use('ggplot')
#print(style.available)
import matplotlib.pyplot as plt
%matplotlib inline
import csv
import datetime
from IPython.core.display import display, HTML
Explanation: Intro analysis to the dataset <a class="tocSkip">
Pro... |
4,548 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Modifying data in-place
Many of MNE-Python's data objects (~mne.io.Raw, ~mne.Epochs, ~mne.Evoked,
etc) have methods that modify the data in-place (either optionally or
obligatorily). This ca... | 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_raw.fif')
# the preload flag loads the data into memory now
raw = mne.io.read_raw_fif(sample_data_raw_file, p... |
4,549 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data Wrangling NI House Price Index Data
This is a 'messy' 'blog post' that's just a braindump of a notebook to step through NI House Price Index datasets I was playing around with.
It's mo... | Python Code:
from bs4 import BeautifulSoup
import pandas as pd
import requests
# Pull the latest pages of https://www.finance-ni.gov.uk/publications/ni-house-price-index-statistical-reports and extract links
base_url= 'https://www.finance-ni.gov.uk/publications/ni-house-price-index-statistical-reports'
base_content = r... |
4,550 | Given the following text description, write Python code to implement the functionality described.
Description:
Minimum characters to be replaced to make frequency of all characters same
Function to find the minimum operations to convert given string to another with equal frequencies of characters ; Frequency of charact... | Python Code:
def minOperations(s ) :
freq =[0 ] * 26
n = len(s )
for i in range(n ) :
freq[ord(s[i ] ) - ord(' A ' ) ] += 1
freq . sort(reverse = True )
answer = n
for i in range(1 , 27 ) :
if(n % i == 0 ) :
x = n // i
y = 0
for j in range(i ) :
y += min(freq[j ] , x )
answer = min(an... |
4,551 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Handling spectral energy distributions
This notebook illustrates how to use the sed module of nmmn. This module is very convenient for dealing with spectral energy distributions (SEDs)—the d... | Python Code:
%pylab inline
import nmmn.sed as sed
Explanation: Handling spectral energy distributions
This notebook illustrates how to use the sed module of nmmn. This module is very convenient for dealing with spectral energy distributions (SEDs)—the distributions of luminosity $\nu L_\nu$ as a function of $\nu$.
Oft... |
4,552 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
IA369Z - Reprodutibilidade em Pesquisa Computacional.
testes
teste
teste
teste
teste
teste
Descrição de códigos para devices e coletas
Code Client Device
ESP8266 Runing program language LUA.... | Python Code:
-- Campainha IoT - LHC - v1.1
-- ESP Inicializa pinos, Configura e Conecta no Wifi, Cria conexão TCP
-- e na resposta de um "Tocou" coloca o ESP em modo DeepSleep para economizar bateria.
-- Se nenhuma resposta for recebida em 15 segundos coloca o ESP em DeepSleep.
led_pin = 3
status_led = gpio.LOW
ip_serv... |
4,553 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: Preparing the Dataset
The
MNIST dataset is normalized to [0,1] range as per the explanation here
Step2: Make the train and test datasets
Step3: Visualization
Data l... | Python Code:
#pip install --force-reinstall torch==1.2.0 torchvision==0.4.0 -f https://download.pytorch.org/whl/torch_stable.html
%pylab inline
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset
import torch.utils.data.dataloader as dataloader
impo... |
4,554 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
EX 4
Step1: (二)準備測試資料
利用make_classification產生分類資料，n_features=2表示共有兩個特徵, n_informative=2 代表有兩個類別
所產生之 X
Step2: (三)測試分類器並作圖
接下來這段程式碼有兩個for 迴圈，外迴圈走過三個的dataset，內迴圈則走過所有的分類器。
為求簡要說明，我們將程式碼簡略如下：... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_moons, make_circles, make_classification
from sklearn.neighbors import KNe... |
4,555 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Generate a Cubic Lattice with an Interpenetrating Dual Cubic Lattice
OpenPNM offers several options for generating dual networks. This tutorial will outline the use of the basic CubicDual c... | Python Code:
import scipy as sp
import numpy as np
import openpnm as op
import matplotlib.pyplot as plt
%matplotlib inline
np.random.seed(10)
wrk = op.Workspace() # Initialize a workspace object
wrk.settings['loglevel'] = 50
Explanation: Generate a Cubic Lattice with an Interpenetrating Dual Cubic Lattice
OpenPNM offe... |
4,556 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Single-layer Perceptron For MNIST Dataset
Load the dataset
Step1: Visualize a sample subset of data
Step2: Side Note
Step3: Tensorflow Session
Step4: Evaluating the model | Python Code:
import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
mnist_data = input_data.read_data_sets('/tmp/data', one_hot=True)
Explanation: Single-layer Perceptron For MNIST Dataset
Load the dataset
End of explanation
## Visualize a sample subset of data
import matp... |
4,557 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Optimization Exercise 1
Imports
Step1: Hat potential
The following potential is often used in Physics and other fields to describe symmetry breaking and is often known as the "hat potential... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
Explanation: Optimization Exercise 1
Imports
End of explanation
def hat(x,a,b):
V=-a*x**2+b*x**4
return V
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(1.0, 10.0, 1.0)==-9.0... |
4,558 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Cauchy noise inference example
The Cauchy distribution (a Student-t with 1 degree of freedom) has fatter tails than the normal, meaning that it can be a better approximation to the noise pro... | Python Code:
import pints
import pints.toy as toy
import pints.plot
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats
x = np.linspace(-15, 15, 1000)
y_c = scipy.stats.t.pdf(x, 1, loc=0, scale=1)
y_t = scipy.stats.t.pdf(x, 3, loc=0, scale=1)
y_norm = scipy.stats.norm.pdf(x, 0, 3)
plt.plot(x, y_c, lab... |
4,559 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
DataFrame object
Create SparkContext and SparkSession
Step1: Create a DataFrame object
Creat DataFrame by reading a file
Step2: Create DataFrame with createDataFrame function
From an RDD
E... | Python Code:
# create entry points to spark
try:
sc.stop()
except:
pass
from pyspark import SparkContext, SparkConf
from pyspark.sql import SparkSession
sc=SparkContext()
spark = SparkSession(sparkContext=sc)
Explanation: DataFrame object
Create SparkContext and SparkSession
End of explanation
mtcars = spark.re... |
4,560 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Cahn-Hilliard with Primtive and Legendre Bases
This example uses a Cahn-Hilliard model to compare two different bases representations to discretize the microstructure. One basis representaio... | Python Code:
%matplotlib inline
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
Explanation: Cahn-Hilliard with Primtive and Legendre Bases
This example uses a Cahn-Hilliard model to compare two different bases representations to discretize the microstructure. One basis representai... |
4,561 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data Generators
One-hot encoding classes
Step1: Stratified split into train/val
Step2: Generator class
Step3: Generator instances
Step4: PR-AUC-based Callback
The callback would be used
... | Python Code:
train_df = pd.read_csv('/home/dex/Desktop/ml/cloud data/train.csv')
train_df.head()
train_df = train_df[~train_df['EncodedPixels'].isnull()]
train_df['Image'] = train_df['Image_Label'].map(lambda x: x.split('_')[0])
train_df['Class'] = train_df['Image_Label'].map(lambda x: x.split('_')[1])
classes = train_... |
4,562 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
barf
Step1: Gives a simple framework for defined what the different fields in the data should look like
The parsing is done with third-party libraries | Python Code:
import re
import string
class SequenceModel(object):
def __init__(self, alphabet, flags=re.IGNORECASE):
self.alphabet = alphabet
self.pattern = re.compile(r'[{alphabet}]*$'.format(alphabet=alphabet),
flags=flags)
def __str__(self):
return 'S... |
4,563 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Frost Number Model
Link to this notebook
Step1: Part 1
Adapt the base case configuration to a mean temperature of the coldest month of -13C, and of the warmest month +19.5C (the actual valu... | Python Code:
# Import standard Python modules
import numpy as np
import pandas
import matplotlib.pyplot as plt
# Import the FrostNumber PyMT model
import pymt.models
frost_number = pymt.models.FrostNumber()
Explanation: Frost Number Model
Link to this notebook: https://github.com/csdms/pymt/blob/master/docs/demos/frost... |
4,564 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Chapter 3 - Generative Models
This chapter introduces learning from a bayesian perspective, giving some simple examples and showing how Naive Bayes relies on this kind of theory.
Bayesian co... | Python Code:
coin = bernoulli(0.7)
samples = coin.rvs(20)
num_heads = sum(samples)
num_tails = len(samples) - num_heads
Explanation: Chapter 3 - Generative Models
This chapter introduces learning from a bayesian perspective, giving some simple examples and showing how Naive Bayes relies on this kind of theory.
Bayesian... |
4,565 |
Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
logistic Regression using sklearn
| Python Code::
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)
model.fit(X_train, y_train)
|
4,566 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Calculate Asym vs. Emin from bhm_e
Rewriting calc_Asym_vs_emin_energies for bhm_e.
Generate Asym_df for a specific dataset.
P. Schuster
July 18, 2018
Step1: Load data
Step2: Functionalize | Python Code:
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='ticks')
import sys
import os
import os.path
import scipy.io as sio
import time
import numpy as np
np.set_printoptions(threshold=np.nan) # print entire matrices
import pandas as pd
from tqdm import *
sys.path.append('../s... |
4,567 |
Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
downloading small dataset of less than 100MB from tensorflow_datasets
| Python Code::
import tensorflow_datasets as tfds
ds, meta = tfds.load('citrus_leaves', with_info=True, split='train', shuffle_files=True)
|
4,568 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
NiftyNet provides a "CRF-RNN" layer for image segmentation, following the idea proposed in
Zheng et al., Conditional Random Fields as Recurrent Neural Networks, ICCV 2015.
Different from man... | Python Code:
import sys
niftynet_path = '/Users/demo/Documents/NiftyNet/'
sys.path.insert(0, niftynet_path)
from niftynet.layer.crf import CRFAsRNNLayer
import nibabel as nib
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
ct_image = nib.load('100_CT.nii').get_data()
logits = nib.load('100__n... |
4,569 | 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 explo... |
4,570 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Advanced Notebook
Step1: BQPlot
Examples here are shamelessly stolen from the amazing
Step2: ipyvolume | Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
from pandas.tools.plotting import scatter_matrix
from sklearn.datasets import load_boston
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_context('poster')
sns.set_style('whitegrid')
plt.rcParams['figure.figsiz... |
4,571 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<div align="right">Python 3.6 Jupyter Notebook</div>
Geotagging WiFi access points
<div class="alert alert-warning">
**This notebook contains advanced exercises that are only applicable to s... | Python Code:
# Load relevant libraries.
from os import path
import pandas as pd
import numpy as np
import folium
import glob
from tqdm import tqdm
import random
%matplotlib inline
# Load custom modules.
import sys
sys.path.append('..')
from utils import getmedian, haversine
from utils import llaToECEF as coords_to_geom... |
4,572 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
SciPy - Library of scientific algorithms for Python
J.R. Johansson (jrjohansson at gmail.com)
The latest version of this IPython notebook lecture is available at http
Step1: Introduction
Th... | Python Code:
# what is this line all about? Answer in lecture 4
%matplotlib inline
import matplotlib.pyplot as plt
from IPython.display import Image
Explanation: SciPy - Library of scientific algorithms for Python
J.R. Johansson (jrjohansson at gmail.com)
The latest version of this IPython notebook lecture is available... |
4,573 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
计算传播与机器学习
王成军
wangchengjun@nju.edu.cn
计算传播网 http
Step1: 训练集和测试集
Step2: 交叉验证
cross-validation
k-fold CV, the training set is split into k smaller sets (other approaches are described below,... | Python Code:
%matplotlib inline
import sklearn
from sklearn import datasets
from sklearn import linear_model
import matplotlib.pyplot as plt
from sklearn.metrics import classification_report
from sklearn.preprocessing import scale
# boston data
boston = datasets.load_boston()
y = boston.target
X = boston.data
' '.join(... |
4,574 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ABU量化系统使用文档
<center>
<img src="./image/abu_logo.png" alt="" style="vertical-align
Step1: 上一节使用AbuFactorBuyBreak和AbuFactorSellBreak且混入基本止盈止损策略AbuFactorAtrNStop，
风险控制止损策略AbuFactorPreA... | Python Code:
from __future__ import print_function
from __future__ import division
import warnings
warnings.filterwarnings('ignore')
warnings.simplefilter('ignore')
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import os
import sys
# 使用insert 0即只使用github，避免交叉使用了pip安装的abupy，导致... |
4,575 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
How to plot topomaps the way EEGLAB does
If you have previous EEGLAB experience you may have noticed that topomaps
(topoplots) generated using MNE-Python look a little different from those
c... | Python Code:
# Authors: Mikołaj Magnuski <mmagnuski@swps.edu.pl>
#
# License: BSD (3-clause)
import numpy as np
from matplotlib import pyplot as plt
import mne
print(__doc__)
Explanation: How to plot topomaps the way EEGLAB does
If you have previous EEGLAB experience you may have noticed that topomaps
(topoplots) gener... |
4,576 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Loading large datasets
Learning Objectives
- Understand difference between loading data entirely in-memory and loading in batches from disk
- Practice loading a .csv file from disk in ba... | Python Code:
import tensorflow as tf
import shutil
print(tf.__version__)
tf.enable_eager_execution()
Explanation: Loading large datasets
Learning Objectives
- Understand difference between loading data entirely in-memory and loading in batches from disk
- Practice loading a .csv file from disk in batches using the ... |
4,577 |
Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
Linear Regression using sklearn
| Python Code::
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)
model = LinearRegression()
model.fit(X_train, y_train)
|
4,578 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Gaussian Mixture Model with ADVI
Here, we describe how to use ADVI for inference of Gaussian mixture model. First, we will show that inference with ADVI does not need to modify the stochasti... | Python Code:
%matplotlib inline
import theano
theano.config.floatX = 'float64'
import pymc3 as pm
from pymc3 import Normal, Metropolis, sample, MvNormal, Dirichlet, \
DensityDist, find_MAP, NUTS, Slice
import theano.tensor as tt
from theano.tensor.nlinalg import det
import numpy as np
import matplotlib.pyplot as pl... |
4,579 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<img src='rc_logo.png' style="height
Step1: Locally and Remote
Run locally
Connect to the cloud (e.g AWS)
Connect to supercomputer (e.g. XSEDE Resource)
Add compute power
Step2: Plot a His... | Python Code:
2+4
print("hello")
print("Hello world!")
Explanation: <img src='rc_logo.png' style="height:75px">
Efficient Data Analysis with the IPython Notebook
<img src='data_overview.png' style="height:500px">
Objectives
Become familiar with the IPython Notebook.
Introduce the IPython landscape.
Getting started with ... |
4,580 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Classifiers
A classifier is a function from an asset and a moment in time to a categorical output such as a string or integer label
Step1: Previously, we saw that the latest attribute produ... | Python Code:
from quantopian.pipeline.data import Fundamentals
# Since the underlying data of Fundamentals.exchange_id
# is of type string, .latest returns a Classifier
exchange = Fundamentals.exchange_id.latest
Explanation: Classifiers
A classifier is a function from an asset and a moment in time to a categorical outp... |
4,581 | 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... |
4,582 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Test with toy model
Step1: We'll define the modifier at two different temperatures, and we'll run each for 10000 snapshots. Note also that our two atoms have different masses.
Step2: Withi... | Python Code:
topology = paths.engines.toy.Topology(n_spatial=3,
n_atoms=2,
masses=np.array([2.0, 8.0]),
pes=None)
initial_snapshot = paths.engines.toy.Snapshot(
coordinates=np.array([[0.0, 0.0, 0.0],... |
4,583 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Compute the power spectral density of raw data
This script shows how to compute the power spectral density (PSD)
of measurements on a raw dataset. It also show the effect of applying SSP
to ... | Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io, read_proj, read_selection
from mne.... |
4,584 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Running Tune experiments with Dragonfly
In this tutorial we introduce Dragonfly, while running a simple Ray Tune experiment.
Tune’s Search Algorithms integrate with Dragonfly and, as a resul... | Python Code:
# !pip install ray[tune]
!pip install dragonfly-opt==0.1.6
Explanation: Running Tune experiments with Dragonfly
In this tutorial we introduce Dragonfly, while running a simple Ray Tune experiment.
Tune’s Search Algorithms integrate with Dragonfly and, as a result,
allow you to seamlessly scale up a Dragonf... |
4,585 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Neural Networks with TensorFlow and Keras
Step1: First Step
Step2: Second Step
Step3: What is the intuition here
Step4: No overfitting, probably as good as it gets
Does this look like yo... | Python Code:
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
%pylab inline
import pandas as pd
print(pd.__version__)
import tensorflow as tf
tf.logging.set_verbosity(tf.logging.ERROR)
print(tf.__version__)
import keras
print(keras.__version__)
Explanation: Neural Networks with TensorFlow and Keras
... |
4,586 | 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... |
4,587 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data Science Tutorial
Now that we've covered some Python basics, we will begin a tutorial going through many tasks a data scientist may perform. We will obtain real world data and go throug... | Python Code:
def download_file(url, local_filename):
import requests
# stream = True allows downloading of large files; prevents loading entire file into memory
r = requests.get(url, stream = True)
with open(local_filename, 'wb') as f:
for chunk in r.iter_content(chunk_size=1024):
... |
4,588 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
First, I'm going to define a function that will print the power (watts per square meter) that the earth would receive from the sun if there were no atmosphere.
Step1: Now I'm going to take ... | Python Code:
def Solar_Power_Calculator(Day_Of_Year,Lattitude,Hour_of_Day):
'''This function will tell you how much power the sun is radiating on one square meter
of the earth when it is sunny in any location in the world at any time.
Inputs: Day_Of_Year, Lattitude, Hour_of_Day
Output: Power (watts)'''
... |
4,589 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Dénes Csala
MCC, 2022
Based on Elements of Data Science (Allen B. Downey, 2021) and Python Data Science Handbook (Jake VanderPlas, 2018)
License
Step1: Motivating Random Forests
Step2: T... | Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
plt.style.use('seaborn')
Explanation: Dénes Csala
MCC, 2022
Based on Elements of Data Science (Allen B. Downey, 2021) and Python Data Science Handbook (Jake VanderPlas, 2018)
License: MIT
Supervised Learning In-... |
4,590 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Les déterminants du choix de contraception en Indonésie
Présentation littéraire
Selon de récentes estimations, l'Indonésie a une population d'environ 255 millions d'habitants. Petit à petit ... | Python Code:
%%javascript
$.getScript('https://kmahelona.github.io/ipython_notebook_goodies/ipython_notebook_toc.js')
Explanation: Les déterminants du choix de contraception en Indonésie
Présentation littéraire
Selon de récentes estimations, l'Indonésie a une population d'environ 255 millions d'habitants. Petit à petit... |
4,591 | 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', 'mohc', 'hadgem3-gc31-ll', 'seaice')
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: MOHC
Source ID: HADGEM3-GC31-LL
Topic: Seaice
Sub-Topics: Dynamics, T... |
4,592 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example for bulk function management
Shows
Step1: Demonstration model
Step2: NOTE
Step3: The result of the function call is very important. It tells us what was created and the names.
The... | Python Code:
import veneer
v = veneer.Veneer()
%matplotlib inline
Explanation: Example for bulk function management
Shows:
Creating multiple modelled variables
Creating multiple functions of the same form, each using one of the newly created modelled variables
Applying multiple functions
End of explanation
v.network().... |
4,593 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Cross Country Production Data
This program extracts particular series from the Penn World Tables (PWT). Data and documentation for the PWT are available at https
Step1: Construct data sets
... | Python Code:
# Set the current value of the PWT data file
current_pwt_file = 'pwt100.xlsx'
# Import data from local source or download if not present
if os.path.exists('../xslx/pwt100.xlsx'):
info = pd.read_excel('../xslx/'+current_pwt_file,sheet_name='Info',header=None)
legend = pd.read_excel('../xslx/'+curren... |
4,594 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Short demo of psydata functions
Step1: I'll demo some functions here using a dataset I simulated earlier.
Step2: Bin bernoulli trials, compute binomial statistics
Compute binomial trials f... | Python Code:
import seaborn as sns
import psyutils as pu
%load_ext autoreload
%autoreload 2
%matplotlib inline
sns.set_style("white")
sns.set_style("ticks")
Explanation: Short demo of psydata functions
End of explanation
# load data:
dat = pu.psydata.load_psy_data()
dat.info()
Explanation: I'll demo some functions here... |
4,595 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Ejemplo práctico de uso de diccionarios y for loops ( y gráficas).
Comportamiento de neuronas.
Utilizando un modelo matemático (modelo de electrodifusión).
El modelo describe la actividad el... | Python Code:
%matplotlib inline
Explanation: Ejemplo práctico de uso de diccionarios y for loops ( y gráficas).
Comportamiento de neuronas.
Utilizando un modelo matemático (modelo de electrodifusión).
El modelo describe la actividad eléctrica de una neurona, al variar los distintos parámetros. <br />
Yo tengo un sistem... |
4,596 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
GA360 Segmentology
GA360 funnel analysis using Census data.
License
Copyright 2020 Google LLC,
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file excep... | Python Code:
!pip install git+https://github.com/google/starthinker
Explanation: GA360 Segmentology
GA360 funnel analysis using Census data.
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 obtain ... |
4,597 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
DKRZ CMIP6 submission form for ESGF data publication
General Information (to be completed based on official CMIP6 references)
Data to be submitted for ESGF data publication must follow the r... | Python Code:
from dkrz_forms import form_widgets
form_widgets.show_status('form-submission')
Explanation: DKRZ CMIP6 submission form for ESGF data publication
General Information (to be completed based on official CMIP6 references)
Data to be submitted for ESGF data publication must follow the rules outlined in the CM... |
4,598 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1. Linear Regression
Step1: First let's add the new features.
Step2:
Step3: 1.1 Training the Model
1.1.1 Correlation Matrix
Step4: Should I have multiplied those features by num_answers... | Python Code:
import pandas as pd
import json
json_data = open('/home/yohna/Documents/quora_challenges/views/sample/input00.in') # Edit this to where you have put the input00.in file
data = []
for line in json_data:
data.append(json.loads(line))
data.remove(9000)
data.remove(1000)
df = pd.DataFrame(data)
df['anonymo... |
4,599 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Classifiers
A classifier is a function from an asset and a moment in time to a categorical output such as a string or integer label
Step1: Previously, we saw that the latest attribute produ... | Python Code:
from quantopian.pipeline.data import morningstar
# Since the underlying data of morningstar.share_class_reference.exchange_id
# is of type string, .latest returns a Classifier
exchange = morningstar.share_class_reference.exchange_id.latest
Explanation: Classifiers
A classifier is a function from an asset a... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.