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11,700 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ISCpy
ISCpy a robust ISC config file parser. It has virtually unlimited
possibilities for depth and quantity of ISC config files. ISC config files include BIND and DHCP config files among a ... | Python Code:
import iscpy
with open('named.conf') as fp:
s = fp.read()
config = iscpy.ParseISCString(s)
type(config)
Explanation: ISCpy
ISCpy a robust ISC config file parser. It has virtually unlimited
possibilities for depth and quantity of ISC config files. ISC config files include BIND and DHCP config files amon... |
11,701 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Advanced Automatic Differentiation in JAX
Authors
Step1: The higher-order derivatives of $f$ are
Step2: Evaluating the above in $x=1$ would give us
Step3: In the multivariable case, highe... | Python Code:
import jax
f = lambda x: x**3 + 2*x**2 - 3*x + 1
dfdx = jax.grad(f)
Explanation: Advanced Automatic Differentiation in JAX
Authors: Vlatimir Mikulik & Matteo Hessel
Computing gradients is a critical part of modern machine learning methods. This section considers a few advanced topics in the areas of automa... |
11,702 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The curse of hunting rare things
What are the chances of intersecting features with a grid of cross-sections?
I'd like to know the probability of intersecting features with a grid of cross-s... | Python Code:
area = 120000.0 # km^2, area covered by transects
population = 120 # Total number of features (guess)
no_lines = 250 # Total number of transects
line_length = 150 # km, mean length of a transect
feature_width = 0.5 # km, width of features
density = population / area
length = no_lines * l... |
11,703 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Simple interactive bacgkround jobs with IPython
We start by loading the backgroundjobs library and defining a few trivial functions to illustrate things with.
Step1: Now, we can create a jo... | Python Code:
from IPython.lib import backgroundjobs as bg
import sys
import time
def sleepfunc(interval=2, *a, **kw):
args = dict(interval=interval,
args=a,
kwargs=kw)
time.sleep(interval)
return args
def diefunc(interval=2, *a, **kw):
time.sleep(interval)
raise Excep... |
11,704 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Presented Below are two plots in subplot configuration. The upper plot is a sum of the cusp crossings. The lower plot is a plot of the cusp latitude and the spacecraft latitude/longitude p... | Python Code:
import tsyganenko as tsyg
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from spacepy import coordinates as coord
import spacepy.time as spt
from spacepy.time import Ticktock
import datetime as dt
from mpl_toolkits.mplot3d import Axes3D
import sys
#adding the year data here so I don... |
11,705 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Content and Objective
Show approximations by using gaussian approximation
Additionally, applying Gram-Schmidt for "orthonormalizing" a set of functions
Step1: definitions
Step2: Define Gra... | Python Code:
# importing
import numpy as np
import scipy.signal
import scipy as sp
import sympy as sym
from sympy.plotting import plot
Explanation: Content and Objective
Show approximations by using gaussian approximation
Additionally, applying Gram-Schmidt for "orthonormalizing" a set of functions
End of explanation
#... |
11,706 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
CS228 Python Tutorial
Adapted by Volodymyr Kuleshov and Isaac Caswell from the CS231n Python tutorial by Justin Johnson (http
Step1: Python versions
There are currently two different suppor... | Python Code:
def quicksort(arr):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) / 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(right)
print quicksort([3,6,8,10,1,2,1])
... |
11,707 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
A method to use the present_load to balance the leg of poppy
Step1: A trick to switch from real time to simulated time using time function (because my VREP is not in real time - about 3 tim... | Python Code:
from poppy.creatures import PoppyHumanoid
poppy = PoppyHumanoid(simulator='vrep')
%pylab inline
#import time
Explanation: A method to use the present_load to balance the leg of poppy
End of explanation
import time as real_time
class time:
def __init__(self,robot):
self.robot=robot
def time(... |
11,708 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Homework
Step1: Task
Replace previous model with equivalent in prettytensor or tf.slim
Try to make you code as compact as possible
Step2: You can play generated sample using any midi playe... | Python Code:
import numpy as np
from music21 import stream, midi, tempo, note
from grammar import unparse_grammar
from preprocess import get_musical_data, get_corpus_data
from qa import prune_grammar, prune_notes, clean_up_notes
from generator import __sample, __generate_grammar, __predict
import tflearn
N_epochs = 128... |
11,709 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Today's Objectives
0. Cloning LectureNotes
1. Opening & Navigating the Jupyter Notebook
2. Data type basics
3. Loading data with pandas
4. Cleaning and Manipulating data with pandas
5. Visua... | Python Code:
# Integer arithematic
1 + 1
# Integer division version floating point division
print (6 // 4, 6/ 4)
Explanation: Today's Objectives
0. Cloning LectureNotes
1. Opening & Navigating the Jupyter Notebook
2. Data type basics
3. Loading data with pandas
4. Cleaning and Manipulating data with pandas
5. Visualizi... |
11,710 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
LAB 3b
Step1: Verify tables exist
Run the following cells to verify that we previously created the dataset and data tables. If not, go back to lab 1b_prepare_data_babyweight to create them.... | Python Code:
%%bash
sudo pip freeze | grep google-cloud-bigquery==1.6.1 || \
sudo pip install google-cloud-bigquery==1.6.1
Explanation: LAB 3b: BigQuery ML Model Linear Feature Engineering/Transform.
Learning Objectives
Create and evaluate linear model with BigQuery's ML.FEATURE_CROSS
Create and evaluate linear model ... |
11,711 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<img src="http
Step1: Tutorial - How to work with the OpenEnergy Platform (OEP)
<br>
<div class="alert alert-block alert-danger">
This is an important information!
</div>
<div class="alert ... | Python Code:
__copyright__ = "Zentrum für nachhaltige Energiesysteme Flensburg"
__license__ = "GNU Affero General Public License Version 3 (AGPL-3.0)"
__url__ = "https://github.com/openego/data_processing/blob/master/LICENSE"
__author__ = "wolfbunke"
Explanation: <img src="http://193.175.187.164/static/OEP_l... |
11,712 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Title
Step1: Load Iris Dataset
Step2: Make Iris Dataset Imbalanced
Step3: Upsampling Minority Class To Match Majority | Python Code:
# Load libraries
import numpy as np
from sklearn.datasets import load_iris
Explanation: Title: Handling Imbalanced Classes With Upsampling
Slug: handling_imbalanced_classes_with_upsampling
Summary: How to handle imbalanced classes with upsampling during machine learning in Python.
Date: 2016-09-06 12:00
C... |
11,713 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Random Forest
In random forests, each tree in the ensemble is built from a sample drawn with replacement (i.e., a bootstrap sample) from the training set. In addition, when splitting a node ... | Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('fivethirtyeight')
df = pd.read_csv("data/historical_loan.csv")
# refine the data
df.years = df.years.fillna(np.mean(df.years))
#Load the preprocessing module
from sklearn import preprocessing
categorica... |
11,714 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Py-EMDE
Python Email Data Entry
The following code can gather data from weather stations reporting to the CHORDS portal, package it up into the proper format for GLOBE Email Data Entry , and... | Python Code:
import requests
import json
r = requests.get('http://3d-kenya.chordsrt.com/instruments/2.geojson?start=2017-03-01T00:00&end=2017-05-01T00:00')
if r.status_code == 200:
d = r.json()['Data']
else:
print("Please verify that the URL for the weather station is correct. You may just have to try again wit... |
11,715 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Experimenting with CV Scores
CVScores displays cross validation scores as a bar chart with the
average of the scores as a horizontal line.
Step2: Classification
Step3: Regression | Python Code:
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import StratifiedKFold
from yellowbrick.model_selection import CVScores
import os
from yellowbrick.download import download_all
## The path to the test data sets
FIXTURES = os.pat... |
11,716 | 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>=2.1,<2.2"
Explanation: Advanced: Alternate Backends
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 explanation
import ph... |
11,717 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial Part 18
Step1: Next we create a network to implement the policy. We begin with two convolutional layers to process
the image. That is followed by a dense (fully connected) layer ... | Python Code:
%tensorflow_version 1.x
!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py
import deepchem_installer
%time deepchem_installer.install(version='2.3.0')
!pip install 'gym[atari]'
import deepchem as dc
import numpy as np
class PongEnv(dc.rl.GymE... |
11,718 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
TUTORIAL 04 - Graetz problem 1
Keywords
Step1: 3. Affine decomposition
In order to obtain an affine decomposition, we proceed as in the previous tutorial and recast the problem on a fixed, ... | Python Code:
from dolfin import *
from rbnics import *
Explanation: TUTORIAL 04 - Graetz problem 1
Keywords: successive constraints method
1. Introduction
This Tutorial addresses geometrical parametrization and the successive constraints method (SCM). In particular, we will solve the Graetz problem, which deals with fo... |
11,719 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="http
Step1: For the sake of visualizing values at nodes on our grid, we'll define a handy little function
Step2: Let's review the numbering of nodes and links. The lines below wil... | Python Code:
from landlab import RasterModelGrid
import numpy as np
mg = RasterModelGrid((3, 4), xy_spacing=100.0)
h = mg.add_zeros('surface_water__depth', at='node')
h[:] = 7 - np.abs(6 - np.arange(12))
Explanation: <a href="http://landlab.github.io"><img style="float: left" src="../../landlab_header.png"></a>
Mapping... |
11,720 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Strings-and-Text" data-toc-modified-id="Strings-and-Text-1"><span class="toc... | Python Code:
# code for loading the format for the notebook
import os
# path : store the current path to convert back to it later
path = os.getcwd()
os.chdir(os.path.join('..', '..', 'notebook_format'))
from formats import load_style
load_style(plot_style=False)
os.chdir(path)
# magic to print version
%load_ext waterma... |
11,721 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This example demonstrates one possible way to cluster data sets that are too large to fit into memory using MDTraj and scipy.cluster. The idea for the algorithim is that we'll cluster every ... | Python Code:
from __future__ import print_function
import random
from collections import defaultdict
import mdtraj as md
import numpy as np
import scipy.cluster.hierarchy
stride = 5
subsampled = md.load('ala2.h5', stride=stride)
print(subsampled)
Explanation: This example demonstrates one possible way to cluster data s... |
11,722 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Diagonalizing a Matrix
$
\mathbf{A} x_1 = \lambda_1 x_1 \
\mathbf{A} x_2 = \lambda_2 x_2 \
\mathbf{A} \times \begin{vmatrix} x_1 & x_2 \end{vmatrix} = \begin{vmatrix} \lambda_1 x_1 & \lambda... | Python Code:
import numpy as np
from scipy.linalg import eig, inv
from diffmaps_util import k, diag, sort_eigens
m = np.array([.8, .2, .5, .5]).reshape(2,2)
m
u0 = np.array([0,1])
for i in range(0,50):
u0 = u0.dot(m)
print u0
w, v = eig(m)
print w.real
print v
v.dot(inv(v).dot(u0))
Explanation: Diagonalizing a Matr... |
11,723 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Regularization
Step1: We have referred to regularization in earlier sections, but we
want to develop this important idea more fully. Regularization is
the mechanism by which we navigate the... | Python Code:
from IPython.display import Image
Image('../../../python_for_probability_statistics_and_machine_learning.jpg')
Explanation: Regularization
End of explanation
import sympy as S
S.var('x:2 l',real=True)
J=S.Matrix([x0,x1]).norm()**2 + l*(1-x0-2*x1)
sol=S.solve(map(J.diff,[x0,x1,l]))
print(sol)
Explanation: ... |
11,724 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Simple Query
This is a simple single-level query.
Default Columns
Step2: The system also supports Pan-STARRS1 and 2MASS cross-matches using the panstarrs1 and twomass keywords
Step3:... | Python Code:
circle =
--Selections: Cluster RA
1=CONTAINS(POINT('ICRS',gaia.ra,gaia.dec),
CIRCLE('ICRS',{ra:.4f},{dec:.4f},{rad:.2f}))
.format(ra=230, dec=0, rad=4)
df = make_simple_query(
WHERE=circle, # The WHERE part of the SQL
random_index=1e4, # a shortcut to use the random_index... |
11,725 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Meetup 1
Going to parse texts for most used words.
Step1: We want to "tokenize" the text and discard "stopwords" like 'a', 'the', 'in'. These words aren't relevant for our analysis.
To toke... | Python Code:
# Lets see how many lines are in the PDF
# We can use the '!' special character to run Linux commands inside of our notebook
!wc -l test.txt
# Now lets see how many words
!wc -w test.txt
import nltk
from nltk import tokenize
# Lets open the file so we can access the ascii contents
# fd stands for file desc... |
11,726 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Ocean
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify d... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cnrm-cerfacs', 'sandbox-1', 'ocean')
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: CNRM-CERFACS
Source ID: SANDBOX-1
Topic: Ocean
Sub-Topics: Timesteppi... |
11,727 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Representational Similarity Analysis
Representational Similarity Analysis is used to perform summary statistics
on supervised classifications where the number of classes is relatively high.
... | Python Code:
# Authors: Jean-Remi King <jeanremi.king@gmail.com>
# Jaakko Leppakangas <jaeilepp@student.jyu.fi>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
from pandas import read_csv
import matplotlib.pyplot as plt... |
11,728 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Working with sensor locations
This tutorial describes how to read and plot sensor locations, and how
the physical location of sensors is handled in MNE-Python.
Step1: About montages and... | Python Code:
import os
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # noqa
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')
... |
11,729 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
syncID
Step1: Next, we read in the example data. Note that you will need to update the filepaths below to work on your machine.
Step2: Now we can plot the data.
Step3: Save this project w... | Python Code:
import numpy as np
import matplotlib
import matplotlib.pyplot as mplt
from scipy import linalg
from scipy import io
### Ordinary Least Squares
### SOLVES 2-CLASS LEAST SQUARES PROBLEM
### LOAD DATA ###
### IF LoadClasses IS True, THEN LOAD DATA FROM FILES ###
### OTHERSIE, RANDOMLY GENERATE DATA ###
LoadCl... |
11,730 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
An RNN for short-term predictions
This model will try to predict the next value in a short sequence based on historical data. This can be used for example to forecast demand based on a coupl... | Python Code:
import numpy as np
import utils_datagen
import utils_display
from matplotlib import pyplot as plt
import tensorflow as tf
print("Tensorflow version: " + tf.__version__)
Explanation: An RNN for short-term predictions
This model will try to predict the next value in a short sequence based on historical data.... |
11,731 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Adding a Data Set to pods
Open Data Science Initiative
28th May 2014 Neil D. Lawrence
Adding a data set to GPy should be done in two stages. Firstly, you need to edit the data_resources.json... | Python Code:
def boston_housing(data_set='boston_housing'):
if not data_available(data_set):
download_data(data_set)
all_data = np.genfromtxt(os.path.join(data_path, data_set, 'housing.data'))
X = all_data[:, 0:13]
Y = all_data[:, 13:14]
return data_details_return({'X' : X, 'Y': Y}, data_set... |
11,732 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Using the nlputils library
In this iPython Notebook are some examples of how various parts of the nlputils library can be used with text and other datasets. General knowledge of common machi... | Python Code:
from __future__ import unicode_literals, division, print_function, absolute_import
from builtins import str, range
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import offsetbox
from scipy.spatial.distance import pdist, squareform
from sklearn.datasets import fetch_20newsgroups, load_d... |
11,733 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Marvin Maps
Marvin Maps is how you deal with the DAP MAPS FITS files easily. You can retrieve maps in several ways. Let's take a took.
From a Marvin Maps
Marvin Maps takes the same inputs... | Python Code:
# import the maps
from marvin.tools.maps import Maps
# Load a MPL-5 map
mapfile = '/Users/Brian/Work/Manga/analysis/v2_0_1/2.0.2/SPX-GAU-MILESHC/8485/1901/manga-8485-1901-MAPS-SPX-GAU-MILESHC.fits.gz'
# Let's get a default map of
maps = Maps(filename=mapfile)
print(maps)
Explanation: Marvin Maps
Marvin Map... |
11,734 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
🏭 Coal Plant ON/OFF
Step1: 🛎️ [DON’T PANIC] It’s safe to ignore the warnings.
When we pip install the requirements, there might be some warnings about conflicting dependency versions.
For... | Python Code:
# Get the sample source code.
!git clone https://github.com/GoogleCloudPlatform/python-docs-samples.git ~/python-docs-samples
%cd ~/python-docs-samples/people-and-planet-ai/geospatial-classification
!pip install -r requirements.txt -c constraints.txt
Explanation: 🏭 Coal Plant ON/OFF: Predictions
Time esti... |
11,735 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Getting Started with Images
cv2.imread, cv2.imshow, cv2.imwrite
Reading an image - cv2.imread()
cv2.imread() has two arguments, one address of the image
and the other as following arguments
... | Python Code:
import numpy as np
import cv2
ls
file_adr = 'Me1.png'
img = cv2.imread(file_adr,cv2.IMREAD_GRAYSCALE) # Alternate- 0 cv2.IMREAD_GRAYSCALE - 0
cv2.imwrite('Me1_gray.jpg', img) #
img2 = cv2.imread('Me1_gray.jpg', cv2.IMREAD_COLOR)
Explanation: Getting Started with Images
cv2.imread, cv2.imshow, cv2.imwrite... |
11,736 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
Given a pandas DataFrame, how does one convert several binary columns (where 1 denotes the value exists, 0 denotes it doesn't) into a single categorical column? | Problem:
import pandas as pd
df = pd.DataFrame({'A': [1, 0, 0, 0, 1, 0],
'B': [0, 1, 0, 0, 0, 1],
'C': [0, 0, 1, 0, 0, 0],
'D': [0, 0, 0, 1, 0, 0]})
df["category"] = df.idxmax(axis=1) |
11,737 | 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', 'pcmdi', 'pcmdi-test-1-0', 'seaice')
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: PCMDI
Source ID: PCMDI-TEST-1-0
Topic: Seaice
Sub-Topics: Dynamics, T... |
11,738 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Общая информация
Срок сдачи
Step1: IRIS
Step2: MNIST
Step3: Задание 5 | Python Code:
import numpy as np
import matplotlib.pyplot as plt
from line_profiler import LineProfiler
from sklearn.metrics.pairwise import pairwise_distances
import seaborn as sns
from sklearn import datasets
from sklearn.base import ClassifierMixin
from sklearn.datasets import fetch_mldata
from sklearn.neighbors.base... |
11,739 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
A transformada discreta de Fourier (DFT)
Caso unidimensional
Transformada Discreta de Fourier em uma dimensão
Step1: Para exemplificar o caso unidimensional, vamos pegar uma imagem bidimens... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
from numpy.fft import *
import sys,os
ia898path = os.path.abspath('../../')
if ia898path not in sys.path:
sys.path.append(ia898path)
import ia898.src as ia
Explanation: A transformada discreta de Four... |
11,740 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
2A.ml - Pipeline pour un réduction d'une forêt aléatoire - énoncé
Le modèle Lasso permet de sélectionner des variables, une forêt aléatoire produit une prédiction comme étant la moyenne d'ar... | Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
%matplotlib inline
Explanation: 2A.ml - Pipeline pour un réduction d'une forêt aléatoire - énoncé
Le modèle Lasso permet de sélectionner des variables, une forêt aléatoire produit une prédiction comme étant la moyenne d'arbres de régression. C... |
11,741 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Welcome to Pynq Audio
This notebook shows the basic recording and playback features of the Pynq-Z1.
It uses the audio jack to play back recordings from the built-in microphone, as well as a ... | Python Code:
from pynq import Overlay
from pynq.drivers import Audio
Overlay('base.bit').download()
pAudio = Audio()
Explanation: Welcome to Pynq Audio
This notebook shows the basic recording and playback features of the Pynq-Z1.
It uses the audio jack to play back recordings from the built-in microphone, as well as a ... |
11,742 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
I am using python and scikit-learn to find cosine similarity between item descriptions. | Problem:
import numpy as np
import pandas as pd
import sklearn
from sklearn.feature_extraction.text import TfidfVectorizer
df = load_data()
tfidf = TfidfVectorizer()
from sklearn.metrics.pairwise import cosine_similarity
response = tfidf.fit_transform(df['description']).toarray()
tf_idf = response
cosine_similarity_mat... |
11,743 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction to data munging with Jupyter and pandas
PyGotham 2015
Step1: The case for open source data tools
Reproducibility and Transparency
Cost -- compare capabilities between software ... | Python Code:
from __future__ import division
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import rpy2
from IPython.display import display, Image, YouTubeVideo
%matplotlib inline
Explanation: Introduction to data munging with Jupyter and pandas
PyG... |
11,744 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This is the <a href="https
Step1: How do we define direction of an earth magnetic field?
Earth magnetic field is a vector. To define a vector we need to choose a coordinate system. We use r... | Python Code:
import numpy as np
from geoscilabs.mag import Mag, Simulator
from SimPEG.potential_fields import magnetics as mag
from SimPEG import utils, data
from discretize import TensorMesh
Explanation: This is the <a href="https://jupyter.org/">Jupyter Notebook</a>, an interactive coding and computation environment.... |
11,745 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1>Astro 283 Homework 5</h1>
Bijan Pourhamzeh
Step1: <h3>Random Sampling</h3>
Here we sample from a distribution given by the equation
$$
p(x\mid \alpha,\beta) = \left{
\begin{array}{ll}
\... | Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy.special import iv
import scipy.stats
from csv import reader
from __future__ import print_function
Explanation: <h1>Astro 283 Homework 5</h1>
Bijan Pourhamzeh
End of explanation
#Choose parameters and plot to see what it looks ... |
11,746 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Definition of the problem
We need to develop a model that can classify breast cells as bningn (non harmful) or malignant (cancerous).
The list of attribues are
Step1: Clean data
missing dat... | Python Code:
# 1 Read dataset
cols = [
'clump thickness',
'uniformity of cell size',
'uniformity of cell shape',
'marginal adhesion',
'single epithelial cell size',
'bare nuclei',
'bland chromatin',
'normal nucleoli',
'mitoses',
'class']
df = pd.read... |
11,747 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
How to handle models from Python?
In this tutorial you will learn how to handle models from Python. This is done using the GammaLib classes GModels, GModel, GModelSpatial, GModelSpectral, an... | Python Code:
import gammalib
Explanation: How to handle models from Python?
In this tutorial you will learn how to handle models from Python. This is done using the GammaLib classes GModels, GModel, GModelSpatial, GModelSpectral, and GModelTemporal. You can find all the information on the GammaLib classes in the Doxyge... |
11,748 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
When I first ran this, my dataframes weren't "aligned".
So it's very important to check your datasets after every load.
The correspondence between dates and topics and numerical features is ... | Python Code:
print(len(dates))
print(len(topics))
print(len(nums))
print(sum(nums.favorite_count >= 1))
sum(nums.index == dates.index) == len(dates)
sum(nums.index == topics.index) == len(dates)
sgd = SGDRegressor()
sgd
sgd = SGDRegressor().fit(topics.values, nums.favorite_count)
Explanation: When I first ran this, my ... |
11,749 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: What's this TensorFlow business?
You've written a lot of code in this assignment to provide a whole host of neural network functionality. Dropout, Batch Norm, and 2D convolutions are ... | Python Code:
import tensorflow as tf
import numpy as np
import math
import timeit
import matplotlib.pyplot as plt
%matplotlib inline
from cs231n.data_utils import load_CIFAR10
def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=10000):
Load the CIFAR-10 dataset from disk and perform preproce... |
11,750 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Boston Housing Prediction
Author
Step1: Loading the boston dataset - Train and Test
Step2: Understanding the distribution and relationship of the data
Describing the data to understand the... | Python Code:
import pandas as pd
import numpy as np
Explanation: Boston Housing Prediction
Author: Rishu Shrivastava, Babu Sivaprakasam
Link: https://www.kaggle.com/c/boston-housing
Last Update: 02 Apr 2018
Importing libraries
End of explanation
data_path = "C:/Users/Rishu/Desktop/dATA/boston/"
boston_data=pd.read_csv(... |
11,751 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
You are currently looking at version 1.0 of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the Jupyter Notebook ... | Python Code:
import pandas as pd
df = pd.read_csv('olympics.csv', index_col=0, skiprows=1)
for col in df.columns:
if col[:2]=='01':
df.rename(columns={col:'Gold'+col[4:]}, inplace=True)
if col[:2]=='02':
df.rename(columns={col:'Silver'+col[4:]}, inplace=True)
if col[:2]=='03':
df.ren... |
11,752 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Evolutionary game theory - solutions
Assume the frequency dependent selection model for a population with two types of individuals
Step1: B. $f_1(x)=x_1x_2 - x_2\qquad f_2(x)=x_2 - x_1 + 1/... | Python Code:
import sympy as sym
x_1 = sym.symbols("x_1")
sym.solveset(3 * x_1 - 2 * (1 - x_1), x_1)
Explanation: Evolutionary game theory - solutions
Assume the frequency dependent selection model for a population with two types of individuals: $x=(x_1, x_2)$ such that $x_1 + x_2 = 1$. Obtain all the stable distributi... |
11,753 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
My installtion instructions
Step1: Import Policy, RL agent, ...
Step3: Define a Callback Function
Step5: Create and wrap the environment
Step6: Define and train the PPO agent
Step9: Plo... | Python Code:
import stable_baselines
stable_baselines.__version__
Explanation: My installtion instructions: https://gitlab.com/-/snippets/2057703
Source: https://colab.research.google.com/github/Stable-Baselines-Team/rl-colab-notebooks/blob/master/monitor_training.ipynb
See also: https://stable-baselines.readthedocs.io... |
11,754 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Numpy Arrays and Vectorization
Frequently, matrices and vectors are needed for computation and are a convenient way to store and access data. Vectors are more commonly many rows with a singl... | Python Code:
# Python imports
import numpy as np
Explanation: Numpy Arrays and Vectorization
Frequently, matrices and vectors are needed for computation and are a convenient way to store and access data. Vectors are more commonly many rows with a single column. A significant amount of work has been done to make compute... |
11,755 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Modeling Protein-Ligand Interactions with Atomic Convolutions
By Nathan C. Frey | Twitter and Bharath Ramsundar | Twitter
This DeepChem tutorial introduces the Atomic Convolutional Neural Ne... | Python Code:
!pip install -q condacolab
import condacolab
condacolab.install()
!/usr/local/bin/conda info -e
!/usr/local/bin/conda install -c conda-forge pycosat mdtraj pdbfixer openmm -y -q # needed for AtomicConvs
!pip install --pre deepchem
import deepchem
deepchem.__version__
import deepchem as dc
import os
import... |
11,756 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Convolutions and sliding windows
Plots inline
Step1: Imports
Step2: Some utility functions for making an image montage for display and padding images
Step3: Load a photo of some fruit
Ste... | Python Code:
%matplotlib inline
Explanation: Convolutions and sliding windows
Plots inline:
End of explanation
import os
import numpy as np
from matplotlib import pyplot as plt
from scipy.ndimage import convolve
from skimage.filters import gabor_kernel
from skimage.color import rgb2grey
from skimage.util.montage import... |
11,757 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Creating a chord progression with a genetic algorithm
This work is the result of an experiment done some months ago. I used a simple genetic algorithm to find a solution to a classic exercis... | Python Code:
from IPython import display
display.Image('img/simple.jpg', width=400)
Explanation: Creating a chord progression with a genetic algorithm
This work is the result of an experiment done some months ago. I used a simple genetic algorithm to find a solution to a classic exercise of harmony: given a certain voi... |
11,758 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
First, I made a mistake naming the data set! It's 2015 data, not 2014 data. But yes, still use 311-2014.csv. You can rename it.
Importing and preparing your data
Import your data, but only t... | Python Code:
#df = pd.read_csv("small-311-2015.csv")
df = pd.read_csv("311-2014.csv", nrows=200000)
df.head(2)
df.info()
def parse_date (str_date):
return dateutil.parser.parse(str_date)
df['created_dt']= df['Created Date'].apply(parse_date)
df.head(3)
df.info()
Explanation: First, I made a mistake naming the data ... |
11,759 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Configuring MNE python
This tutorial gives a short introduction to MNE configurations.
Step1: MNE-python stores configurations to a folder called .mne in the user's
home directory, or to Ap... | Python Code:
import os.path as op
import mne
from mne.datasets.sample import data_path
fname = op.join(data_path(), 'MEG', 'sample', 'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(fname).crop(0, 10)
original_level = mne.get_config('MNE_LOGGING_LEVEL', 'INFO')
Explanation: Configuring MNE python
This tutorial gives ... |
11,760 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction to Probabilitic Graphical Models
Step1: Contents
What is machine learning
Different ways of learning from data
Why probabilistic graphical models
Major types of PGMs
1. What is... | Python Code:
from IPython.display import Image
Explanation: Introduction to Probabilitic Graphical Models
End of explanation
%run ../scripts/1/discretize.py
data
Explanation: Contents
What is machine learning
Different ways of learning from data
Why probabilistic graphical models
Major types of PGMs
1. What is machine ... |
11,761 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Model Training
Code for finding the best predictive model
Author
Step1: The default directory is the code subdirectory. Changing to the main repo directory above.
Upload Data
Step2: Rando... | Python Code:
import os
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import json
from IPython.display import Image
from IPython.core.display import HTML
Explanation: Model Training
Code for finding the best predictive model
Author: Jimmy Charité
Email: jimmy.charite@gmail... |
11,762 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: Let's download and import our primary Canadian Immigration dataset using pandas read_excel() method. Normally, before we can do that, we would need to download a modul... | Python Code:
import numpy as np # useful for many scientific computing in Python
import pandas as pd # primary data structure library
from PIL import Image # converting images into arrays
Explanation: <a href="https://cognitiveclass.ai"><img src = "https://ibm.box.com/shared/static/9gegpsmnsoo25ikkbl4qzlvlyjbgxs5x.png... |
11,763 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Artifact correction with Maxwell filter
This tutorial shows how to clean MEG data with Maxwell filtering.
Maxwell filtering in MNE can be used to suppress sources of external
intereference a... | Python Code:
import mne
from mne.preprocessing import maxwell_filter
data_path = mne.datasets.sample.data_path()
Explanation: Artifact correction with Maxwell filter
This tutorial shows how to clean MEG data with Maxwell filtering.
Maxwell filtering in MNE can be used to suppress sources of external
intereference and c... |
11,764 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
initialize the Cosmological models
Step1: Define proxy modelling
Use a mass proxy, define the probability for observing a proxy given a mass and redhsift
$$
P(\log\lambda|M,z) = N(\mu(M,z),... | Python Code:
#CCL cosmology
cosmo_ccl = ccl.Cosmology(Omega_c = 0.30711 - 0.048254, Omega_b = 0.048254, h = 0.677, sigma8 = 0.8822714165197718, n_s=0.96, Omega_k = 0, transfer_function='eisenstein_hu')
#ccl_cosmo_set_high_prec (cosmo_ccl)
cosmo_numcosmo, dist, ps_lin, ps_nln, hmfunc = create_nc_obj (cosmo_ccl)
psf = hm... |
11,765 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Limpieza de datos sobre edificios con certificacion LEED
1. Introduccion
EL United States Green Building Council (USGBG) tiene una base de datos de edificios que cuentan con certificación LE... | Python Code:
# Librerias utilizadas
import pandas as pd
import sys
import os
import csv
from lxml import html
import requests
import time
# Configuracion del sistema
print('Python {} on {}'.format(sys.version, sys.platform))
print('Pandas version: {}'.format(pd.__version__))
import platform; print('Running on {} {}'.fo... |
11,766 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Ronica Reddick
&
Nick Pulito
in association with
"Those Data Bootcamp Guys" -- Professors Backus and Coleman
present
"3 Guys Named Chris"
Scene 1
Step1: Scene 2
Step2: Chris Hemsworth
“The... | Python Code:
#This guided coding excercise requires associated .csv files: CE1.csv, CH1.csv, CP1.csv, Arnold1.csv, Bruce1.csv, and Tom1.csv
#make sure you have these supplemental materials ready to go in your active directory before proceeding
#Let's start coding! We first need to make sure our preliminary packages are... |
11,767 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
property
Python has a great concept called property, which makes the life of an object oriented programmer much simpler. Before defining and going into details of what a property in Python i... | Python Code:
CONST = 10 # some constant
class Weather_balloon():
temp = 222
def convert_temp_to_f(self):
return self.temp * CONST
w = Weather_balloon()
w.temp = 122
print(w.convert_temp_to_f())
class Circle():
area = None
radius = None
def __init__(self, radius):
self.radiu... |
11,768 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
MLE 모수 추정의 예
베르누이 분포의 모수 추정
이 과정을 스스로 쓸 줄 알아야 돼
각각의 시도 $x_i$에 대한 확률은 베르누이 분포
$$ P(x | \theta ) = \text{Bern}(x | \theta ) = \theta^x (1 - \theta)^{1-x}$$
샘플이 $N$개 있는 경우, Likelihood
$$ L = P(... | Python Code:
theta0 = 0.6
x = sp.stats.bernoulli(theta0).rvs(1000)
N0, N1 = np.bincount(x, minlength=2)
N = N0 + N1
theta = N1 / N
theta
Explanation: MLE 모수 추정의 예
베르누이 분포의 모수 추정
이 과정을 스스로 쓸 줄 알아야 돼
각각의 시도 $x_i$에 대한 확률은 베르누이 분포
$$ P(x | \theta ) = \text{Bern}(x | \theta ) = \theta^x (1 - \theta)^{1-x}$$
샘플이 $N$개 있는 경우, ... |
11,769 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
# Getting Started with gensim
This section introduces the basic concepts and terms needed to understand and use gensim and provides a simple usage example.
Core Concepts and Simple Example
A... | Python Code:
raw_corpus = ["Human machine interface for lab abc computer applications",
"A survey of user opinion of computer system response time",
"The EPS user interface management system",
"System and human system engineering testing of EPS",
"Relati... |
11,770 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Krisk also introduced a very simplistic for you to resync data or create a reproducible charts. Consider this plot,
Step1: Executing this code below
Step2: Would let you to modify the plot... | Python Code:
p = kk.bar(df[df.year == 1952],'continent',y='pop', how='mean')
p.set_size(width=800)
Explanation: Krisk also introduced a very simplistic for you to resync data or create a reproducible charts. Consider this plot,
End of explanation
p.resync_data(df[df.year == 2007])
Explanation: Executing this code below... |
11,771 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Excercises Electric Machinery Fundamentals
Chapter 5
Problem 5-2
Step1: Description
Assume that the motor of Problem 5-1 is operating at rated conditions.
Step2: (a)
What are the magnitude... | Python Code:
%pylab notebook
%precision %.4g
import cmath
Explanation: Excercises Electric Machinery Fundamentals
Chapter 5
Problem 5-2
End of explanation
Vt = 480 # [V]
PF = 0.8
fse = 60 # [Hz]
p = 8.0
Pout = 400 * 746 # [W]
Xs = 0.6 # [Ohm]
Explanation: Description
Assume that the mot... |
11,772 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Dimensionality Reduction
The sheer size of data in the modern age is not only a challenge for computer hardware but also a main bottleneck for the performance of many machine learning algori... | Python Code:
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn
from sklearn import datasets
from sklearn.decomposition import PCA
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_int... |
11,773 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introdução ao Numpy
master
Step1: Observe que mesmo no retorno de uma função, a cópia explícita pode não acontecer. Veja o exemplo a
seguir de uma função que apenas retorna a variável de en... | Python Code:
import numpy as np
a = np.arange(6)
b = a
print "a =\n",a
print "b =\n",b
b.shape = (2,3) # mudança no shape de b,
print "\na shape =",a.shape # altera o shape de a
b[0,0] = -1 # mudança no conteúdo de b
print "a =\n",a ... |
11,774 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Nursery School Dataset
Team Members
Sharan
Srikanth
Kalyan
Nursery Database was derived from a hierarchical decision model originally developed to rank applications for nursery schools. It w... | Python Code:
# Importing the libraries which we need now.
import pandas
from pandas.plotting import scatter_matrix
import matplotlib.pyplot as plt
%matplotlib inline
# Dataset from - https://archive.ics.uci.edu/ml/datasets/Nursery
df = pandas.read_table('nursery.txt', sep=',', header=None, names=['parents', 'has_nurs',... |
11,775 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Connecting Job Titles by Their Similarity Scores
Step1: Data loading
Step2: After fixing some bugs with parsing job titles, we re-parsed job titles with problem by title_parse.py script. T... | Python Code:
import my_util as my_util; from my_util import *
import cluster_skill_helpers as cluster_skill_helpers
from cluster_skill_helpers import *
import os
import random
from time import time
import gc
# Turn on auto garbage collection
gc.enable()
HOME_DIR = 'd:/larc_projects/job_analytics/'
DATA_DIR = HOME_DIR +... |
11,776 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Будем совсем неразумно обучаться на всем train'е, так как тогда мы переобучимся,
то есть наш алгоритм "подгониться" под закономерности, присущие только train'у,
а на реальных данных будет не... | Python Code:
# Выделяем outdoor'ы и indoor'ы.
sample_out = sample[result[:, 0] == 1]
sample_in = sample[result[:, 1] == 1]
result_out = result[result[:, 0] == 1]
result_in = result[result[:, 1] == 1]
# Считаем размер indoor- и outdoor-частей в train'е.
train_size_in = int(sample_in.shape[0] * 0.75)
train_size_out = int... |
11,777 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Advanced Exo from 'Intro to Pandas'
Step1: we decide to only take the 'confirmed' cases and not the suspected or probable ones since 'suspected' and 'probable' are very subjective terms and... | Python Code:
# load all data and parse the 'date' column
def load_data():
sl_files=glob.glob('Data/ebola/sl_data/*.csv')
guinea_files=glob.glob('Data/ebola/guinea_data/*.csv')
liberia_files=glob.glob('Data/ebola/liberia_data/*.csv')
sl = pd.concat((pd.read_csv(file, parse_dates=['date']) for file in sl... |
11,778 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Please find torch implementation of this notebook here
Step1: Get data
We use a binarized version of MNIST.
Step2: Training
Step3: We use add-one smoothing for class conditional Bernoulli... | Python Code:
import numpy as np
try:
import torchvision
except ModuleNotFoundError:
%pip install -qq torchvision
import torchvision
import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
!mkdir figures # for saving plots
key = jax.random.PRNGKey(1)
# helper function to show images
def show_image... |
11,779 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<p>
<img src="http
Step1:
Step2: know the difference
Let ${y_{n}}{n\in\mathbb{N}}$ be a sequence, where $y{n}=f(n)$ for some function $f$. Assume that each coefficient $y_{n}$ is not know... | Python Code:
from sympy import *
from sympy.abc import n, i, N, x, k, y
init_printing()
%run src/commons.py
Explanation: <p>
<img src="http://www.cerm.unifi.it/chianti/images/logo%20unifi_positivo.jpg"
alt="UniFI logo" style="float: left; width: 20%; height: 20%;">
<div align="right">
Massimo Nocentini<br>
<sm... |
11,780 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Cloud Computing
Basics
What is Cloud Computing?
On-demand services, delivered over the network.
Relevant Services
Step1: Setting up a Cloud Service
Step2: Create Storage Account
Step3: Wo... | Python Code:
# standard library
import os
import time
import shutil
# Load Python SDK
from azure import *
from azure.servicemanagement import *
from azure.storage import *
# Subscription details
subscription_id = '1a61650c-ada5-4173-a8da-2a4ffcfab747'
certificate_path = 'mycert.pem'
# Initialize connection
sms = Servi... |
11,781 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
PyGeM
Tutorial 1
Step1: We need to read a parameters file. If does not exist the FFDParameters() class creates a default prm file that you have to edit for your problem specifications.
Step... | Python Code:
%matplotlib inline
import pygem as pg
Explanation: PyGeM
Tutorial 1: Free Form Deformation on a sphere in stl file format
In this tutorial we will show the typical workflow. In particular we are going to parse the parameters file for the FFD, read an stl file of a sphere, perform the FFD and write the resu... |
11,782 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<br><p style="font-family
Step1: This is a really large dataset, at least in terms of the number of rows. But with 6 columns, what does this hold?
Step2: Looks like it has different indic... | Python Code:
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
data = pd.read_csv(r'C:\Users\hrao\Documents\Personal\HK\Python\world-development-indicators\Indicators.csv')
data.shape
Explanation: <br><p style="font-family: Arial; font-size:3.75em;color:purple; font-style:bold">
Matpl... |
11,783 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
I'm trying to create a 2-dimensional array in Scipy/Numpy where each value represents the Manhattan distance from the center. It's supposed to have the same shape as the first two d... | Problem:
import numpy as np
from scipy.spatial import distance
shape = (6, 6)
xs, ys = np.indices(shape)
xs = xs.reshape(shape[0] * shape[1], 1)
ys = ys.reshape(shape[0] * shape[1], 1)
X = np.hstack((xs, ys))
mid_x, mid_y = (shape[0]-1)/2.0, (shape[1]-1)/2.0
result = distance.cdist(X, np.atleast_2d([mid_x, mid_y]), 'mi... |
11,784 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
CORDEX ESGF submission form
General Information
Data to be submitted for ESGF data publication must follow the rules outlined in the Cordex Archive Design Document <br /> (https
Step1: S... | Python Code:
from dkrz_forms import form_widgets
form_widgets.show_status('form-submission')
Explanation: CORDEX ESGF submission form
General Information
Data to be submitted for ESGF data publication must follow the rules outlined in the Cordex Archive Design Document <br /> (https://verc.enes.org/data/projects/doc... |
11,785 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pandas Visualization
Step1: DataFrame.plot
Step2: We can select which plot we want to use by passing it into the 'kind' parameter.
Step3: You can also choose the plot kind by using the Da... | Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib notebook
# see the pre-defined styles provided.
plt.style.available
# use the 'seaborn-colorblind' style
plt.style.use('seaborn-colorblind')
Explanation: Pandas Visualization
End of explanation
np.random.seed(123)
df = pd.Da... |
11,786 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Creating Customer Segments
In this project you, will analyze a dataset containing annual spending amounts for internal structure, to understand the variation in the different types of custom... | Python Code:
import warnings
warnings.filterwarnings('ignore')
# Import libraries: NumPy, pandas, matplotlib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# Tell iPython to include plots inline in the notebook
%matplotlib inline
# Read dataset
data = pd.read_csv("wholesale-customers.csv")
print... |
11,787 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1> Preprocessing using Cloud Dataflow </h1>
<h2>Learning Objectives</h2>
<ol>
<li>Create ML dataset using <a href="https
Step1: After installing Apache Beam, restart your kernel by se... | Python Code:
%pip install apache-beam[gcp]==2.13.0
Explanation: <h1> Preprocessing using Cloud Dataflow </h1>
<h2>Learning Objectives</h2>
<ol>
<li>Create ML dataset using <a href="https://cloud.google.com/dataflow/">Cloud Dataflow</a></li>
<li>Simulate a dataset where no ultrasound is performed (i.e. male or femal... |
11,788 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Load the spectroscopic data
Step1: Hmm, the specobjid's are floats, but they should be integers to avoid possible rounding errors during comparison. Lets fix that
Step2: Find matches
Firs... | Python Code:
spec_data_raw = table.Table.read('SAGADropbox/data/saga_spectra_raw.fits.gz')
spec_data_raw
Explanation: Load the spectroscopic data
End of explanation
# Just setting the dtype does *not* do the conversion of the values. It instead tells numpy to
# re-interpret the same set of bits as thought they were i... |
11,789 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Setup
Step1: Periodic Yield
The thread periodic_yeild is woken up at 30ms intervals where it calls sched_yield and relinquishes its time-slice.
The expectation is that the task will have a ... | Python Code:
from trappy.stats.Topology import Topology
from bart.sched.SchedMultiAssert import SchedMultiAssert
from bart.sched.SchedAssert import SchedAssert
import trappy
import os
import operator
import json
#Define a CPU Topology (for multi-cluster systems)
BIG = [1, 2]
LITTLE = [0, 3, 4, 5]
CLUSTERS = [BIG, LITTL... |
11,790 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a id="top"></a>
UN SDG Indicator 6.6.1
Step1: <a id="plat_prod"></a>Choose Platforms and Products ▴
List available products for each platform
Step2: Choose products
Step3: <a id="e... | Python Code:
# Supress Warning
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
import warnings
import matplotlib.pyplot as plt
# Allow importing of our utilities.
import sys
import os
sys.path.append(os.environ.get('NOTEBOOK_ROOT'))
# Import the datacube and the API
import datacube
from utils.data... |
11,791 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Explicit 2D Benchmarks
This file demonstrates how to generate, plot, and output data for 1d benchmarks
Choose from
Step1: Generate the data with noise
Step2: Plot inline and save image
Ste... | Python Code:
from pypge.benchmarks import explicit
import numpy as np
# visualization libraries
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import gridspec
# plot the visuals in ipython
%matplotlib inline
Explanation: Explicit 2D Benchmarks
This file demonstrates how to gener... |
11,792 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step4: Improving the Search Index
(Inspired by and borrowed heavily from
Step5: Stemming
As we could see from the results of the last assignment, our simple index doesn't handle punctuation... | Python Code:
import pickle, bz2, re
from collections import namedtuple, defaultdict, Counter
from IPython.display import display, HTML
from math import log10
Summaries_file = 'data/air__Summaries.pkl.bz2'
Abstracts_file = 'data/air__Abstracts.pkl.bz2'
Summaries = pickle.load( bz2.BZ2File( Summaries_file, 'rb' ) )
Abstr... |
11,793 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Constraint Satisfaction Problems (CSPs)
This IPy notebook acts as supporting material for topics covered in Chapter 6 Constraint Satisfaction Problems of the book Artificial Intelligence
Ste... | Python Code:
from csp import *
Explanation: Constraint Satisfaction Problems (CSPs)
This IPy notebook acts as supporting material for topics covered in Chapter 6 Constraint Satisfaction Problems of the book Artificial Intelligence: A Modern Approach. We make use of the implementations in csp.py module. Even though this... |
11,794 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Template Scan Analysis
This notebook is the 2nd part of the template scan analysis. Its input are the output files of the template_scan_analysis.sh shell script, which is simply a little scr... | Python Code:
ls template_scan_dac_*.h5
Explanation: Template Scan Analysis
This notebook is the 2nd part of the template scan analysis. Its input are the output files of the template_scan_analysis.sh shell script, which is simply a little script calling the digicam-template command on a defined set of raw-data files fo... |
11,795 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Exports nodes and edges from tweets (Retweets, Mentions, or Replies) [CSV]
Exports nodes and edges from tweets (either from retweets or mentions) in json format that can be exported from SFM... | Python Code:
import sys
import json
import re
import numpy as np
from datetime import datetime
import pandas as pd
tweetfile = '/home/soominpark/sfmproject/Work/Network Graphs/food_security.csv'
tweets = pd.read_csv(tweetfile)
Explanation: Exports nodes and edges from tweets (Retweets, Mentions, or Replies) [CSV]
Exp... |
11,796 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Natural Language Processing in a Kaggle Competition
Step1: Now set up our function. This will clean all of the reviews for us.
Step2: Great! Now it is time to go ahead and load our data in... | Python Code:
import re
from bs4 import BeautifulSoup
Explanation: Natural Language Processing in a Kaggle Competition: Movie Reviews
<img src='Movie_thtr.jpg', width = 800, height = 600>
Source
I decided to try playing around with a Kaggle competition. In this case, I entered the "When bag of words meets bags of popco... |
11,797 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Why Objects?
Provide modularity and reuse through hierarchical structures
Object oriented programming is a different way of thinking.
Programming With Objects
Step1: Initial concepts
An obj... | Python Code:
from IPython.display import Image
Image(filename='Classes_vs_Objects.png')
Explanation: Why Objects?
Provide modularity and reuse through hierarchical structures
Object oriented programming is a different way of thinking.
Programming With Objects
End of explanation
# Definiting a Car class
class Car(objec... |
11,798 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Scikit-Learn 朴素贝叶斯分类
朴素贝叶斯分类是机器学习中常见的分类方法之一，本节将带大家深入学习一下 Scikit-Learn 中朴素贝叶斯分类工具包的相关使用方法。
首先，我们导入相关的库，并构建第一个示例数据集：
Step1: Scikit-Learn 中的 make_blobs 方法常被用来生成聚类算法的测试数据，直观地说，make_blobs 会根据用户指... | Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
from sklearn.datasets import make_blobs
X, y = make_blobs(100, 2, centers=2, random_state=2, cluster_std=1.5)
plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='RdBu');
Explanation: Scikit-Learn 朴素贝叶斯分类
朴素贝叶斯... |
11,799 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Vectorization and JIT compilation in Python
Examples for the QuantEcon 2017 PhD workshops
John Stachurski
Step1: Problem 1
Step2: Here's a typical time series
Step3: Here's a function tha... | Python Code:
import numpy as np
from numba import vectorize, jit, float64
from quantecon.util import tic, toc
import matplotlib.pyplot as plt
Explanation: Vectorization and JIT compilation in Python
Examples for the QuantEcon 2017 PhD workshops
John Stachurski
End of explanation
α = 4
Explanation: Problem 1: A Time Ser... |
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