Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
|---|---|---|
3,500 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
%load_ext Cython
%%cython
cimport cython
cimport numpy as np
@cython.wraparound(False)
@cython.boundscheck(False)
def cython_diff2d(np.ndarray[double, ndim=2] u,np.ndarray[double, ndim=2] v, double dx2, double dy2, doub... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Parametry symulacji
Step2: Walidacja wyników
Step3: znamy rozwiązanie równania dyfuzji na nieskończonym obszarze startujące z punktu
Step5: ... |
3,501 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
x=pd.DataFrame() #Mejor hasta ahora
for m in range(1995,2018):
if m < 2016:
o='.xlsx'
else:
o='.xls'
if m < 2000:
sK=3
else:
sK=2
n='Precio_Bolsa_Nacional_($kwh)_' + str(m) + o
y=pd.read_excel(n, skiprows=sK... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 2.-- Compute e imprima el número de registros con datos faltantes.
Step2: 3.-- Compute e imprima el número de registros duplicados.
Step3: 4.-... |
3,502 | <ASSISTANT_TASK:>
Python Code:
from pymldb import Connection
mldb = Connection()
%%bash
mkdir -p /mldb_data/data
curl "file://mldb/mldb_test_data/ml-20m.zip" 2>/dev/null > /mldb_data/data/ml-20m.zip
unzip /mldb_data/data/ml-20m.zip -d /mldb_data/data
%%bash
head /mldb_data/data/ml-20m/README.txt
%%bash
head /mldb_dat... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Download the MovieLens 20M data
Step3: Load the data into MLDB
Step4: Take a peek at the dataset
Step5: Singular Value Decomposition (SVD)
St... |
3,503 | <ASSISTANT_TASK:>
Python Code:
N = 6000
known_labels_ratio = 0.1
X, y = make_moons(n_samples=N, noise=0.1, shuffle=True)
rp = np.random.permutation(int(N/2))
data_P = X[y==1][rp[:int(len(rp)*known_labels_ratio)]]
data_U = np.concatenate((X[y==1][rp[int(len(rp)*known_labels_ratio):]], X[y==0]), axis=0)
print("Amount of ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Transductive PU learning
|
3,504 | <ASSISTANT_TASK:>
Python Code:
DISPLAY_ROWS = 6 # screen is 6 pixels tall
DISPLAY_COLS = 50 # screen is 50 pixels wide
display = [ # set display pixels to False
[False for i in range(0, DISPLAY_COLS)]
for i in range(0, DISPLAY_ROWS)]
def rect(display, a, b):
'''rect AxB turns on all of the pixels in ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Instructions patterns and handlers
Step2: Retrieving the input
Step3: Counting number of pixels that are ON
Step4: Part Two
|
3,505 | <ASSISTANT_TASK:>
Python Code:
import skrf as rf
ring_slot = rf.Network('data/ring slot.s2p')
from skrf.data import ring_slot
ring_slot
short = rf.data.wr2p2_short
delayshort = rf.data.wr2p2_delayshort
short - delayshort
short/delayshort
short = rf.data.wr2p2_short
line = rf.data.wr2p2_line
delayshort = line ** ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: If this produces an error, please see the installation tutorial.
Step2: If you cant find ring slot.s2p, then just import it from the skrf.data ... |
3,506 | <ASSISTANT_TASK:>
Python Code:
from sklearn.model_selection import train_test_split, KFold
from sklearn.linear_model import LinearRegression, Ridge, SGDRegressor, ElasticNet
from sklearn.kernel_ridge import KernelRidge
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegre... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: **DATASET WAS TAKEN FROM https
Step2: Women is coded as 1 vs Man being 0 so that's why there is negative correlation between sex and shoe size
... |
3,507 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# read data
df = pd.read_csv('HR_comma_sep.csv')
# print first rows
df.head()
# print info, we have no nulls
df.info()
# describe numeric columns
# satisfaction_level and last_evaluation seems percen... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Probability, Expectation Values, and Variance
Step2: There seems to be a difference but before we draw any conclusion we would need to perform ... |
3,508 | <ASSISTANT_TASK:>
Python Code:
from dynamite.operators import sigmax, sigmaz, index_sum, op_sum
# the None default argument will be important later
def build_hamiltonian(L):
interaction = op_sum(index_sum(sigmax(0)*sigmax(i), size=L) for i in range(1,L))
uniform_field = 0.5*index_sum(sigmaz(), size=L)
retur... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: If we look at the nonzero structure of the matrix, it's not at all clear that it's block diagonal
Step2: This is a graphical representation of ... |
3,509 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import yahoo_finance
from yahoo_finance import Share
import numpy as np
import pandas
import matplotlib.pyplot as plt
import datetime
import cvxopt as opt
from cvxopt import blas, solvers
# We will do a lot of optimizations,
# and don't want to see each step.
solvers.o... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: getTimeSeries( ticker, start_date, end_date)
Step2: getMultTimeSeries( tickers, start_date, end_date)
Step7: markowitzReturns( returns)
Step8:... |
3,510 | <ASSISTANT_TASK:>
Python Code:
# restart your notebook if prompted on Colab
try:
import verta
except ImportError:
!pip install verta
HOST = "app.verta.ai"
PROJECT_NAME = "Census Income Classification"
EXPERIMENT_NAME = "Logistic Regression"
WORKSPACE = "XXXXX"
import os
os.environ['VERTA_EMAIL'] = 'XXXXXXXXXX'... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: This example features
Step2: Phase 1
Step3: Prepare data
Step4: Prepare hyperparameters
Step5: Train models
Step6: Revisit Workflow
Step7: ... |
3,511 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
from jyquickhelper import add_notebook_menu
add_notebook_menu()
from mlstatpy.data.wikipedia import download_pageviews
import os
from datetime import datetime
download_pageviews(datetime(2018,2,1), folder=".")
with open("pageviews-20180... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Récupérer un fichier wikipédia
Step2: On ne garde que les pages françaises.
Step3: Les données sont biaisées car les pages non démandées par l... |
3,512 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Sparsity preserving clustering Keras example
Step2: Train a tf.keras model for MNIST to be pruned and clustered
Step3: Evaluate the baseline m... |
3,513 | <ASSISTANT_TASK:>
Python Code:
from databaker.framework import *
# put your input-output files here
inputfile = "example1.xls"
outputfile = "example1.csv"
previewfile = "preview.html"
from databaker.framework import *
tab = loadxlstabs("example1.xls", sheetids="stones", verbose=True)[0]
print(tab)
cellbag = tab
print... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Cell bag selection
Step2: cellbag.is_XXX()
Step3: cellbag.filter(word)
Step4: cellbag1.union(cellbag2)
Step5: cellbag1.waffle(cellbag2)
Step... |
3,514 | <ASSISTANT_TASK:>
Python Code:
import math
def euclidean_distance(x1, y1, x2, y2):
return math.sqrt((x1 - x2) ** 2 + (y1-y2) ** 2)
euclidean_distance(0,0,1,1)
values_list = [0,0,1,1]
euclidean_distance(*values_list)
values_tuple = (0,0,1,1)
euclidean_distance(*values_tuple)
values_dict = { 'x1': 0, 'y1': 0, 'x2':... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: We can unpack a list or tuple into positional arguments using a star *
Step2: Similarly, we can use double star ** to unpack a dictionary into ... |
3,515 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from ray import tune
def train_function(config, checkpoint_dir=None):
for i in range(30):
loss = config["mean"] + config["sd"] * np.random.randn()
tune.report(loss=loss)
api_key = "YOUR_COMET_API_KEY"
project_name = "YOUR_COMET_PROJECT_NAME"
# This ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Now, given that you provide your Comet API key and your project name like so
Step2: You can add a Comet logger by specifying the callbacks argu... |
3,516 | <ASSISTANT_TASK:>
Python Code:
# Login information (Edit here or be prompted by the next cell)
email = None
mcurl = "https://materialscommons.org/api"
# Construct a Materials Commons client
from materials_commons.cli.user_config import make_client_and_login_if_necessary
if email is None:
print("Account (email):")
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Cloning a project
Step2: Example 1
Step3: Example 2
Step4: Example 3
Step5: Using the ClonedProject
Step6: File transfer
Step7: Upload one... |
3,517 | <ASSISTANT_TASK:>
Python Code:
[x**2 for x in range(0,10)]
[x for x in range(1,20) if x%2==0 ]
[x for x in 'MATHEMATICS' if x in ['A','E','I','O','U']]
for i in range(1,101):
if int(i**0.5)==i**0.5:
print i
[i for i in range(1,101) if int(i**0.5)==i**0.5]
import numpy as np
# matrix = [ range(0,5), range(5... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Eg1
Step2: Eg2
Step3: Eg3
Step4: Additional examples (mentioned as exercise for users)
Step5: Eg
Step6: Eg
Step7: The Time Advantage
Step8... |
3,518 | <ASSISTANT_TASK:>
Python Code:
import collections
print(collections.Counter(['a', 'b', 'c', 'a', 'b', 'b']))
print(collections.Counter({'a': 2, 'b': 3, 'c': 1}))
print(collections.Counter(a=2, b=3, c=1))
import collections
c = collections.Counter()
print('Initial :', c)
c.update('abcdaab')
print('Sequence:', c)
c.upda... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: An empty Counter can be constructed with no arguments and populated via the update() method
Step2: Accessing Counts
Step3: The elements() meth... |
3,519 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
from numpy import nonzero
import matplotlib.pyplot as plt # to generate plots
from mpl_toolkits.basemap import Basemap # plot on map projections
import matplotlib.dates as mdates
import datetime
from netCDF4 import Dataset # http:... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 2. Set and read input NetCDF file info
Step2: 2.2 Parse time
Step3: 3. Subregion for nino3 area
Step4: time
Step5: Get Index using np.nonzer... |
3,520 | <ASSISTANT_TASK:>
Python Code:
#import modules
import numpy as np
from matplotlib import pyplot as plt
# Help function
def is_zero_vector(v):
Check whether vector v is a zero vector
Arguments:
- v : vector
Return:
- True if v is a nonzero vector. Otherwise, false
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: ★ Eigenvalues And Singular Values ★
Step3: 12.1 power Iteration methods
Step4: Example
Step6: Theorem
Step7: Example
Step9: Rayleigh Quotie... |
3,521 | <ASSISTANT_TASK:>
Python Code:
import magma as m
import mantle
@m.circuit.combinational
def full_adder(A: m.Bit, B: m.Bit, C: m.Bit) -> (m.Bit, m.Bit):
return A ^ B ^ C, A & B | B & C | C & A # sum, carry
import fault
tester = fault.PythonTester(full_adder)
assert tester(1, 0, 0) == (1, 0), "Failed"
assert teste... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: A full adder has three single bit inputs, and returns the sum and the carry. The sum is the exclusive or of the 3 bits, the carry is 1 if any tw... |
3,522 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import openpathsampling as paths
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from openpathsampling.visualize import PathTreeBuilder, PathTreeBuilder
from IPython.display import SVG, HTML
def ipynb_visualize(movevis):
Default settings to sh... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Single Replica TIS
Step2: Open the storage and load things from it.
Step3: One of the points of SRTIS is that we use a bias (which comes from ... |
3,523 | <ASSISTANT_TASK:>
Python Code:
from os import path as p, chdir
if 'examples' in p.abspath('.'):
chdir('..')
from riko.modules.fetchpage import pipe
url = 'https://news.ycombinator.com/'
next(pipe(conf={'url': url, 'start': '<title>', 'end': '</title>'}))
from riko.modules.xpathfetchpage import pipe
xpath = '/html... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Examples
Step2: Fetch a webpage using an xpath
Step3: Word Count
Step4: Fetching feeds
Step5: Please see the FAQ for a complete list of supp... |
3,524 | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.append('..')
import socnet as sn
sn.node_size = 3
sn.node_color = (0, 0, 0)
sn.edge_width = 1
sn.edge_color = (192, 192, 192)
sn.node_label_position = 'top center'
g = sn.load_graph('twitter.gml')
sn.show_graph(g, nlab=True)
<END_TASK> | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Carregando e visualizando o grafo
|
3,525 | <ASSISTANT_TASK:>
Python Code:
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
y = iris.target
from sklearn.cross_validation import cross_val_score
from sklearn.svm import LinearSVC
cross_val_score(LinearSVC(), X, y, cv=5)
cross_val_score(LinearSVC(), X, y, cv=5, scoring="f1_macro")
y % 2
cross... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Let's go to a binary task for a moment
Step2: Implementing your own scoring metric
Step3: There are other ways to do cross-valiation
Step4: E... |
3,526 | <ASSISTANT_TASK:>
Python Code:
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.5
import numpy as np
from matplotlib import pyplot as plt
import tensorflow as tf
print(tf.__version__)
x = tf.constant([2, 3, 4])
x
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Operations on Tensors
Step2: Point-wise operations
Step3: NumPy Interoperability
Step4: You can convert a native TF tensor to a NumPy array u... |
3,527 | <ASSISTANT_TASK:>
Python Code:
# Inicializacao
%matplotlib inline
import numpy as np
from matplotlib import pyplot as plt
# Abrindo conjunto de dados
import csv
with open("biometria.csv", 'rb') as f:
dados = list(csv.reader(f))
rotulos_volei = [d[0] for d in dados[1:-1] if d[0] is 'V']
rotulos_futebol = [d[0] ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Teorema de Bayes
Step2: Podemos verificar a estabilidade do modelo para diferentes tamanhos de conjunto de treino de forma semelhante a que fiz... |
3,528 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import math
data_path = 'Bike-Sharing-Dataset/hour.csv'
rides = pd.read_csv(data_path)
rides.head()
rides[:24*10].plot(x='dteday', y='cnt')
d... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Load and prepare the data
Step2: Checking out the data
Step3: Dummy variables
Step4: Scaling target variables
Step5: Splitting the data into... |
3,529 | <ASSISTANT_TASK:>
Python Code:
%%sh
# ls -l ~/Downloads/G20*csv
# mv ~/Downloads/G20*csv G20.csv
data = pd.read_csv('G20.csv')
cols = ['Area', 'Population_2010', 'Population_2011',
'Population_2012', 'Population_2013', 'Population_2014',
'Population_2015', 'GDP_2010', 'GDP_2011', 'GDP_2012', 'GDP_2013',
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Data Cleanup
Step2: Experiments
Step3: Ideas
Step4: IRIS Dataset
Step5: Random Forest
Step6: SVM
|
3,530 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import astropy.table
import astropy.cosmology
import astropy.io.fits as fits
import astropy.units as u
import os.path
assert 'DESIMODEL' in os.environ
assert os.path.exists(os.path.join(os.getenv('DESIMODEL'), 'data', 'spectra', 'spec-sky.dat'))
import desimodel
import spe... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Parts of this notebook assume that the desimodel package is installed (both its git and svn components) and its data/ directory is accessible vi... |
3,531 | <ASSISTANT_TASK:>
Python Code:
import os
import zipfile
from math import log, sqrt
import numpy as np
import pandas as pd
from sklearn import linear_model
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
%matplotlib inline
# Put files in current direction into a ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Unzipping files with house sales data
Step2: Load in house sales data
Step3: Create new features
Step4: Squaring bedrooms will increase the s... |
3,532 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
sns.set_style('white')
from scipy.interpolate import griddata
xb = np.array([-5,-4,-3,-2,-1,0,1,2,3,4,5])
yb = np.array([-5,-5,-5,-5,-5,-5,-5,-5,-5,-5,-5])
yt = np.array([5]*11)
yc = np.array(0)
x... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Sparse 2d interpolation
Step2: The following plot should show the points on the boundary and the single point in the interior
Step3: Use meshg... |
3,533 | <ASSISTANT_TASK:>
Python Code:
import random
num = [random.randint(0,10) for i in range(1000)]
hist = {}
for i in num:
hist[i] = hist.get(i, 0) + 1
hist
def count1(num):
hist = {}
for i in num:
hist[i] = hist.get(i, 0) + 1
return hist
%timeit count1(num)
def count2(num):
hist = {}
for ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Mesurer le temps que cela prend
Step2: Comparons avec une autre implémentation
Step3: Et une dernière version, la plus rapide
|
3,534 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'dwd', 'sandbox-3', 'aerosol')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "ema... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
3,535 | <ASSISTANT_TASK:>
Python Code:
year = arange(1955,2005,5)
y = array([ -0.0480, -0.0180, -0.0360, -0.0120, -0.0040,
0.1180, 0.2100, 0.3320, 0.3340, 0.4560 ])
fig,ax = subplots()
ax.scatter(year,y,color="k",label="data")
xlabel("year")
ylabel("anomaly (degrees C)")
title("World temperature anomaly");
t = (year-1950)... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: A polynomial interpolant can be used to fit the data. Here we build one using a Vandermonde matrix. First, though, we express time as decades si... |
3,536 | <ASSISTANT_TASK:>
Python Code:
# Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: BigBiGAN으로 이미지 생성하기
Step2: 설정
Step7: 이미지를 표시하는 일부 함수 정의하기
Step8: BigBiGAN TF Hub 모듈을 로드하고 사용 가능한 기능 표시하기
Step19: 다양한 함수에 편리하게 액세스할 수 있도록 래퍼 ... |
3,537 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
view_sentence_range = (10, 20)
DON'T MODIFY A... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Language Translation
Step3: Explore the Data
Step6: Implement Preprocessing Function
Step8: Preprocess all the data and save it
Step10: Chec... |
3,538 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sampling_rate = 20 # This quantity is on Hertz
step = 1.0 / sampling_rate
Tmax = 20.0
time = np.arange(0, Tmax, step)
N_to_use = 1024 # Should be a power of two.
print("The smalles frequency that the FFT will disce... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Size of the FFT
Step2: Analysis of the sampling rate on the limits of what the FFT can tell us.
Step3: A word about frequencies units and the ... |
3,539 | <ASSISTANT_TASK:>
Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Stefan Appelhoff <stefan.appelhoff@mailbox.org>
# Richard Höchenberger <richard.hoechenberger@gmail.com>
#
# License: BSD-3-Clause
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Set parameters
Step2: Frequency analysis
Step3: Now, let's take a look at the spatial distributions of the PSD, averaged
Step4: Alternatively... |
3,540 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
#Load libraries for data processing
import pandas as pd #data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
from scipy.stats import norm
## Supervised learning.
from sklearn.preprocessing import StandardScaler
from sklear... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Classification with cross-validation
Step2: To get a better measure of prediction accuracy (which you can use as a proxy for “goodness of fit” ... |
3,541 | <ASSISTANT_TASK:>
Python Code:
# install Pint if necessary
try:
import pint
except ImportError:
!pip install pint
# download modsim.py if necessary
from os.path import exists
filename = 'modsim.py'
if not exists(filename):
from urllib.request import urlretrieve
url = 'https://raw.githubusercontent.com/A... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: To paraphrase two Georges, "All models are wrong, but some models are
Step2: When this function is called, it modifies bikeshare. As long as th... |
3,542 | <ASSISTANT_TASK:>
Python Code:
import graphlab
sales = graphlab.SFrame('kc_house_data.gl/')
# In the dataset, 'floors' was defined with type string,
# so we'll convert them to int, before using it below
sales['floors'] = sales['floors'].astype(int)
import numpy as np # note this allows us to refer to numpy as np in... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Load in house sales data
Step2: If we want to do any "feature engineering" like creating new features or adjusting existing ones we should do t... |
3,543 | <ASSISTANT_TASK:>
Python Code:
def arb(M):
for x in M:
return x
assert False, 'Error: arb called with empty set!'
def cart_prod(A, B):
return { (x, y) for x in A for y in B }
def separate(Pairs, States, Σ, 𝛿):
Result = { (q1, q2) for q1 in States
for q2 in States
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: The function cart_prod(A, B) computes the Cartesian product $A \times B$ of the sets $A$ and $B$ where $A \times B$ is defined as follows
Step2:... |
3,544 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
np.random.seed(0)
p = [3.2, 5.6, 9.2]
x = np.arange(-8., 5., 0.1)
y = np.polyval(p, x) + np.random.randn(x.shape[0])*1.
plt.plot(x, y);
# STEP 1 - define your model
def my_model(p, x):
return np.polyval(p, x)
# STEP 2 - define your cost function
def my_costfun(p, x, y):
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: MCMC (emcee)
Step2: a simple example - draw sample from uniformly distribution
Step3: how about Gaussian distribution?
Step4: how to use MCMC... |
3,545 | <ASSISTANT_TASK:>
Python Code:
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as data_utils
import numpy as np
word_pair = [['고양이', '흰'],
['고양이', '동물'],
['국화', '흰'],
['국화', '식물'],
['선인장',... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 1. Dataset 준비
Step2: Dataset Loader 설정
Step3: 2. 사전 설정
Step4: 3. Trainning loop
Step5: 4. Predict & Evaluate
Step6: 5. plot embedding space... |
3,546 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
%matplotlib inline
import cartopy
import cartopy.crs as ccrs
ax = plt.axes(projection=ccrs.PlateCarree())
ax.coastlines()
print('axes type:', type(ax))
ax = plt.axes(projection=ccrs.PlateCarree())
ax.coastlines()
ax.set_global()
plt.plot([-100, 50], [25... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Then let's import the cartopy
Step2: In addition, we import cartopy's coordinate reference system submodule
Step3: Creating GeoAxes
Step4: He... |
3,547 | <ASSISTANT_TASK:>
Python Code:
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.datasets import make_blobs
#create data
data = make_blobs(n_samples=200,n_features=2,centers=4,cluster_std=1.8,random_state=101)
plt.scatter(data[0][:,0],data[0][:,1],c=data[1],cmap='rainbow')
from s... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Create some data
Step2: Visualize data
Step3: Creating Clusters
|
3,548 | <ASSISTANT_TASK:>
Python Code:
from pytrends.request import TrendReq
google_username = "mm.trends.api@gmail.com"
google_password = ""
path = ""
# connect to Google
pytrend = TrendReq(google_username, google_password, custom_useragent='Pytrends')
trend_payload = {'q': 'Pizza, Italian, Spaghetti, Breadsticks, Sausage', '... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: GOOGLEINDEX_US
|
3,549 | <ASSISTANT_TASK:>
Python Code:
%run db2.ipynb
%%sql -q
DROP TABLE CENTRAL_LINE;
CREATE TABLE CENTRAL_LINE
(
STATION_NO INTEGER GENERATED ALWAYS AS IDENTITY,
STATION VARCHAR(31),
UPPER_STATION VARCHAR(31) GENERATED ALWAYS AS (UCASE(STATION))
)
;
INSERT INTO CENTRAL_LINE(STATION)
VALUES 'West Ruislip','Ruisl... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Table of Contents
Step2: Back to Top
Step3: The pattern 'Ruislip' will look for a match of Ruislip
Step4: If you didn't place the % at the be... |
3,550 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
# Use Floyd's cifar-10 dataset if ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Image Classification
Step2: Explore the Data
Step5: Implement Preprocess Functions
Step8: One-hot encode
Step10: Randomize Data
Step12: Che... |
3,551 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import torch
a, b = load_data()
ab = torch.cat((a, b), 0)
<END_TASK> | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
3,552 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import math
import cmath
from scipy.optimize import root
import matplotlib.pyplot as plt
%matplotlib inline
a = ("Table1.txt")
a
class InterfazPolimero:
def __init__ (self,a):
self.a=a
def Lire(self):
self.tab = pd.read_csv(... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Polymère
Step2: Calcul de la concentration finale
Step3: Table des valeurs
Step4: Calcul de c2
Step5: Graphique
Step6: Graphique
Step7: ... |
3,553 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 使用 TensorFlow Transform 预处理数据
Step2: 安装 TensorFlow Transform
Step3: 是否已重新启动运行时？
Step4: 数据：创建一些虚拟数据
Step6: Transform：创建一个预处理函数
Step7: 总结
|
3,554 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
%matplotlib inline
# Code here
from sklearn.datasets import load_iris
iris_dataset = load_iris()
features = iris_dataset.feature_names
data = iris_dataset.data
targets = iris_dataset.target
df = pd.DataFrame(data, co... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Question 1
Step2: Question 2
Step3: Create a pair-plot of the iris dataset similar to this figure using only numpy and
Step4: Question 3
|
3,555 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
# Configurable test settings
channel_count = 3 # Simulate sampling from multiple channels.
sample_count = 8 # Number of samples (each sample -> one value per channel).
N = channel_count * sample_count
src_data = np.arange(1, N + 1, dtype='uint8')
src_chunks = [src_da... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Simulate concatenate behaviour on host (i.e., using numpy)
Step2: Device
Step3: Allocate arrays
Step4: Create Transfer Control Descriptor (TC... |
3,556 | <ASSISTANT_TASK:>
Python Code:
from notebook_preamble import J, V, define
define('pair_up == dup uncons swap unit concat zip')
J('[1 2 3] pair_up')
J('[1 2 2 3] pair_up')
define('total_matches == 0 swap [i [=] [pop +] [popop] ifte] step')
J('[1 2 3] pair_up total_matches')
J('[1 2 2 3] pair_up total_matches')
define... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: I'll assume the input is a Joy sequence of integers (as opposed to a string or something else.)
Step2: Now we need to derive total_matches. It... |
3,557 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
import shutil
print(tf.__version__)
tf.enable_eager_execution()
CSV_COLUMN_NAMES = ["fare_amount","dayofweek","hourofday","pickuplon","pickuplat","dropofflon","dropofflat"]
CSV_DEFAULTS = [[0.0],[1],[0],[-74.0], [40.0], [-74.0], [40.7]]
def parse_row(row):
fie... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Input function reading from CSV
Step2: Run the following test to make sure your implementation is correct
Step3: Exercise 2
Step4: Tests
Step... |
3,558 | <ASSISTANT_TASK:>
Python Code:
print(conf.toDebugString()) #Instance of SparkConf with options set by the extension
conf.setAppName('ExtensionTestingApp')
#conf.setMaster('spark://dell-inspiron:7077') # if master is started using command line
conf.setMaster('local[*]')
from pyspark import SparkContext
sc=SparkContext.... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: User adds other options and starts the spark context
Step2: Example spark job
|
3,559 | <ASSISTANT_TASK:>
Python Code:
import sys # system module
import pandas as pd # data package
import matplotlib.pyplot as plt # graphics module
import datetime as dt # date and time module
import numpy as np
%matplotlib inline
plt.style.use("gg... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Quandl <a id=data></a>
Step2: We can also pass start_date and end_date parameters to control the dates for the downloaded data
Step3: Now, let... |
3,560 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
# Arrays
import numpy as np
# Plotting
import matplotlib.pyplot as plt
# pairinteraction :-)
from pairinteraction import pireal as pi
qd = pi.QuantumDefect("Rb", 50, 0, 0.5)
print("Core polarizability: ac =",qd.ac)
print("Effective coulomb potential")
print(" Z ="... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Our code starts with loading the required modules for the computation. It is irrelevant whether we use the pireal or picomplex modules here, bec... |
3,561 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
import copy
import os
import pandas as pd
import matplotlib.pyplot as plt
import tsam.timeseriesaggregation as tsam
%matplotlib inline
raw = pd.read_csv('testdata.csv', index_col = 0)
def plotTS(data, periodlength, vmin, vmax, label = 'T [°C]'):
fi... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Input data
Step2: Create a plot function for the temperature for a visual comparison of the time series
Step3: Hierarchical aggregation with m... |
3,562 | <ASSISTANT_TASK:>
Python Code:
from os import path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.forward import make_forward_dipole
from mne.evoked import combine_evoked
from mne.simulation import simulate_evoked
data_path = mne.datasets.sample.data_path()
subjects_dir = op.join(data_path... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Let's localize the N100m (using MEG only)
Step2: Calculate and visualise magnetic field predicted by dipole with maximum GOF
Step3: Estimate t... |
3,563 | <ASSISTANT_TASK:>
Python Code:
from os.path import basename, exists
def download(url):
filename = basename(url)
if not exists(filename):
from urllib.request import urlretrieve
local, _ = urlretrieve(url, filename)
print("Downloaded " + local)
download("https://github.com/AllenDowney/Thin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Exponential distribution
Step2: Here's the distribution of interarrival times from a dataset of birth times.
Step3: Here's what the CCDF looks... |
3,564 | <ASSISTANT_TASK:>
Python Code:
config = configparser.ConfigParser()
config.sections()
config.read('example.ini')
config.sections()
'bitbucket.org' in config
'bytebong.com' in config
config['bitbucket.org']['User']
config['DEFAULT']['Compression']
topsecret = config['topsecret.server.com']
topsecret['ForwardX11']
topsec... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Please note that default values have precedence over fallback values. For instance, in our example the 'CompressionLevel' key was specified only... |
3,565 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
url = 'https://raw.githubusercontent.com/henriquepgomide/caRtola/master/data/2019/2019-medias-jogadores.csv'
medias = pd.read_csv(url)
medias.head()
medias.shape
medias.columns
qtd_atletas = len(medias['player_id'].unique())
print(qtd_atletas)
posicoes = medias['play... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Quantidade única de jogadores é do mesmo tamanho do Dataframe.
Step2: Para o contexto desse estudo, vamos analisar cada posição utilizada no Ca... |
3,566 | <ASSISTANT_TASK:>
Python Code:
print(" pi ~= 3.14 (two first digits).")
print(" pi ~= 22/7 = {} (two first digits).".format(22.0 / 7.0))
print(" pi ~= 355/113 = {} (six first digits).".format(355.0 / 113.0))
def mathpi():
from math import pi
return pi
print("First method: using math.pi gives pi ~= {:.17f} (17 ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: This method is extremely limited, and will not give you more than 13 correct digits, as math.pi is stored as a float number (limited precision).... |
3,567 | <ASSISTANT_TASK:>
Python Code:
# You want to be able to rotate scatterplots in 3D, so don't show them inline
%matplotlib tk
# 'pip install bunch' if you do not have 'bunch' package
import bunch
# Our utility code resides in module dim_reduce.py, which we import here:
import dim_reduce
from sklearn.decomposition impor... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Now let us apply a PCA dimensionality reduction method to the "iris" dataset (which is 4D).
Step2: A 3D scatterplot should display in a separat... |
3,568 | <ASSISTANT_TASK:>
Python Code:
from auxi.tools.chemistry import thermochemistry as thermo
#TODO: The following line of code is not working, and must be fixed.
#thermo.convert_fact_file_to_auxi_thermo_file("path/to/factsage_file", "path/to/new_auxi_thermo_file")
thermo.list_compounds()
thermo.load_data_auxi('data')
th... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Loading Thermochemical Data
Step2: The result lists all the compounds with the phases for which data are available. Taking the compound SiO2 as... |
3,569 | <ASSISTANT_TASK:>
Python Code:
!pip install -q -U apache-beam[gcp]
# Automatically restart kernel after installs
import IPython
app = IPython.Application.instance()
app.kernel.do_shutdown(True)
import os
from datetime import datetime
import apache_beam as beam
from apache_beam.io.gcp.datastore.v1new.datastoreio impo... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Import libraries
Step2: Configure GCP environment settings
Step3: Authenticate your GCP account
Step4: Copy the public playlist data into you... |
3,570 | <ASSISTANT_TASK:>
Python Code:
# TFlearn libraries
import tflearn
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
import tflearn.datasets.mnist as mnist
# General purpose libraries
import matplotlib... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Gathering Data
Step2: It looks like each sample (55k samples in the training set and 10k samples in the test set). Let's just try to output 1 i... |
3,571 | <ASSISTANT_TASK:>
Python Code:
%%file sq.py
def square(n):
return n*n
import firefly
remote_sq = firefly.Client("http://127.0.0.1:8000")
remote_sq.square(n=4)
%%file add.py
# your code here
%%file credit_grade.py
Program to find the credit grade of a person.
import zlib
import random
def find_credit_grade(email)... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Let us run it as a service using firefly by running the following command in your terminal.
Step2: The function will be available with the same... |
3,572 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Basic regression
Step2: Auto MPG 数据集
Step3: 数据清洗
Step4: 为了保证这个初始示例的简单性，删除这些行。
Step5: "Origin" 列实际上是分类的，而不是数字。因此，使用 pd.get_dummies 将其转换为独热码：
... |
3,573 | <ASSISTANT_TASK:>
Python Code:
# Load libraries
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
# Generate features matrix and target vector
X, y = make_classification(n_samples = 10000,
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Generate Features And Target Data
Step2: Create Logistic Regression
Step3: Cross-Validate Model Using F1
|
3,574 | <ASSISTANT_TASK:>
Python Code:
from ggplot import *
import pandas as pd
from sklearn import datasets
# import iris data
iris = datasets.load_iris()
df1 = pd.DataFrame(iris.data, columns = iris.feature_names)
df2 = pd.DataFrame(iris.target_names[iris.target])
df = pd.concat([df1, df2], axis = 1)
df.head()
df.columns = ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Data
Step2: geom_point (scatter plot)
Step3: The plot shows that setosa class can be linearly separated from other two classes.
Step4: geom_p... |
3,575 | <ASSISTANT_TASK:>
Python Code:
import opsimsummary as oss
from opsimsummary import Tiling, HealpixTiles
# import snsims
import healpy as hp
%matplotlib inline
import matplotlib.pyplot as plt
class NoTile(Tiling):
pass
noTile = NoTile()
class MyTile(Tiling):
def __init__(self):
pass
@property
d... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: This section pertains to how to write a new Tiling class
Step2: ```
Step4: Using the class HealpixTiles
|
3,576 | <ASSISTANT_TASK:>
Python Code:
import seaborn as sns;
sns.set(color_codes=True)
tips = sns.load_dataset("tips")
ax = sns.barplot(x="day", y="total_bill", data=tips)
ax = sns.barplot(x="day", y="total_bill", hue="sex", data=tips)
from echarts import Echart, Legend, Bar, Axis, Line
from IPython.display import HTML
char... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: https
|
3,577 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import itertools
pop_size = 60
seq_length = 100
alphabet = ['A', 'T', 'G', 'C']
base_haplotype = "AAAAAAAAAA"
pop = {}
pop["AAAAAAAAAA"] = 40
pop["AAATAAAAAA"] = 30
pop["AATTTAAAAA"] = 30
pop["AAATAAAAAA"]
mutation_rate = 0.0001 # per gen per individual per site
def... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Make population dynamic model
Step2: Setup a population of sequences
Step3: Add mutation
Step4: Walk through population and mutate basepairs.... |
3,578 | <ASSISTANT_TASK:>
Python Code:
from collatex import *
collation = Collation()
witness_1707 = open( "../data/sonnet/Lope_soneto_FR_1707.txt", encoding='utf-8' ).read()
witness_1822 = open( "../data/sonnet/Lope_soneto_FR_1822.txt", encoding='utf-8' ).read()
collation.add_plain_witness( "wit 1707", witness_1707 )
collatio... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Imagine that we are not interested in punctuation and capitalization
Step2: Now, let's collate the normalized copies.
Step3: Normalization 2. ... |
3,579 | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.append('../../metal')
import metal
%load_ext autoreload
%autoreload 2
%matplotlib inline
import pickle
with open("data/basics_tutorial.pkl", 'rb') as f:
X, Y, L, D = pickle.load(f)
X.shape
Y.shape
L.shape
from metal.utils import split_data
Xs, Ys, Ls, Ds = split_... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Step 1
Step2: If you need to divide your data into splits, you can do so with the provided utility function. We split our data 80/10/10 into tr... |
3,580 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import os
import matplotlib.pyplot as plt
%matplotlib inline
from cycler import cycler
from pylab import rcParams
rcParams['figure.figsize'] = 8, 6
rcParams.update({'font.size': 15})
# color and linestyle cycle
#colors = [x['color'] for x in list(rcParams['axes.prop_cyc... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Merge multiple network definitions that share the same data layers into a single definition to train within the same single process
Step2: Afte... |
3,581 | <ASSISTANT_TASK:>
Python Code:
# Load pickled data
import pickle
import pandas as pd
# TODO: Fill this in based on where you saved the training and testing data
training_file = 'data/train.p'
validation_file= 'data/valid.p'
testing_file = 'data/test.p'
with open(training_file, mode='rb') as f:
train = pickle.load(f... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: The MNIST data that TensorFlow pre-loads comes as 28x28x1 images.
Step2: Visualize Data
Step3: Preprocess Data
Step4: Setup TensorFlow
Step5:... |
3,582 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
def tokenize(s, stop_words=[] or '', punctuation='`~!@#$%^&*()_-+={[}]|\:;"<,>.?/}\t'):
Split a string into a list of words, removing punctuation and stop words.
a = s.splitlines()
b = []
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step3: Word counting
Step5: Write a function count_words that takes a list of words and returns a dictionary where the keys in the dictionary are the ... |
3,583 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
%matplotlib inline
import freqopttest.util as util
import freqopttest.data as data
import freqopttest.kernel as kernel
import freqopttest.tst as tst
import freqopttest.glo as glo
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as st... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: mean embedding test. J=2 locations
Step2: This showed that if both the test locations are the same at [0, 0], then the covariance matrix is sin... |
3,584 | <ASSISTANT_TASK:>
Python Code:
import vcsn
%%automaton a
context = "lal_char(abc), b"
$ -> 0
0 -> 1 a
1 -> $
2 -> 0 a
1 -> 3 a
a.is_accessible()
a.accessible()
a.accessible().is_accessible()
<END_TASK> | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: The following automaton has states that cannot be reached from the initial(s) states
Step2: Calling accessible returns a copy of the automaton ... |
3,585 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from sklearn.model_selection import train_test_split
# Let X be our input data consisting of
# 5 samples and 2 features
X = np.arange(10).reshape(5, 2)
# Let y be the target feature
y = [0, 1, 2, 3, 4]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Validation Data
Step2: Estimators
|
3,586 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
A = np.array([1,1,2,3,3,3,4,5,6,7,8,8])
B = np.array([1,2,8])
C = A[~np.in1d(A,B)]
<END_TASK> | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
3,587 | <ASSISTANT_TASK:>
Python Code:
# Installs the vit_jax package from Github.
!pip install -q git+https://github.com/google-research/vision_transformer
import jax
import jax.numpy as jnp
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_datasets as tfds
i... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Use model
Step2: tfds zero-shot evaluation
|
3,588 | <ASSISTANT_TASK:>
Python Code:
mondat="A "
mondat+="mezőn legelésző "
mondat+="bárányok "
mondat+="mélyen "
mondat+="hallgatnak."
print(mondat)
kisbetuk='qwertzuiopasdfghjklyxcvbnm'
nagybetuk='QWERTZUIOPASDFGHJKLYXCVBNM'
extra='+- %=.~'
kicsi=['al',9,'+',42.137,'szoveg',69,1j]
telefon_konyv={'Alonzo Hinton': '(855) ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 02-egyszerű számolás
Step2: 04-lista manipulálás
Step3: 05-szótár kezelés
Step4: 06-logikai
|
3,589 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
s = pd.Series([1,3,5,np.nan,6,8])
s
dates = pd.date_range('20130101', periods=6)
dates
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
df
df2 = pd.DataFrame({ 'A' : 1.,
'B' : pd.Timestamp('20130102'),
'C' : pd.Seri... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Series
Step2: DataFrame
Step3: Creating a DataFrame by passing a dict of objects that can be converted to series-like.
Step4: Panel
Step5: V... |
3,590 | <ASSISTANT_TASK:>
Python Code:
# Jupyter setup to expand cell display to 100% width on your screen (optional)
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# Import relevant modules and setup for calling glmnet
%reset -f
%matplotlib inline
import sy... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: As an example, we set $\alpha = 0.2$ (more like a ridge regression), and give double weights to the latter half of the observations. To avoid to... |
3,591 | <ASSISTANT_TASK:>
Python Code:
# from qiita_db.study import Study
# from shutil import copy
# from os import mkdir
# ffp = '/home/qiita/emp-sample-info-files'
# study_ids = [ 550, 632, 638, 659, 662, 678, 713, 714, 722, 723,
# 755, 776, 804, 805, 807, 808, 809, 810, 829... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Porting refined EMP metadata to Qiita sample info files
Step2: Replace or add columns
Step3: Export action columns for problem studies
|
3,592 | <ASSISTANT_TASK:>
Python Code:
import math
def funcion(x):
return (math.pow(math.e,6*x))+(1.44*math.pow(math.e,2*x))-(2.079*math.pow(math.e,4*x))-(0.333)
def biseccion(intA, intB, errorA, noMaxIter):
if(funcion(intA)*funcion(intB)<0):
noIter = 0
errorTmp = 1
intTmp = 0
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: <h1>Intento de algoritmo de regla falsa</h1>
Step2: <h1>(testing) Intento de algoritmo de Newton - Raphson (en x^10 -1)</h1>
Step3: <h1>Intent... |
3,593 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division
import google.datalab.bigquery as bq
import matplotlib.pyplot as plot
import numpy as np
%bq tables list --project cloud-datalab-samples --dataset httplogs
%bq tables describe -n cloud-datalab-samples.httplogs.logs_20140615
%%bq query -n logs
SELECT timest... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Understanding the Logs Data
Step2: Transforming Logs into a Time Series
Step3: Visualizing the Time Series Data
Step4: Anomaly Detection
Step... |
3,594 | <ASSISTANT_TASK:>
Python Code:
from dx import *
import numpy as np
import pandas as pd
from pylab import plt
plt.style.use('seaborn')
h5 = pd.HDFStore('./data/vstoxx_march_2014.h5', 'r')
vstoxx_index = h5['vstoxx_index']
vstoxx_futures = h5['vstoxx_futures']
vstoxx_options = h5['vstoxx_options']
h5.close()
%matplot... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: VSTOXX Futures & Options Data
Step2: VSTOXX index for the first quarter of 2014.
Step3: The VSTOXX futures data (8 futures maturities/quotes p... |
3,595 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
from gatspy.periodic import LombScargle
import sys
%matplotlib inline
from toy_simulator import simulate_LSST
from trilegal_models import random_stars
import simple_gyro as sg
import pandas as pd
fname = "output574523944248.dat"
N = 100
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Randomly select targets from a TRILEGAL output.
Step2: Calculate periods from ages and colours for cool stars
Step3: Draw from a sum of two Ga... |
3,596 | <ASSISTANT_TASK:>
Python Code:
import pysal.lib
import numpy as np
from pysal.explore.giddy.directional import Rose
%matplotlib inline
f = open(pysal.lib.examples.get_path('spi_download.csv'), 'r')
lines = f.readlines()
f.close()
lines = [line.strip().split(",") for line in lines]
names = [line[2] for line in lines[1:... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Visualization
Step2: Inference
Step3: Here all the four sector counts are signficantly different from their expectation under the null.
Step4:... |
3,597 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
from qutip import *
from qutip.piqs import *
N = 10
system = Dicke(N = N)
[jx, jy, jz] = jspin(N)
jp = jspin(N,"+")
jm = jp.dag()
w0 = 1
h0 = w0 * jz
gCE = 1
gP = N * gCE
system.hamiltonian = h0
system.collective_emission = gCE
system.pumping = gP
L = sys... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 1) Time evolution
Step2: Liouvillian and steady state $\rho_\text{ss}$
Step3: Time integration for $g^{(2)}(\tau)$ and $\langle J_{+}J_{-}\ran... |
3,598 | <ASSISTANT_TASK:>
Python Code:
%load_ext watermark
%watermark -a '' -u -d -v -p numpy,pandas,matplotlib,scipy,sklearn
%matplotlib inline
# Added version check for recent scikit-learn 0.18 checks
from distutils.version import LooseVersion as Version
from sklearn import __version__ as sklearn_version
import numpy as np
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Load data
Step2: Visualize data
Step3: Data sets
Step4: Answer
Step5: Explanation
Step6: Use GridSearchCV to tune hyper-parameters.
Step7: ... |
3,599 | <ASSISTANT_TASK:>
Python Code:
# Setup, just putting together what we will need to do the analysis
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.spines import Spine
from matplotlib.projections.polar import PolarAxes
from matplotlib.projections import register_projection
%matp... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Types Matchups
Step2: Status Analysis
Step3: Archenemies
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.