Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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14,400 | <ASSISTANT_TASK:>
Python Code:
import iris
import iris.quickplot as qplt
temperature = iris.load_cube('air.sig995.2012.nc')
qplt.contourf(temperature[0,:,:])
gca().coastlines()
print temperature
zonal_mean = temperature.collapsed('latitude', iris.analysis.MEAN)
qplt.contourf(zonal_mean)
#Code is a bit more complicat... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: This is how iris cube look like
Step2: We can perform different operations on cubes. For example create zonal mean
Step3: Here we plot timesir... |
14,401 | <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
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Description:
Step1: Word embeddings
Step2: Download the IMDb Dataset
Step3: Take a look at the train/ directory. It has pos and neg folders with movie reviews lab... |
14,402 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
from activation_clustering import ac_model, utils
# The same dataset preprocessing as used in the baseline cifar10 model training.
def input_fn(batch_size, ds, label_key='label'):
dataset = ds.batch(batch_si... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Train an activation clustering model from a baseline model
Step2: Activation clustering model's configurations. The first entry in each pair i... |
14,403 | <ASSISTANT_TASK:>
Python Code:
# See Anaconda installed packages
!conda list
# List environments
!conda info -e
# Create Python 3 environment
!conda create -n py3k python=3 anaconda
# Activate Python 3 environment
!source activate py3k
# Deactivate Python 3 environment
!source deactivate
# Update Anaconda
!conda update... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: <h2 id="ipython-notebook">IPython Notebook</h2>
Step2: | Command | Description |
Step3: <h2 id="git">Git</h2>
S... |
14,404 | <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
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Description:
Step1: Image Classification
Step2: Explore the Data
Step5: Implement Preprocess Functions
Step8: One-hot encode
Step10: Randomize Data
Step12: Che... |
14,405 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
def well2d(x, y, nx, ny, L=1.0):
Compute the 2d quantum well wave function.
scalarfield=(2/L*np.sin(nx*np.pi*x/L)*np.sin(ny*np.pi*y/L))
well=scalarfield
return well
psi = well2d(np.linspace(0,1,10), np.... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Contour plots of 2d wavefunctions
Step3: The contour, contourf, pcolor and pcolormesh functions of Matplotlib can be used for effective visuali... |
14,406 | <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
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Description:
Step1: So far the systems we have studied have been physical in the sense that they exist in the world, but they have not been physics, in the sense of... |
14,407 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
#Review the documentation for NumPy's random module:
np.random?
#print 5 uniformly distributed numbers between 0 and 1
print(np.random.random(5))
#print another 5 - should be different
print(np.random.random(5))
#prin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Random Processes in Physics
Step2: Some basic functions to point out (we'll get to others in a bit)
Step3: Notice you have to use 1-11 for the... |
14,408 | <ASSISTANT_TASK:>
Python Code:
number_list = [1, 2, 4, 8, 16, 32]
the_pythons = ["Graham", "Terry", "Michael", "Eric", "Terry", "John"]
mixed = [1, "Terry", 4]
print (mixed)
monty = ("Graham", "Terry", "Michael", "Eric", "Terry", "John")
# the entire tuple
print (monty)
# one element at a time
for name in monty:
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Tuple
Step2: [] brackets or square brackets
Step3: Why?
Step4: Dictionaries
Step5: Retrieving a Value from a Dictionary
Step6: Testing for ... |
14,409 | <ASSISTANT_TASK:>
Python Code:
strings = "stressed"
print(strings[::-1])
strings1 = u"パタトクカシーー"
print(strings1[::2])
strings_p = u"パトカー"
strings_t = u"タクシー"
strings_sum = ''
for p, t in zip(strings_p, strings_t):
strings_sum += p + t
print(strings_sum)
strings3 = "Now I need a drink, alcoholic of course, after t... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 01 パタトクカシーー
Step2: 02「パトカー」＋「タクシー」＝「パタトクカシーー」
Step3: 03. 円周率
Step4: 04. 元素記号
Step5: 05 n-gram
Step6: 06. 集合
Step7: 07. テンプレートによる文生成
Step8:... |
14,410 | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.append('..')
sys.path.append('../geostatsmodels')
from geostatsmodels import utilities, variograms, model, kriging, geoplot
import matplotlib.pyplot as plt
import numpy as np
import pandas
z = utilities.readGeoEAS('../data/ZoneA.dat')
P = z[:,[0,1,3]]
pt = [2000, 47... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: We'll read the data from ZoneA.dat.
Step2: We want the first, second and fourth columns of the data set, representing the x and y spatial coord... |
14,411 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import vispy
import vispy.gloo as gloo
from vispy import app
from vispy.util.transforms import perspective, translate, rotate
# load the vispy bindings manually for the notebook which enables webGL
# %load_ext vispy
n = 100
a_position = np.random.uniform(-1, 1, (n, 3)).... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Jupyter Notebook backend demo
Step3: Every cell above was preparing our GL Canvas for operation. Now we will create the Canvas instance and bec... |
14,412 | <ASSISTANT_TASK:>
Python Code:
send(IP(dst="1.2.3.4")/TCP(dport=502, options=[("MSS", 0)]))
ans = sr([IP(dst="8.8.8.8", ttl=(1, 8), options=IPOption_RR())/ICMP(seq=RandShort()), IP(dst="8.8.8.8", ttl=(1, 8), options=IPOption_Traceroute())/ICMP(seq=RandShort()), IP(dst="8.8.8.8", ttl=(1, 8))/ICMP(seq=RandShort())], ver... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 2_ Adanced firewalking using IP options is sometimes useful to perform network enumeration. Here is more complicate one-liner
Step2: Now that, ... |
14,413 | <ASSISTANT_TASK:>
Python Code:
%%capture --no-stderr
!pip3 install kfp --upgrade
import kfp.components as comp
dataproc_submit_spark_job_op = comp.load_component_from_url(
'https://raw.githubusercontent.com/kubeflow/pipelines/1.7.0-rc.3/components/gcp/dataproc/submit_spark_job/component.yaml')
help(dataproc_submit... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load the component using KFP SDK
Step2: Sample
Step3: Example pipeline that uses the component
Step4: Compile the pipeline
Step5: Submit the... |
14,414 | <ASSISTANT_TASK:>
Python Code:
li = ["this", "is", "a", "list"]
print(li)
print(li[1:3]) # Print element 1 (inclusive) to 3 (exclusive)
print(li[2:]) # Print element 2 and everything after that
print(li[:-1]) # Print everything BEFORE element -1 (the last one)
import numpy as np
x = np.array([1, 2, 3, 4, 5])
print... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: With NumPy arrays, all the same functionality you know and love from lists is still there.
Step2: These operations all work whether you're usin... |
14,415 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
x = np.arange(25)
x
x = da.arange(25, chunks=(5,))
y = x ** 2
y
y.visualize()
da.sqrt(x)[-1].visualize()
x = da.arange(250, chunks=(5,))
x.visualize()
x = da.ones((15, 15), chunks=(5,5))
x.sum(axis=1).visualize()
import dask.multiprocessing
y.compute(get = dask.multipro... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: <h1>MISSING SEPERATOR ARGS FOR SPACE DELIMITED FILE!!!</h1>
|
14,416 | <ASSISTANT_TASK:>
Python Code:
import crpropa
class ObserverPlane(crpropa.ObserverFeature):
Detects all particles after crossing the plane. Defined by position (any
point in the plane) and vectors v1 and v2.
def __init__(self, position, v1, v2):
crpropa.ObserverFeature.__init__(self)
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Custom Observer
Step3: As test, we propagate some particles in a random field with a sheet observer
Step4: and plot the final position of the ... |
14,417 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
!head -n 30 open_exoplanet_catalogue.txt
data=np.genfromtxt('open_exoplanet_catalogue.txt',delimiter=",")
assert data.shape==(1993,24)
plt.hist(data[:,2],range(0,16));
plt.box(False)
plt.xlabel("$M sin i (M_JUP)$");
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Exoplanet properties
Step2: Use np.genfromtxt with a delimiter of ',' to read the data into a NumPy array called data
Step3: Make a histogram ... |
14,418 | <ASSISTANT_TASK:>
Python Code:
# disable ssl warnings
import urllib3
urllib3.disable_warnings()
keycloak_url = 'http://localhost:8080'
token_endpoint = '/auth/realms/demo/protocol/openid-connect/token'
client_id = 'demo'
client_secret = 'c083d72c-a262-40b1-ad51-326f6977d74b'
token_url = "{}{}".format(keycloak_url, to... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Keycloak client
Step2: Get OAuth access token from Keycloak
Step3: Execute WPS Process with access token
|
14,419 | <ASSISTANT_TASK:>
Python Code:
# Hit shift + enter or use the run button to run this cell and see the results
print 'hello world11_0_11'
print 'hello world'
# The last line of every code cell will be displayed by default,
# even if you don't print it. Run this cell to see how this works.
print 2 + 2 # The result of th... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Nicely formatted results
Step2: Creating cells
Step3: Once you've run all three cells, try modifying the first one to set class_name to your n... |
14,420 | <ASSISTANT_TASK:>
Python Code:
import lasio
import datetime
import numpy
import os
import matplotlib.pyplot as plt
%matplotlib inline
depths = numpy.arange(10, 50, 0.5)
fake_curve = numpy.random.random(len(depths))
fake_curve[-10:] = numpy.nan # Add some null values at the bottom
plt.plot(depths, fake_curve)
l = la... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Step 1
Step2: Step 2
Step3: Let's add some information to the header
Step4: Next, let's make a new item in the ~Parameters section for the op... |
14,421 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib
matplotlib.style.use('ggplot')
# set default figure size
from pylab import rcParams
rcParams['figure.figsize'] = 16, 8
import pandas as pd
import urllib2
def load_data(ip_addr):
data = pd.read_csv(urllib2.urlopen("h... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Loading Data into a Dataframe
Step2: Plotting the Scenarios
Step3: Putting it all Together
|
14,422 | <ASSISTANT_TASK:>
Python Code:
import torch
x = torch.Tensor(5, 3)
print(x)
len(x)
x.shape
y = torch.rand(5,3)
print(y)
print(x + y)
print(torch.add(x, y))
result = torch.Tensor(5, 3)
print(result)
torch.add(x, y, out=result)
print(result)
print('before y:', y)
y.add_(x)
print('after y:', y)
x.t_()
# numpy 스럽게 사용 가능
p... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: x.copy_(y), x.t_()는 x가 변경되는 연산
Step2: 기타 연산 자료
Step3: CharTensor를 제외하고 CPU상의 모든 텐서는 numpy로 변환하는 것을 지원
Step4: tensor들은 .cuda function을 사용해 gpu... |
14,423 | <ASSISTANT_TASK:>
Python Code:
import json
import requests
from requests.adapters import HTTPAdapter
from requests.packages.urllib3.util.retry import Retry
def requests_retry_session(
retries=3,
backoff_factor=0.3,
status_forcelist=(500, 502, 504),
session=None,
):
session = session or requests.Sess... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: MMW production API endpoint base url.
Step2: The job is not completed instantly and the results are not returned directly by the API request th... |
14,424 | <ASSISTANT_TASK:>
Python Code:
from dionysus import Simplex, Filtration, StaticPersistence, \
vertex_cmp, data_cmp, data_dim_cmp, \
DynamicPersistenceChains
from math import sqrt
scx = [Simplex((2,), 0), # C
Simplex((0,), 1), ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: We will compute persistent homology of a 2-simplex (triangle) ABC. The filtration is as follows
Step2: Now the persistent homology is computed.... |
14,425 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import subprocess
import matplotlib.pyplot as plt
import random
import numpy as np
plt.style.use('ggplot')
figsize(10,5)
file = "./bwa/input2.sorted.bam"
p = subprocess.Popen(["samtools", "view", "-q10", "-F260", file],
stdout=subprocess.PIPE)
coord... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Calculate the Nonredundant Read Fraction (NRF)
Step2: Make figures prettier and biger
Step3: Parse the SAM file and extract the unique start c... |
14,426 | <ASSISTANT_TASK:>
Python Code:
# Load pickled data
import pickle
# TODO: Fill this in based on where you saved the training and testing data
DATA_DIR = './traffic-signs-data/'
training_file = DATA_DIR + 'train.p'
validation_file= DATA_DIR + 'valid.p'
testing_file = DATA_DIR + 'test.p'
with open(training_file, mode='rb'... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Step 1
Step2: Include an exploratory visualization of the dataset
Step3: Step 2
Step4: Model Architecture
Step5: Features and Labels
Step6: ... |
14,427 | <ASSISTANT_TASK:>
Python Code:
# Plots will be show inside the notebook
%matplotlib notebook
import matplotlib.pyplot as plt
# NumPy is a package for manipulating N-dimensional array objects
import numpy as np
# Pandas is a data analysis package
import pandas as pd
import problem_unittests as tests
# Load data and pr... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: With Pandas we can load the aforementioned CSV data.
Step2: With the data loaded we can plot it as a scatter plot using matplotlib.
Step4: Mod... |
14,428 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'pcmdi', 'sandbox-3', 'atmos')
# 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
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Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
14,429 | <ASSISTANT_TASK:>
Python Code:
import hashlib
import os
import pickle
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils import resample
from tqdm import tqdm
from zipfil... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step3: The notMNIST dataset is too large for many computers to handle. It contains 500,000 images for just training. You'll be using a subset of this... |
14,430 | <ASSISTANT_TASK:>
Python Code:
2 + 3
2*3
2**3
sin(pi)
from math import sin, pi
sin(pi)
a = 10
a
c =
from pruebas_1 import prueba_1_1
prueba_1_1(_, c)
A = [2, 4, 8, 10]
A
A*2
f = lambda x: x**2 + 1
f(2)
def g(x):
y = x**2 + 1
return y
g(2)
def cel_a_faren(grados_cel):
grados_faren = # Escrib... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Sin embargo no existen funciones trigonométricas cargadas por default. Para esto tenemos que importarlas de la libreria math
Step2: Variables
S... |
14,431 | <ASSISTANT_TASK:>
Python Code::
from sklearn.svm import SVC
from sklearn.metrics import classification_report
# create a linear SVC model with balanced class weights
model = SVC(C=1, kernel='linear', class_weight='balanced')
# fit model
model.fit(X_train, y_train)
# make predictions on test data
y_pred = model.predict(... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
|
14,432 | <ASSISTANT_TASK:>
Python Code:
!pip install --upgrade pip
!pip install -q -U tfx
import os
import tempfile
import urllib
import pandas as pd
import tensorflow_model_analysis as tfma
from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext
from tfx import v1 as tfx
print('TFX vers... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Install and import TFX
Step2: Please ignore the incompatibility error and warnings. Make sure to re-run the cell.
Step3: Import the MLMD libra... |
14,433 | <ASSISTANT_TASK:>
Python Code:
df = pd.read_csv('../data/titanic.csv', index_col='PassengerId')
df.head()
df_no_missing = df[['Survived', 'Pclass', 'Fare', 'Age', 'Sex']].dropna()
X_train_withStrings = df_no_missing[['Pclass', 'Fare', 'Age', 'Sex']]
y_train = df_no_missing['Survived']
def strings_to_int(df, target_col... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 2. Оставьте в выборке четыре признака
Step2: 6. Обучите решающее дерево с параметром random_state=241 и остальными параметрами по умолчанию.
St... |
14,434 | <ASSISTANT_TASK:>
Python Code:
import os
import requests
import pandas as pd
import csv
import urllib2
import openpyxl
import csv
def xls_state():
path_year = os.path.join(os.getcwd())
file_name = path_year + "/" + "MSA_STATE"+ ".xls"
url= "https://www.census.gov/2010census/xls/fips_codes_website.xls"
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: INGESTION
Step2: Define function taht downloads census xls file that contains cbsa and the corresponding msa name and principal cities that be... |
14,435 | <ASSISTANT_TASK:>
Python Code:
from datetime import datetime
import matplotlib.pyplot as plt
import metpy.calc as mpcalc
from metpy.io import get_upper_air_data
from metpy.io.upperair import UseSampleData
from metpy.plots import SkewT
from metpy.units import concatenate
with UseSampleData(): # Only needed to use our l... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Create a new figure. The dimensions here give a good aspect ratio
|
14,436 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import pymc3 as pm
import matplotlib.pyplot as plt
import seaborn
import warnings
warnings.filterwarnings('ignore')
from collections import OrderedDict
from time import time
import numpy as np
import pandas as pd
import matplotlib.... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The Adult Data Set is commonly used to benchmark machine learning algorithms. The goal is to use demographic features, or variables, to predict ... |
14,437 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import cv2
import sys
import os
sys.path.insert(0, os.path.abspath('..'))
import salientregions as sr
import cProfile
%pylab inline
#Load the image
path_to_image = 'images/graffiti.jpg'
img = cv2.imread(path_to_image)
sr.show_image(img)
%%timeit
#Time: creation of the d... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Binarization
Step2: Binary detection
Step3: MSER detection
Step4: Conclusion
|
14,438 | <ASSISTANT_TASK:>
Python Code:
from neon.initializers import Gaussian
from neon.optimizers import GradientDescentMomentum, Schedule
from neon.layers import Conv, Dropout, Activation, Pooling, GeneralizedCost
from neon.transforms import Rectlin, Softmax, CrossEntropyMulti, Misclassification
from neon.models import Model... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Overfitting
Step2: This situation illustrates the importance of plotting the validation loss (blue) in addition to the training cost (red). The... |
14,439 | <ASSISTANT_TASK:>
Python Code:
# Put your code here!
# Put your code here!
from IPython.display import HTML
HTML(
<iframe
src="https://goo.gl/forms/NOKKHPQ0oKn1B7e23?embedded=true"
width="80%"
height="1200px"
frameborder="0"
marginheight="0"
marginwidth="0">
Loading...
</iframe>
)
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Part 2
Step3: Assignment wrapup
|
14,440 | <ASSISTANT_TASK:>
Python Code:
# Load libraries
from sklearn.tree import DecisionTreeClassifier
from sklearn import datasets
# Load data
iris = datasets.load_iris()
X = iris.data
y = iris.target
# Create decision tree classifer object using gini
clf = DecisionTreeClassifier(criterion='gini', random_state=0)
# Train ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load Iris Dataset
Step2: Create Decision Tree Using Gini Impurity
Step3: Train Model
Step4: Create Observation To Predict
Step5: Predict Obs... |
14,441 | <ASSISTANT_TASK:>
Python Code:
import qspectra as qs
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Parameters of the electronic Hamiltonian
ham = qs.ElectronicHamiltonian(np.array([[12881., 120.], [120., 12719.]]),
bath=qs.DebyeBath(qs.CM_K * 77., 35., 106.),
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Absorption spectra
Step2: 2D spectra
|
14,442 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division
import graphlab
products = graphlab.SFrame('amazon_baby_subset.gl/')
# The same feature processing (same as the previous assignments)
# ---------------------------------------------------------------
import json
with open('important_words.json', 'r') as f... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load and process review dataset
Step2: Just like we did previously, we will work with a hand-curated list of important words extracted from the... |
14,443 | <ASSISTANT_TASK:>
Python Code:
# Author: Ivana Kojcic <ivana.kojcic@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
# Kostiantyn Maksymenko <kostiantyn.maksymenko@gmail.com>
# Samuel Deslauriers-Gauthier <sam.deslauriers@gmail.com>
# License: BSD (3-clause)
import os.path as op
import numpy a... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: In order to simulate source time courses, labels of desired active regions
Step3: Create simulated source activity
Step4: Here,
Step5: Simul... |
14,444 | <ASSISTANT_TASK:>
Python Code:
%load_ext snakeviz
%load_ext memory_profiler
%load_ext line_profiler
%load_ext autoreload
%autoreload 2
import re
from collections import Counter
def words(text):
return re.findall(r'\w+', text.lower())
WORDS = Counter(words(open('big.txt').read()))
def P(word, N=sum(WORDS.values())... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: amdahl law, focus on one part at a time
Step2: Casual Profiling 👕👖
Step3: timeit ⌛⌛⌛
Step4: each run does thousand or millions of repetitio... |
14,445 | <ASSISTANT_TASK:>
Python Code:
# Authors: Eric Larson <larson.eric.d@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Mark Wronkiewicz <wronk.mark@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne.preprocessing import maxwell_filter
print(__doc__)
data_path = mne.da... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Set parameters
|
14,446 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'inpe', 'besm-2-7', 'seaice')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "emai... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 2... |
14,447 | <ASSISTANT_TASK:>
Python Code:
import recordlinkage
from recordlinkage.datasets import load_febrl1
dfA = load_febrl1()
dfA
indexer = recordlinkage.Index()
indexer.full()
candidate_links = indexer.index(dfA)
print (len(dfA), len(candidate_links))
# (1000*1000-1000)/2 = 499500
indexer = recordlinkage.Index()
indexer.... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The dataset is loaded with the following code. The returned datasets are
Step2: Make record pairs
Step3: With the method index, all possible (... |
14,448 | <ASSISTANT_TASK:>
Python Code:
# import common packages
import numpy as np
from collections import OrderedDict
# lib from Qiskit Aqua Chemistry
from qiskit_aqua_chemistry import FermionicOperator
# lib from Qiskit Aqua
from qiskit_aqua import Operator
from qiskit_aqua import (get_algorithm_instance, get_optimizer_insta... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Step 1
Step2: Step 2
Step3: We use the classical eigen decomposition to get the smallest eigenvalue as a reference.
Step4: Step 3
Step5: Ste... |
14,449 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import time
import helper
source_path = 'data/letters_source.txt'
target_path = 'data/letters_target.txt'
source_sentences = helper.load_data(source_path)
target_sentences = helper.load_data(target_path)
source_sentences[:50].split('\n')
target_sentences[:50].split('\... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Let's start by examining the current state of the dataset. source_sentences contains the entire input sequence file as text delimited by newline... |
14,450 | <ASSISTANT_TASK:>
Python Code:
import GPy, safeopt
from SafeRLBench.algo import SafeOptSwarm
from SafeRLBench.envs import Quadrocopter, LinearCar
from SafeRLBench.policy import NonLinearQuadrocopterController, LinearPolicy
from SafeRLBench.measure import BestPerformance, SafetyMeasure
from SafeRLBench import Bench
# se... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Linear Car
Step2: Below we output the results of the safety measure. List comprehension is used to get a more readable format for the
Step3: Q... |
14,451 | <ASSISTANT_TASK:>
Python Code:
import numpy
import numpy as np
import sys
import math
import matplotlib.pyplot as plt
def periodic(i,limit,add):
Choose correct matrix index with periodic boundary conditions
Input:
- i: Base index
- limit: Highest \"legal\" index
- add: Number to ad... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Periodic boundary conditions
Step3: Set up spin matrix, initialize to ground state
Step4: Create and initialize variables
Step5: Setup array ... |
14,452 | <ASSISTANT_TASK:>
Python Code:
class Character(object):
def __init__(self):
self.life = 1000
def attacked(self):
self.life -= 10
print(u"공격받음! 생명력 =", self.life)
a = Character()
b = Character()
c = Character()
a.life, b.life, c.life
a.attacked()
b.attacked()
a.attacked()... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 이 클래스로 a, b, c 세 개의 캐릭터 객체를 생성한다.
Step2: 모든 객체의 초기 life 속성값은 모두 1000이다.
Step3: 하지만 공격을 받은 캐릭터의 생명력은 감소된다.
Step4: 클래스 상속
Step5: 이 클래스의 객체를 만들... |
14,453 | <ASSISTANT_TASK:>
Python Code:
poetry_output = !htid2rsync --f data/poetry.txt | rsync -azv --files-from=- data.sharc.hathitrust.org::features/ data/poetry/
scifi_output = !htid2rsync --f data/scifi.txt | rsync -azv --files-from=- data.sharc.hathitrust.org::features/ data/scifi/
outputs = list([poetry_output, scifi_out... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: As in the previous notebooks, we'll construct FeatureReader objects for each corpus. The line below reads in path files we created to the downlo... |
14,454 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import random as rnd
import seaborn as sns
import matplotlib.pyplot as plt
train_df = pd.read_csv('train.csv')
test_df = pd.read_csv('test.csv')
print(train_df.columns.values)
train_df.isnull().sum()
print (train_df.info())
trai... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Importamos los datos para entrenar y testear
Step2: Miramos los datos, para ver que si hay nulos o datos que rellenar, como la edad y la cabina... |
14,455 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import xarray as xr
import climlab
# Get the water vapor data
#datapath = "http://ramadda.atmos.albany.edu:8080/repository/opendap/latest/Top/Users/BrianRose/CESM_runs/"
datapath = "http://thredds.atmos.albany.edu:8080... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Integrate the control model out to equilibrium.
Step2: Now let's make two copies of this model and keep them in a list
Step3: We are going to ... |
14,456 | <ASSISTANT_TASK:>
Python Code:
import datetime
import os
import time
import numpy as np
import pandas as pd
import tensorflow as tf
from google.cloud import aiplatform, storage
from google.cloud.aiplatform import gapic as aip
from sklearn.preprocessing import StandardScaler
# Check the TensorFlow version installed
tf._... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Create a Cloud Storage bucket
Step2: Load and preview the data
Step3: Process data
Step6: Scale values
Step7: Create sequences of time serie... |
14,457 | <ASSISTANT_TASK:>
Python Code:
import numpy
import toyplot
y = numpy.linspace(0, 1, 20) ** 2
toyplot.scatterplot(y, width=300);
canvas = toyplot.Canvas(600, 300)
canvas.axes(grid=(1, 2, 0)).plot(y)
canvas.axes(grid=(1, 2, 1)).plot(y, marker="o");
canvas = toyplot.Canvas(600, 300)
canvas.axes(grid=(1, 2, 0)).plot(y, m... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Markers can also be added to regular plots to highlight the datums (they are turned-off by default)
Step2: You can use the size argument to con... |
14,458 | <ASSISTANT_TASK:>
Python Code:
# Panda will be usefull for quick data parsing
import pandas as pd
import numpy as np
# Small trick to get a larger display
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:90% !important; }</style>"))
import matplotlib.pyplot as pl
%matplotlib inlin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Pyplot is the Matplotlib plotting backend and the inline magic to see the graph directly in the notebook
Step2: Or you can use pylab, which sim... |
14,459 | <ASSISTANT_TASK:>
Python Code:
#load packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
pleiades = pd.read_csv('pleiades.csv')
pleiades
pleiades.columns
pleiades.dtypes
pleiades_L = pleiades["Lbol"]
pleiades_T = pleiades["Teff"]
pleiades_L = pleiades_L - 2
pleiades... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: First off, we'll need to read in the data with the pandas function read_csv. A basic example is given below
Step2: "pleiades" is now a pandas d... |
14,460 | <ASSISTANT_TASK:>
Python Code:
def topo_sort(T, D):
Parents = { t: set() for t in T } # dictionary of parents
Children = { t: set() for t in T } # dictionary of children
for s, t in D:
Children[s].add(t)
Parents [t].add(s)
Orphans = { t for (t, P) in Parents.items() if len(P) == 0 }
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Graphical Representation
Step2: The function toDot(Edges, Order) takes two arguments
Step3: Testing
|
14,461 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
import tensorflow_data_validation as tfdv
import tensorflow_transform as tft
print('TF version: {}'.format(tf.__version__))
print('TFT version: {}'.format(tft.__version__))
print('TFDV version: {}'.format(tfdv.__version__))
PROJECT = 'cloud-training-demos' # Rep... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: <img valign="middle" src="images/tfx.jpeg">
Step2: 3. Model Training
Step3: 3.2 TFRecords Input Function
Step4: 3.3 Create feature columns
St... |
14,462 | <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
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<USER_TASK:>
Description:
Step1: Data validation using TFX Pipeline and TensorFlow Data Validation
Step2: Install TFX
Step3: Did you restart the runtime?
Step4: Set up variab... |
14,463 | <ASSISTANT_TASK:>
Python Code:
# Useful starting lines
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
%load_ext autoreload
%autoreload 2
1
x = [2,3,4]
def my_function(l):
l.append(12)
my_function(x)
x
# Matplotlib is used for plotting, plots are directly embedded in the
# notebook thanks to ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Notebook Basics
Step2: Numpy Basics
Step3: Creation of arrays
Step4: ndarray basics
Step5: Basic operators are working element-wise (+, -, *... |
14,464 | <ASSISTANT_TASK:>
Python Code:
# Authors: Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)
import numpy as np
from scipy import stats
from functools import partial
import matplotlib.pyplot as plt
# this changes hidden MPL vars:
from mpl_toolkits.mplot3d import Axes3D # noqa
from mne.stats import (spatio... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Set parameters
Step2: Construct simulated data
Step3: Do some statistics
Step4: Now let's do some clustering using the standard method.
Step5... |
14,465 | <ASSISTANT_TASK:>
Python Code:
help('learning_lab.03_management_interface')
from importlib import import_module
script = import_module('learning_lab.03_management_interface')
from inspect import getsource
print(getsource(script.main))
print(getsource(script.demonstrate))
run ../learning_lab/03_management_interface.py... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Implementation
Step2: Execution
Step3: HTTP
|
14,466 | <ASSISTANT_TASK:>
Python Code:
# For using the same code in either Python 2 or 3
from __future__ import print_function
## Note: Python 2 users, use raw_input() to get player input. Python 3 users, use input()
from IPython.display import clear_output
def display_board(board):
clear_output()
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: Step 1
Step2: Step 2
Step3: Step 3
Step4: Step 4
Step5: Step 5
Step6: Step 6
Step7: Step 7
Step8: Step 8
Step9: Step 9
Step10: Step 10
|
14,467 | <ASSISTANT_TASK:>
Python Code:
import sys
python_version = sys.version_info[0]
print("Python Version: ", python_version)
!pip3 install witwidget
import numpy as np
import pandas as pd
import witwidget
from witwidget.notebook.visualization import WitConfigBuilder, WitWidget
# Download our Pandas dataframe and our test ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Loading the mortgage test dataset
Step2: Preview the Features
Step3: Load the test features and labels into numpy arrays
Step4: Let's take a ... |
14,468 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nasa-giss', 'giss-e2-1g', 'atmos')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name",... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
14,469 | <ASSISTANT_TASK:>
Python Code:
from theano.sandbox import cuda
%matplotlib inline
from imp import reload
import utils; reload(utils)
from utils import *
from __future__ import division, print_function
#path = "data/dogscats/sample/"
path = "data/dogscats/"
model_path = path + 'models/'
if not os.path.exists(model_path... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Are we underfitting?
Step2: ...and load our fine-tuned weights.
Step3: We're going to be training a number of iterations without dropout, so i... |
14,470 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import oandapy
import configparser
%matplotlib inline
import seaborn as sns
import matplotlib.pyplot as plt
config = configparser.ConfigParser()
config.read('../config/config_v1.ini')
account_id = config['oanda']['account_id']
api_key = config['oanda... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Experiment With the Training Data Set
Step2: Vectorized Backtesting With the Test Set - Momentum
Step3: Vectorized Backtesting With the Test S... |
14,471 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
def bayes_table(hypos, prior, likelihood):
Make a table showing a Bayesian update.
table = pd.DataFrame(dict(prior=prior, likelihood=likelihood), index=hypos)
table['unnorm'] = table['prior'] * table['likelihood']
prob_data = table['unnorm'].sum()
t... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Flipping USB Connectors
Step3: Now suppose that the prior probability is 0.5 that the orientation of the connector is correct, and you have bee... |
14,472 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nims-kma', 'sandbox-1', 'ocean')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
14,473 | <ASSISTANT_TASK:>
Python Code:
mu = [2, 3]
cov = [[1, 0], [0, 1]]
rv = sp.stats.multivariate_normal(mu, cov)
xx = np.linspace(0, 4, 120)
yy = np.linspace(1, 5, 150)
XX, YY = np.meshgrid(xx, yy)
plt.grid(False)
plt.contourf(XX, YY, rv.pdf(np.dstack([XX, YY])))
plt.axis("equal")
plt.show()
mu = [2, 3]
cov = [[2, -1],[2,... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 경우 2
|
14,474 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from ecell4.prelude import *
D = 1
radius = 0.005
N_A = 60
U = 0.5
ka_factor = 0.1 # 0.1 is for reaction-limited
N = 20 # a number of samples
import numpy
kD = 4 * numpy.pi * (radius * 2) * (D * 2)
ka = kD * ka_factor
kd = ka * N_A * U * U / (1 - U)
kon = ka * kD / ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Parameters are given as follows. D, radius, N_A, U, and ka_factor mean a diffusion constant, a radius of molecules, an initial number of molecul... |
14,475 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from __future__ import print_function
import os
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
PROJ_ROOT = os.path.join(os.pardir, os.pardir)
## Try adding parameter index=0
pump_data_path = os.path.join(PROJ_ROOT,
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: 3.1 No more docs-guessing
Step3: 3.2 No more copy-pasta
Step4: 3.3 No more copy-pasta between notebooks
Step5: Restart the kernel, let's try ... |
14,476 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import display, HTML
display(HTML('''<img src="image1.png",width=800,height=600>'''))
import numpy as np # numerical libraries
import pandas as pd # for data analysis
import matplotlib as mpl # a big library with plotting functionality
import matplotlib.pyplot as plt... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Description
Step2: Load data and take a peak at it.
Step3: Separate data into training, validation, and test sets. (This division is not used ... |
14,477 | <ASSISTANT_TASK:>
Python Code:
import psycopg2
from configparser import ConfigParser
from pandas import DataFrame
from collections import Counter
cfg = ConfigParser()
cfg.read("db.cfg")
knst = psycopg2.connect(host=cfg['db']['host'], port=cfg['db']['port'],
database=cfg['db']['db'], user=cfg['d... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: We moeten verbinding maken met de databank.
Step3: De SQL om de gegevens op te halen is niet zo moeilijk
Step4: Even de gegevens binnenhalen e... |
14,478 | <ASSISTANT_TASK:>
Python Code:
# Initialization
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
%matplotlib inline
plt.style.use('fivethirtyeight')
def logprofile(z,ust):
''' Return u as function of z(array) and u_star
Uses Charnock relation for wind-wave interactio... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Basic power estimate
Step2: With this simple set-up it is easy to see that a small difference in wind speed translates to a large difference in... |
14,479 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import openpathsampling as paths
import openpathsampling.engines.openmm as peng_omm
from simtk.openmm import app
import simtk.openmm as mm
import simtk.unit as unit
from openmmtools.integrators import VVVRIntegrator
import mdtraj as md
im... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
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Description:
Step1: Setting up the engine
Step2: The storage file will need a template snapshot. In addition, the OPS OpenMM-based Engine has a few properties and ... |
14,480 | <ASSISTANT_TASK:>
Python Code:
Image('./res/fig3_1.png')
# Transition Graph
Image('./res/ex3_3.png')
# Example 3.5
from scipy.signal import convolve2d
reward_matrix = np.zeros((5, 5))
# kernel
kernel = np.array([[0, 1, 0],
[1, 0, 1],
[0, 1, 0]])
iteration_nums = 100
for _ in rang... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
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Description:
Step1: finite MDP
Step2: Exercise 3.4
|
14,481 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
# You can ignore the pink warning that appears
import itertools
import math
import nltk
import string
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
from scipy.spatial.distance import pdist, squareform
from... | <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: TF-IDF (Term Frequency, Inverse Document Frequency)
Step2: What if you change some of those docs, or add another one? Add another c in the last... |
14,482 | <ASSISTANT_TASK:>
Python Code:
from myhdl import *
from myhdlpeek import Peeker
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from sympy import *
init_printing()
import random
#https://github.com/jrjohansson/version_information
%load_ext version_information
%version_informati... | <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: RTL and Implimentation Schamatics are from Xilinx Vivado 2016.1
Step2: And if we try writing to the tuple we will get an error
Step5: Random a... |
14,483 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
%matplotlib inline
from Schelling import SchellingModel
model = SchellingModel(20, 20, 0.85, 0.2, 3)
while model.running and model.schedule.steps < 100:
model.step()
print(model.schedule.steps) # Show how many steps have actually run
model_out = model.... | <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 we instantiate a model instance
Step2: Effect of Homophily on segregation
Step3: Now, we set up the batch run, with a dictionary of fixed ... |
14,484 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import nltk
import string
import matplotlib.pyplot as plt
#read in our data
df = pd.read_csv("../Data/childrens_lit.csv.bz2", sep = '\t', encoding = 'utf-8', compression = 'bz2', index_col=0)
df = df.dropna(subset=["text"])
df
import numpy as np
np.random.seed(1)
df =... | <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: Since the number of children literaturs is a lot to analyze, we'll just randomly select 5 books to do a sentiment analysis using the dictionary ... |
14,485 | <ASSISTANT_TASK:>
Python Code:
class AlarmSensor:
def run(self):
print ("Alarm Ring...")
class WaterSprinker:
def run(self):
print ("Spray Water...")
class EmergencyDialer:
def run(self):
print ("Dial 119...")
alarm_sensor = AlarmSensor()
water_sprinker = WaterSprinker()
emergency_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: 在业务中如果需要将三个部件启动，例如，如果有一个烟雾传感器，检测到了烟雾。在业务环境中需要做如下操作：
Step2: 但如果在多个业务场景中需要启动三个部件，怎么办？Ctrl+C加上Ctrl+V么？当然可以这样，但作为码农的基本修养之一，减少重复代码是应该会被很轻易想到的方法。这样，需... |
14,486 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%precision 2
vocabulary = ['see', 'spot', 'run']
num_terms = len(vocabulary)
num_topics = 2 # K
num_documents = 5 # M
mean_document_length = 5 # xi
term_dirichlet_parameter = 1 # beta
topic_dirichlet_parameter = 1 # alpha
from scipy.stats import dirichlet, poisson
fro... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
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Description:
Step1: Latent Dirichlet Allocation is a generative model for topic modeling. Given a collection of documents, an LDA inference algorithm attempts to de... |
14,487 | <ASSISTANT_TASK:>
Python Code:
# Set up feedback system
from learntools.core import binder
binder.bind(globals())
from learntools.ethics.ex4 import *
import pandas as pd
from sklearn.model_selection import train_test_split
# Load the data, separate features from target
data = pd.read_csv("../input/synthetic-credit-card... | <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 dataset contains, for each applicant
Step2: The confusion matrices above show how the model performs on some test data. We also print addit... |
14,488 | <ASSISTANT_TASK:>
Python Code:
import wasmfun
instructions = [('f64.const', 42),
('call', 'print_ln'),
('call', 'make_background_blue')]
m = wasmfun.Module(
wasmfun.Function('$main', params=[], returns=[], locals=[], instructions=instructions),
wasmfun.ImportedFuncion('... | <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: What is Web Assembly?
Step2: Instructions are packed into functions ...
Step3: Web Assembly modules have a compact binary format
Step5: Web A... |
14,489 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
from scipy.spatial.distance import cosine
# Data was already dlownloaded.
data = pd.read_csv('data/lastfm/lastfm-matrix-germany.csv')
# check out the data set you can do so using data.head():
data.head(6).ix[:,2:10]
#In item based collaborative filtering we do not car... | <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: Item Based Collaborative Filtering
Step2: Now we can start to look at filling in similarities. We will use Cosin Similarities. In Python, the S... |
14,490 | <ASSISTANT_TASK:>
Python Code:
# Author: Alexandre Barachant <alexandre.barachant@gmail.com>
# Jean-Remi King <jeanremi.king@gmail.com>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne import Epochs
from mne.decoding import SPoC
from mne.datasets.fieldtrip_cmc import data_path
fro... | <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: Plot the contributions to the detected components (i.e., the forward model)
|
14,491 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cams', 'sandbox-1', 'seaice')
# 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: 2... |
14,492 | <ASSISTANT_TASK:>
Python Code:
def number_to_words(n):
Given a number n between 1-1000 inclusive return a list of words for the number.
x = []
a = {1:'one',2:'two',3:'three',4:'four',5:'five',6:'six',7:'seven',8:'eight',9:'nine',10:'ten',
11:'eleven',12:'twelve',13:'thirteen',14:'fourteen',15:'fift... | <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: Project Euler
Step2: Now write a set of assert tests for your number_to_words function that verifies that it is working as expected.
Step4: No... |
14,493 | <ASSISTANT_TASK:>
Python Code:
#@title ### Install the Graph Nets library on this Colaboratory runtime { form-width: "60%", run: "auto"}
#@markdown <br>1. Connect to a local or hosted Colaboratory runtime by clicking the **Connect** button at the top-right.<br>2. Choose "Yes" below to install the Graph Nets library on... | <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: Install dependencies locally
Step2: Tutorial of the Graph Nets library
Step3: How to represent graphs as a graphs.GraphsTuple
Step4: Visualiz... |
14,494 | <ASSISTANT_TASK:>
Python Code:
from halomod import AngularCF
import halomod
halomod.__version__
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
acf = AngularCF(z=0.475, zmin=0.45, zmax=0.5)
plt.plot(acf.theta * 180/np.pi, acf.angular_corr_gal)
plt.xscale('log')
plt.yscale('log')
plt.xlabel(r"$\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: The problem illustrated
Step2: Note that this is pretty much 1000x times what Blake+ got, but does have a similar shape.
Step3: OK, this is aw... |
14,495 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
%matplotlib inline
from sherpa import data
from sherpa.astro import data as astrodata
from sherpa import plot
from sherpa.astro import plot as astroplot
x1 = np.asarray([100, 200, 600, 1200])
y1 = np.asarray([2000, 2100, 1400, 3050])
d1 = data.Data1D('oned', x1, y1)
pl... | <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: One dimensional data plots
Step2: We can have some fun with the plot options (these are a mixture of generic options, such as xlog, and ones sp... |
14,496 | <ASSISTANT_TASK:>
Python Code:
#$HIDE_INPUT$
import geopandas as gpd
import pandas as pd
# Load a GeoDataFrame containing regions in Ghana
regions = gpd.read_file("../input/geospatial-learn-course-data/ghana/ghana/Regions/Map_of_Regions_in_Ghana.shp")
print(regions.crs)
# Create a DataFrame with health facilities 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: Setting the CRS
Step2: How do you interpret that?
Step3: In the code cell above, to create a GeoDataFrame from a CSV file, we needed to use bo... |
14,497 | <ASSISTANT_TASK:>
Python Code:
from edward.models import Bernoulli, Beta, Empirical, Uniform
N = 100
def build_fair_dataset(N):
pheads = tf.constant(0.5)
c = Bernoulli(probs=pheads, sample_shape = N)
return sess.run([pheads, c])
def build_unfair_dataset(N):
pheads = tf.constant(0.05)
c = Bernoulli(p... | <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: inference
Step2: exact solution
Step3: RECURSIVE INFERENCE
Step4: approximate inference
Step5: MCMC
Step6: MCMC
Step7: variational infere... |
14,498 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from scipy.stats import logistic
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import Image # Esto es para desplegar imágenes en la libreta
def logistica(z):
Calcula la función logística para cada elemento de z
@param z: un ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step2: 1. Función logística, función de costo y gradiente de la función de costo
Step3: Para probar la función vamos a graficar la función logística e... |
14,499 | <ASSISTANT_TASK:>
Python Code:
% matplotlib inline
from __future__ import print_function
import nibabel as nib
from nilearn.image import resample_img
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import os
import os.path
# The following are a progress bar, these are not strictly necessary:
fro... | <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: Define the variables for this analysis.
Step2: Next define a function to take the average of an image inside a mask and return it
Step3: This... |
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