Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
|---|---|---|
4,300 | <ASSISTANT_TASK:>
Python Code:
solution = 0
N = 0
while N < 1000:
if (N % 3 == 0) or (N % 5 == 0):
solution = solution + N
N+=1
print solution
s=0
a=1
b=1
while b < 4E6:
if b % 2 ==0: s=s+b;
c=a+b
a=b
b=c
print s
n=600851475143 #13195
a=2
while not a > n:
if n%a:
a+=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: Q002
Step2: Q003
Step3: Q004
Step4: Q005
Step5: Q006
Step6: Q007
Step7: Q008
Step8: Q009
Step9: Q010
|
4,301 | <ASSISTANT_TASK:>
Python Code:
a = [1, 2, "Hi"] # Создать список и присвоить переменной `а` этот список
print(a[0], a[1], a[2]) # Обращение к элементам списка, индексация с нуля
b = list() # Создать пустой список
c = [] # Другой способ создать пустой список
a = [0] * 10
print(a... | <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: Можно создать список из однотипных элементов заданной длины
Step2: Доступ к элементам
Step3: Обратите внимание, что при выведении списка таким... |
4,302 | <ASSISTANT_TASK:>
Python Code:
import os.path as op
import numpy as np
import mne
data_path = mne.datasets.opm.data_path()
subject = 'OPM_sample'
subjects_dir = op.join(data_path, 'subjects')
raw_fname = op.join(data_path, 'MEG', 'OPM', 'OPM_SEF_raw.fif')
bem_fname = op.join(subjects_dir, subject, 'bem',
... | <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: Prepare data for localization
Step2: Examine our coordinate alignment for source localization and compute a
Step3: Perform dipole fitting
Step... |
4,303 | <ASSISTANT_TASK:>
Python Code:
numero = 78277485
a = numero % 1000; a
b = numero % 100; b
from mpmath import mp #Librería usada para modiuficar la precision decimal de PI
from math import sqrt
mp.dps = 1000 #Establecer la precisión que queremos
pi_1000 = mp.pi
print(pi_1000)
besima = str(pi_1000)[b+2]
print(besima)
a... | <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: Ejercicio 1
Step2: Convenimos que la posición decimal de las decimas está indicada con el 0.
Step3: 2. Calcula el conjunto de los divisores na... |
4,304 | <ASSISTANT_TASK:>
Python Code:
import graphlab
image_train = graphlab.SFrame('image_train_data/')
image_test = graphlab.SFrame('image_test_data/')
#deep_learning_model = graphlab.load_model('http://s3.amazonaws.com/GraphLab-Datasets/deeplearning/imagenet_model_iter45')
#image_train['deep_features'] = deep_learning_mo... | <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: Load the CIFAR-10 dataset
Step2: Computing deep features for our images
Step3: Train a nearest-neighbors model for retrieving images using dee... |
4,305 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-1', 'sandbox-2', 'landice')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor... | <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... |
4,306 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from numpy import linalg as LA
import itertools
A = np.random.rand(3,3)
M = A @ A.T
D, U = LA.eigh(M) # D is returned as a vector
print(M)
print()
M2 = U @ np.diag(D) @ np.conj(U.T) # Conjugation not always needed
print(M2)
M = np.random.rand(2, 3) + 1j * np.random.ra... | <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: Part 1
Step2: Singular value decomposition
Step5: MPS
Step6: We can compress the parity indicator into a MPS form by a sequence of SVDs, as e... |
4,307 | <ASSISTANT_TASK:>
Python Code:
from ipywidgets import interact, interactive, fixed
import ipywidgets as widgets
import rx
rx
from rx import Observable, Observer
help(Observer)
dir(Observer)
help(Observable)
class MyObserver(Observer):
def on_next(self, x):
print("Got: %s" % x)
def on_error(self, e... | <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: Concepts
Step2: Concepts
Step3: Reference
Step4: Filtering a Sequence
Step5: Interactive Rx - Filtering
Step6: Transforming a Sequence
Step... |
4,308 | <ASSISTANT_TASK:>
Python Code:
markdown_str = ("The tables in this section show the standard association metrics between "
"*observed* human scores and different types of machine scores. "
"These results are computed on the evaluation set. `raw_trim` scores "
"are truncat... | <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: Descriptive holistic score statistics
Step2: Association statistics
Step3: Confusion matrix
Step4: Distribution of human and machine scores
|
4,309 | <ASSISTANT_TASK:>
Python Code:
df = pd.DataFrame({'subject':['A', 'A', 'B', 'B'],
'treatment':['CH', 'DT', 'CH', 'DT'],
'concentration':range(4)},
columns=['subject', 'treatment', 'concentration'])
df
pivoted = df.pivot(index='subject', columns='treatment', valu... | <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: A better representation might be one where the different subjects are in rows, the applied treatments are in columns and outcomes are in the dat... |
4,310 | <ASSISTANT_TASK:>
Python Code:
from nilearn.plotting import plot_anat
%matplotlib inline
plot_anat('/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz', dim=-1);
%%bash
bet /data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz \
/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w_bet.nii.g... | <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: Example of interface
Step2: ... which yields the following
Step3: Using nipype, the equivalent is a bit more verbose
Step4: Now we can verify... |
4,311 | <ASSISTANT_TASK:>
Python Code:
import os
import sys
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install -U google-cloud-aiplatform $USER_FLAG
! pip3 install -U google-cloud-storage $USER_FLAG
if not os.getenv("IS_TESTING... | <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 the latest GA version of google-cloud-storage library as well.
Step2: Restart the kernel
Step3: Before you begin
Step4: Region
Step5:... |
4,312 | <ASSISTANT_TASK:>
Python Code:
from bigbang.archive import Archive
urls = [#"analytics",
"conferences",
"design",
"education",
"gendergap",
"historic",
"hot",
"ietf-privacy",
"ipython-dev",
"ipython-user",
"languages",
"maps-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: The above code reads in preprocessed email archive data. These mailing lists are from a variety of different sources
Step2: Now we have process... |
4,313 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
def gram_linear(x):
Compute Gram (kernel) matrix for a linear kernel.
Args:
x: A num_examples x num_features matrix of features.
Returns:
A num_examples x num_examples Gram matrix of examples.
return x.dot(x.T)
def gram_rbf(x, threshold=1.0):
Comput... | <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:
Step6: Demo code for "Similarity of Neural Network Representations Revisited"
Step7: Tutorial
Step8: Linear CKA can be computed either based on dot p... |
4,314 | <ASSISTANT_TASK:>
Python Code:
a=45
print(a)
b=25
print(b)
x="Hello Everybody!"
print(x)
Amiright=True
print(Amiright)
fl = 0.54
print(fl)
print(a)
print(b)
c=a+b
print(c)
c=a/b
print(c)
c=c+a
print(c)
#addition
print(2+2)
#division or quotient
print(42/42)
#multiplication
print(2*3)
#subtraction
print(2-8)
#expon... | <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: Okay, neat. Can I only make variables that hold numbers?
Step2: Wait a minute, the numerical variables above are only integers...can we use dec... |
4,315 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'sandbox-3', 'ocean')
# 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
<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... |
4,316 | <ASSISTANT_TASK:>
Python Code:
import h2o
from h2o.estimators.uplift_random_forest import H2OUpliftRandomForestEstimator
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.style as style
import pandas as pd
h2o.init(strict_version_check=False) # max_mem_size=10
control_name = "control"
treatmen... | <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: Load data
Step2: Prepare data
Step3: Import data to H2O
Step4: Train H2O UpliftDRF model
Step5: Predict and plot Uplift Score
Step6: Evalua... |
4,317 | <ASSISTANT_TASK:>
Python Code:
import os
import tensorflow.compat.v1 as tf
tf.disable_eager_execution()
import pprint
assert 'COLAB_TPU_ADDR' in os.environ, 'Did you forget to switch to TPU?'
tpu_address = 'grpc://' + os.environ['COLAB_TPU_ADDR']
with tf.Session(tpu_address) as sess:
devices = sess.list_devices()
ppr... | <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: Authentication
Step2: Check imports
Step3: Train and evaluate a GAN model on TPU using TF-GAN.
Step4: Download the data. TensorFlow Datsets w... |
4,318 | <ASSISTANT_TASK:>
Python Code:
%%bash
sudo pip freeze | grep google-cloud-bigquery==1.6.1 || \
sudo pip install google-cloud-bigquery==1.6.1
from google.cloud import bigquery
import pandas as pd
%%bash
export PROJECT=$(gcloud config list project --format "value(core.project)")
echo "Your current GCP Project Name is: ... | <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: Import necessary libraries.
Step2: Set environment variables so that we can use them throughout the entire lab. We will be using our project na... |
4,319 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License")
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The AS... | <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: FlatMap
Step2: Examples
Step3: <table align="left" style="margin-right
Step4: <table align="left" style="margin-right
Step5: <table align="l... |
4,320 | <ASSISTANT_TASK:>
Python Code:
# the output of plotting commands is displayed inline within frontends,
# directly below the code cell that produced it
%matplotlib inline
from __future__ import print_function
# this python library provides generic shallow (copy) and deep copy (deepcopy) operations
from copy import dee... | <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: Setting input parameters
Step2: Creating SASE3 lattice
Step3: Load beam file
Step4: Plot beamfile
Step5: Match beam file
Step6: Tapering th... |
4,321 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function, division
from thinkbayes2 import Pmf, Suite
from thinkbayes2 import MakeMixture
from fractions import Fraction
d1 = Pmf({'Red':Fraction(2), 'Blue':Fraction(4)}, label='d1 (bluish) ')
d1.Print()
d2 = Pmf({'Red':Fraction(4), 'Blue':Fraction(2)}, label... | <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: A dice problem
Step2: And I'll make another Pmf to represent the random choice of one die or the other.
Step3: Now I can use the Random method... |
4,322 | <ASSISTANT_TASK:>
Python Code:
import os
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import sys
% matplotlib notebook
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data(path="mnist.npz")
x_train.shape
y_train.shape
class GAN:
def __init__(self, n... | <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 Set
Step2: Verify data strucutre
Step3: Define model
Step4: Specify Dimensions
Step5: Create GAN model, define I/O , loss function... |
4,323 | <ASSISTANT_TASK:>
Python Code:
2+2
print 2*3
print (4+6)*(2+9) # should calculate to 110
print 12.0/11.0
print(5/3) # Integer division gives a 'floor' value (rounding down, basically).
print(5.0/3.0) # Dividing floats (usually) gives the expected answer.
print(5.0/3) # The interpreter uses the more complex typ... | <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: Another several examples
Step2: One major difference between using a calculator and doing calculations on the computer is that there are a coup... |
4,324 | <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 = (0, 10)
DON'T MODIFY AN... | <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: Language Translation
Step3: Explore the Data
Step6: Implement Preprocessing Function
Step8: Preprocess all the data and save it
Step10: Chec... |
4,325 | <ASSISTANT_TASK:>
Python Code:
def gen():
for i in range(10):
yield i
dataset = tf.data.Dataset.from_generator(gen, tf.float32)\
.make_one_shot_iterator()\
.get_next()
with tf.Session() as sess:
_data = sess.run(dataset)
print(_data)
with tf.Session() as sess:
for _ in range(10):... | <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: generator로 label, feature까지 출력하고 싶다면
Step2: Minibatch를 하고 싶다면
Step3: (2) TextLineDataset
Step4: b'1,1,2,3,4,5,6,7,8,9'
Step5: Modeling
Step... |
4,326 | <ASSISTANT_TASK:>
Python Code:
# 检查你的Python版本
from sys import version_info
if version_info.major != 2 and version_info.minor != 7:
raise Exception('请使用Python 2.7来完成此项目')
import numpy as np
import pandas as pd
# 数据可视化代码
from titanic_visualizations import survival_stats
from IPython.display import display
%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: 从泰坦尼克号的数据样本中，我们可以看到船上每位旅客的特征
Step3: 这个例子展示了如何将泰坦尼克号的 Survived 数据从 DataFrame 移除。注意到 data（乘客数据）和 outcomes （是否存活）现在已经匹配好。这意味着对于任何乘客的 data.loc[i] 都... |
4,327 | <ASSISTANT_TASK:>
Python Code:
%%bash
export PROJECT=$(gcloud config list project --format "value(core.project)")
echo "Your current GCP Project Name is: "$PROJECT
%%bash
# Create a BigQuery dataset for feat_eng if it doesn't exist
datasetexists=$(bq ls -d | grep -w feat_eng)
if [ -n "$datasetexists" ]; then
echo ... | <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 source dataset
Step2: Create the training data table
Step3: Verify table creation
Step4: Baseline Model
Step5: Note, the query takes sev... |
4,328 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division
import pandas as pd
import numpy as np
import scipy as sp
import scipy.stats
import toyplot as tp
df = pd.read_csv('free_throws.csv', names=["away", "home", "team", "player", "score"])
df["at_home"] = df["home"] == df["team"]
df.head()
df.groupby("at_home... | <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 munging
Step2: Overall free throw%
Step3: Aggregating to player level
Step4: Individual tests
Step5: Global tests
Step6: Bonferroni's ... |
4,329 | <ASSISTANT_TASK:>
Python Code:
!git clone https://github.com/markriedl/easygen.git
!cp easygen/*.js /usr/local/share/jupyter/nbextensions/google.colab/
!cp easygen/images/*.png /usr/local/share/jupyter/nbextensions/google.colab/
!apt-get update
!apt-get install chromium-chromedriver
!pip install -r easygen/requirement... | <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: Install requirements
Step2: Download StyleGAN
Step3: Download GPT-2
Step4: Create backend hooks for saving and loading programs
Step5: Impor... |
4,330 | <ASSISTANT_TASK:>
Python Code:
for researcher in researchers:
if 'Wil' in researcher.name:
print 'ID:{}, name:{}'.format(researcher.id, researcher.name)
print 'pre-filter:', query_list[-1].count()
query_list.append(query_list[-1].exclude(researcher_id=2))
print 'post-filter:', query_list[-1].count()
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: Next, let's only keep the pleco tags
Step2: Now let's keep only the tags from January of 2015
Step3: Finally, we name the latest query export_... |
4,331 | <ASSISTANT_TASK:>
Python Code:
# change these to try this notebook out
BUCKET = 'cloud-training-demos-ml'
PROJECT = 'cloud-training-demos'
REGION = 'us-central1'
import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT
os.environ['REGION'] = REGION
os.environ['TFVERSION'] = '1.14'
import tensorflow as tf... | <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: We will look at the titles of articles and figure out whether the article came from the New York Times, TechCrunch or GitHub.
Step4: Let's do ... |
4,332 | <ASSISTANT_TASK:>
Python Code:
x, fs = librosa.load('simpleLoop.wav', sr=44100)
print x.shape
librosa.display.waveplot(x, fs)
from IPython.display import Audio
Audio(x, rate=fs)
onsets = librosa.onset.onset_detect(x, fs)
print onsets # frame numbers of estimated onsets
S = librosa.stft(x)
logS = librosa.logamplitud... | <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 signal
Step2: Listen
Step3: librosa.onset.onset_detect
Step4: Plot the onsets on top of a spectrogram of the audio
Step5: essentia.... |
4,333 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
# YOUR CODE HERE
def hat(x,a,b):
v=-1*a*x**2+b*x**4
return v
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(1.0, 10.0, 1.0)==-9.0
x=np.linspace(-3,3)
b=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: Hat potential
Step2: Plot this function over the range $x\in\left[-3,3\right]$ with $b=1.0$ and $a=5.0$
Step3: Write code that finds the two l... |
4,334 | <ASSISTANT_TASK:>
Python Code:
fifteen_factorial = 15*14*13*12*11*10*9*8*7*6*5*4*3*2*1
print(fifteen_factorial)
import math
print(math.factorial(15))
print("Result correct?", math.factorial(15) == fifteen_factorial)
print(math.factorial(5), math.sqrt(2*math.pi)*5**(5+0.5)*math.exp(-5))
print(math.factorial(10), math.... | <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: Exercise 2
Step2: Exercise 3
Step4: We see that the relative error decreases, whilst the absolute error grows (significantly).
Step6: In late... |
4,335 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
from scipy.optimize import fsolve
from scipy import integrate
import matplotlib.pyplot as plt
from clawpack import pyclaw
from clawpack import riemann
from clawpack.visclaw.ianimate import ianimate
import matplotlib
plt.style.use('seaborn-talk')
from ... | <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: Our next example is something you can experiment with in your own home. Go to the kitchen sink, position the faucet over a flat part of the sin... |
4,336 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
%matplotlib inline
#export
from exp.nb_10 import *
path = datasets.untar_data(datasets.URLs.IMAGENETTE_160)
tfms = [make_rgb, ResizeFixed(128), to_byte_tensor, to_float_tensor]
bs = 64
il = ImageList.from_files(path, tfms=tfms)
sd = SplitData.split_by_fu... | <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: Mixup
Step2: French horn or tench? The right answer is 70% french horn and 30% tench ;)
Step3: NB
Step4: In the original article, the authors... |
4,337 | <ASSISTANT_TASK:>
Python Code:
data_in_shape = (4, 4, 4, 2)
L = MaxPooling3D(pool_size=(2, 2, 2), strides=None, padding='valid', data_format='channels_last')
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
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: [pooling.MaxPooling3D.1] input 4x4x4x2, pool_size=(2, 2, 2), strides=(1, 1, 1), padding='valid', data_format='channels_last'
Step2: [pooling.Ma... |
4,338 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import skrf as rf
rf.stylely()
# imports
from scipy.constants import mil,c
from skrf.media import RectangularWaveguide, Freespace
from skrf.frequency import Frequency
import matplotlib as mpl
# plot formating
mpl.rcParams['lines.linewidth'] = 2
# create frequency obje... | <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: Conductor Loss
Step2: Phase Velocity
Step3: Propagation Constant
|
4,339 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from emo_utils import *
import emoji
import matplotlib.pyplot as plt
%matplotlib inline
X_train, Y_train = read_csv('data/train_emoji.csv')
X_test, Y_test = read_csv('data/tesss.csv')
maxLen = len(max(X_train, key=len).split())
index = 1
print(X_train[index], label_to... | <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: 1 - Baseline model
Step2: Run the following cell to print sentences from X_train and corresponding labels from Y_train. Change index to see dif... |
4,340 | <ASSISTANT_TASK:>
Python Code:
import cvxpy as cp
x = cp.Variable(pos=True)
y = cp.Variable(pos=True)
z = cp.Variable(pos=True)
a = cp.Parameter(pos=True)
b = cp.Parameter(pos=True)
c = cp.Parameter()
objective_fn = 1/(x*y*z)
objective = cp.Minimize(objective_fn)
constraints = [a*(x*y + x*z + y*z) <= b, x >= y**c]
prob... | <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: Notice the keyword argument dpp=True. The parameters must enter in the DGP problem acording to special rules, which we refer to as dpp. The DPP ... |
4,341 | <ASSISTANT_TASK:>
Python Code:
import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
with open('anna.txt', 'r') as f:
text=f.read()
vocab = sorted(set(text))
vocab_to_int = {c: i for i, c in enumerate(vocab)}
int_to_vocab = dict(enumerate(vocab))
encoded = np.array([vocab_to_int... | <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: First we'll load the text file and convert it into integers for our network to use. Here I'm creating a couple dictionaries to convert the chara... |
4,342 | <ASSISTANT_TASK:>
Python Code:
# dogscats/
# train/
# dogs/
# cats/
# valid/
# dogs/
# cats/
# test/
# dogs/
# cats/
# sample/
# <same as for dogscats/>
# Making sure python knows where to look for my utilities folder (it's in this notebook's paren... | <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: Setup matplotlib to plot inside the notebook, import NumPy, SciPy, VGG, and the appropriate Keras libraries.
Step2: Vgg16 Image Recognition Exa... |
4,343 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
import os
import tempfile
import pandas as pd
import tensorflow as tf
import tensorflow_transform as tft
from tensorflow_transform import beam as tft_beam
import tfx_utils
from tfx.utils import io_utils
from tensorflow_metadata.proto.v0 import schema_... | <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: Get the schema URI from the metadata store
Step2: Get the schema that was inferred by TensorFlow Data Validation
Step6: Define features and cr... |
4,344 | <ASSISTANT_TASK:>
Python Code:
## Set up the environment
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
%matplotlib inline
## Load the data set
titanic_df = pd.read_csv('titanic_data.csv')
## Let's see what the data looks like
## First let's see what the columns are.
titan... | <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: Initial Inspection of the Data
Step2: An initial inspection of the data shows us that we have data for 891 passengers not all of whose ages are... |
4,345 | <ASSISTANT_TASK:>
Python Code:
class Student:
count = 0 # Total number of objects created so far, it is static variable as it is declared outside
def __init__(self,name,usn,marks):
Constructor of class Student
Input: name - name of the student : string
usn - univers... | <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: Object Oriented Programming
Step2: Duck Typing and Interfaces
Step3: type() - Obtaining the data type of a variable
|
4,346 | <ASSISTANT_TASK:>
Python Code:
%matplotlib notebook
import pandas as pd
import sys
sys.path.append("../../../bayesianpy")
import bayesianpy
from bayesianpy.network import Builder as builder
import logging
import os
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
# Using the lat... | <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: Next, just a bit of setup code to load the data and setup the Jpype instance.
Step2: Naive Bayes
Step3: The network still needs training, so k... |
4,347 | <ASSISTANT_TASK:>
Python Code:
# Authors: Teon Brooks <teon.brooks@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
from mne.report import Report
from mne.datasets import sample
from mne import read_evokeds
from matplotlib import pyplot as plt
data_path = sample.data_path()
meg_pa... | <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: Do standard folder parsing (this can take a couple of minutes)
Step2: Add a custom section with an evoked slider
|
4,348 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
#Read files:
train = pd.read_csv("train.csv")
test = pd.read_csv("test.csv")
#Combine test and train into one file
train['source']='train'
test['source']='test'
data = pd.concat([train, test],ignore_index=True)
print train.shape, test.shape, data.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: Loading data
Step2: 2. Data Cleaning
Step3: 2. Feature Engineering
Step4: Step2
Step5: Step 3
Step6: Step 4
Step7: Step 5
Step8: Step 6
S... |
4,349 | <ASSISTANT_TASK:>
Python Code:
import time
import numpy as np
import tensorflow as tf
import utils
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import zipfile
dataset_folder_path = 'data'
dataset_filename = 'text8.zip'
dataset_name = 'Text8 Dataset'
class DLProgress(tq... | <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 the text8 dataset, a file of cleaned up Wikipedia articles from Matt Mahoney. The next cell will download the data set to the data folder. ... |
4,350 | <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: TensorFlow Lattice を使った倫理のための形状制約
Step2: 必要なパッケージをインポートします。
Step3: このチュートリアルで使用されるデフォルト値です。
Step4: 事例 1
Step5: データセットを前処理します。
Step7: データをトレ... |
4,351 | <ASSISTANT_TASK:>
Python Code:
# import data from url
from py2cytoscape.data.cyrest_client import CyRestClient
from IPython.display import Image
# Create REST client for Cytoscape
cy = CyRestClient()
# Reset current session for fresh start
cy.session.delete()
# Load a sample network
network = cy.network.create_from('ht... | <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: Global Network Analysis
Step2: Density
Step3: Transitivity
Step4: community detection
Step5: Node Analysis
Step6: Degree
Step7: PageRank
S... |
4,352 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pylab as plt
import convis
inp, out = convis.samples.generate_sample_data(input='random',size=(2000,20,20))
print(inp.shape)
m = convis.models.LN(kernel_dim=(12,7,7),population=True)
print(m.compute_loss(inp,out,dt=500))
m.set_opt... | <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: Then, you need to choose a model, eg. an LN model as well.
Step2: We can have a look at how well our model is doing right now by looking
Step3:... |
4,353 | <ASSISTANT_TASK:>
Python Code:
# Install additional packages
!pip install -q matplotlib-venn
# Import all necessary libs
import json
import sys
import argparse
import pprint
import random
import datetime
import pandas as pd
from google_auth_oauthlib.flow import InstalledAppFlow
from googleapiclient import discovery
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: API Configuration
Step9: API Authentication - OAuth2.0 Flow
Step10: Actual Request to the Ads Data Hub Service API
Step11: Analysis 1
Step12:... |
4,354 | <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: TF-Hub CORD-19 Swivel 埋め込みを探索する
Step2: 埋め込みを分析する
Step5: 埋め込みが異なる用語の意味をうまく捉えていることが分かります。それぞれの単語は所属するクラスタの他の単語に類似していますが（「コロナウイルス」は「SARS」や「MERS」と... |
4,355 | <ASSISTANT_TASK:>
Python Code:
popSize = 4 # This is the number of nodes in the network
update = 'BD' # Either 'BD' or 'DB' for Birth-death or death-Birth updating respectively
direction = 'undirected' # Either 'directed' or 'undirected' graphs are used
stepSize = 0.05 # Step size for the probability for each link in t... | <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's create the graphs, calculate the fixation probability and store it in one file per graph in the folder output.
Step2: Classify graphs... |
4,356 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'sandbox-2', 'atmoschem')
# 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
<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... |
4,357 | <ASSISTANT_TASK:>
Python Code:
import turicreate as tc
sf = tc.SFrame.read_csv("/Users/datalab/bigdata/cjc/w15",
header=False)
sf
dir(sf['X1'])
bow = sf['X1']._count_words()
type(sf['X1'])
type(bow)
bow.dict_has_any_keys(['limited'])
bow.dict_values()[0][:20]
sf
sf['bow'] = bow
sf
typ... | <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 Data
Step2: Transformations
Step3: Text cleaning
Step4: Topic modeling
Step5: pred = m.predict(another_data)
Step6: Seeding the m... |
4,358 | <ASSISTANT_TASK:>
Python Code:
# Author: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne.datasets import sample
import matplotlib.pyplot as plt
print(__doc__)
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
fwd_fname = data_path + '/MEG/sa... | <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: Compute sensitivity maps
Step2: Show gain matrix a.k.a. leadfield matrix with sensitivity map
|
4,359 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import math
v = np.array([2,1])
w = 2 * v
print(w)
# Plot w
origin = [0], [0]
plt.grid()
plt.ticklabel_format(style='sci', axis='both', scilimits=(0,0))
plt.quiver(*origin, *w, scale=10)
plt.show()
b = v / 2
print(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:
Step1: The same approach is taken for scalar division.
Step2: Dot Product Multiplication
Step3: In Python 3.5 and later, you can also use the @ opera... |
4,360 | <ASSISTANT_TASK:>
Python Code:
# your code here
current_users = ["lira", "sarah", "james", "nicole", "AJ"]
new_users = ["lira", "sarah", "li", "chidi", "olympia"]
for i in current_users:
if i in new_users:
print("You need to enter a new username")
else:
print("This username is available")
# your code here
... | <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. Color probability
Step2: 3. Write an if-elif-else chain that determines a person’s stage of life.
Step3: 4. process_data
Step4: 5. user_co... |
4,361 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import math
from scipy import signal
from scipy import stats
# check Parseval's theorem holds numerically
nsamps=1000
# window
w = signal.tukey(nsamps,0.1)
a = np.random.normal(0,1,nsamps) * w
A = np.fft.fft(a)
b = (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: Parsevals theorem when applied to discrete Fourier Transform looks like this.
Step2: Furthermore by the convolution theorem
Step5: Parsevals t... |
4,362 | <ASSISTANT_TASK:>
Python Code:
from google.cloud import aiplatform as vertex_ai
!python -c "import tensorflow as tf; print(f'TF version: {tf.__version__}')"
!python -c "import tfx; print(f'TFX version: {tfx.__version__}')"
!python -c "import kfp; print(f'KFP version: {kfp.__version__}')"
print(f"vertex_ai: {vertex_ai.... | <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: Validate lab package version installation
Step2: Note
Step3: The config.py module configures the default values for the environment specific s... |
4,363 | <ASSISTANT_TASK:>
Python Code:
from games import (GameState, Game, Fig52Game, TicTacToe, query_player, random_player,
alphabeta_player, play_game, minimax_decision, alphabeta_full_search,
alphabeta_search, Canvas_TicTacToe)
%psource Game
%psource TicTacToe
game52 = Fig52Game(... | <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: GameState namedtuple
Step2: Now let's get into details of all the methods in our Game class. You have to implement these methods when you creat... |
4,364 | <ASSISTANT_TASK:>
Python Code:
from BioTechTopics import Topics
from plotBokehJpnb2 import plotBokehInJpnb
import time
# make instance of Topics object and load the data
t=Topics()
t.load() # unpickles LDA, tf, and tf-idf representations, puts text data from JSON into pandas dataframe
#plotBokehInJpnb(t,'antibody')
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: Section 1
Step2: Fast named entity return is possible because all NLP (TextRank, tfidf, named entity recognition) is done offline and stored in... |
4,365 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'csir-csiro', 'sandbox-2', 'landice')
# 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
<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... |
4,366 | <ASSISTANT_TASK:>
Python Code:
%%bash
pushd /workspace/nvidia-examples/tensorrt/tftrt/examples/object_detection/
bash ../helper_scripts/install_pycocotools.sh;
popd
import os
os.environ['CUDA_VISIBLE_DEVICES']='0'
import time
import logging
import numpy as np
import tensorflow as tf
print("TensorFlow version: ", tf.__... | <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: TF saved model
Step3: Helper functions
Step4: <a id="2"></a>
Step5: We employ saved_model_cli to inspect the inputs and outputs ... |
4,367 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
import mxnet as mx
from mxnet import nd, autograd
import numpy as np
from collections import defaultdict
mx.random.seed(1)
# ctx = mx.gpu(0)
ctx = mx.cpu(0)
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
imp... | <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: Dataset
Step3: Check the data real quick
Step4: Preparing the data for training
Step5: Long short-term memory (LSTM) RNNs
Step6: Attach the ... |
4,368 | <ASSISTANT_TASK:>
Python Code:
import harness
from harness import Harness
from pandas import Categorical
from sklearn import datasets, discriminant_analysis
iris = datasets.load_iris()
# Harness is just a dataframe
df = Harness(
data=iris['data'], index=Categorical(iris['target']),
estimator=discriminant_analys... | <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: More Examples
Step2: Build & Run Tests
|
4,369 | <ASSISTANT_TASK:>
Python Code:
# Import Pandas & NumPy
import pandas as pd
import numpy as np
# Create a tiny dataset, as a list of tuples
name = ('Oslo','Copenhaguen','Helsinki','Stockholm','Reykjavik')
pop = ( 647676, 583348, 626305, 917297, 121822 )
area = ( 480.76, 86.20, 715.49, 188.0, 273 )
data = [ (1000+i,n,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: 1 Creating a DataFrame
Step2: Let's view the dataframe. We can print it
Step3: Or we can just show it, and it will be nicely formatted.
Step4:... |
4,370 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns
import numpy as np
s=[]
i=0
def find_peaks(a):
Find the indices of the local maxima in a sequence.
# YOUR CODE HERE
if a[0]>a[1]: #if the first number is bigger than the second number
s.app... | <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: Peak finding
Step3: Here is a string with the first 10000 digits of $\pi$ (after the decimal). Write code to perform the following
|
4,371 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('ggplot')
train = pd.read_csv("data/trainRF.csv")
test = pd.read_csv("data/testRF.csv")
train_13 = train.drop(['day','month', 'duration'], axis = 1)
test_13 = test.drop(['day','month', ... | <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: Implementing Random Forest
Step2: Key input parameters (in addition to decision trees)
Step3: Out-of-Bag Error
|
4,372 | <ASSISTANT_TASK:>
Python Code:
import sys
import os
from itertools import count
from pathlib import Path
sys.path.insert(0, str(Path(os.environ['HOME'], 'git', 'skanb', 'pea-test-set')))
import utils as asvt_utils
import numpy as np
import matplotlib.pyplot as plt
from astropy.table import Table, vstack
from astropy.ti... | <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: Get acq stats data and clean
Step2: Get ASVT data and make it look more like acq stats data
Step3: Combine flight acqs and ASVT data
Step4: C... |
4,373 | <ASSISTANT_TASK:>
Python Code:
import phoebe
b = phoebe.default_binary()
b.add_dataset('lc', dataset='lc01')
print(b.filter(qualifier='pblum*', dataset='lc01'))
print(b.get_parameter('pblum_mode'))
print(b.get_parameter('pblum_component'))
b.set_value('pblum_component', 'secondary')
print(b.filter(qualifier='pblum*', ... | <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: In the default mode, you can change which of the stars you'd like to provide the luminosity. By default, this is the primary component. To pro... |
4,374 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from pymc3 import *
import numpy as np
import matplotlib.pyplot as plt
size = 200
true_intercept = 1
true_slope = 2
x = np.linspace(0, 1, size)
# y = a + b*x
true_regression_line = true_intercept + true_slope * x
# add noise
y = true_regression_line + np.random.normal... | <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: Generating data
Step2: Estimating the model
Step3: This should be fairly readable for people who know probabilistic programming. However, woul... |
4,375 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
fngs_ts = np.load('/home/eric/cmp/fngs/outputs/ts_roi/pp264-2mm/sub-0025864_ses-1_bold_pp264-2mm.npy')
cpac_ts = np.load('/home/eric/cmp/cpac/pipeline_HCPtest/sub-0025864_ses-1/roi_timeseries/_scan_res... | <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: Things to Note
|
4,376 | <ASSISTANT_TASK:>
Python Code:
import sys
import os
import inspect
# add the path to opengrid to sys.path
script_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
sys.path.append(os.path.join(script_dir, os.pardir, os.pardir))
from opengrid.library import houseprint
from opengrid.library i... | <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: Create a tmpo session, and enter debug mode to get more output.
Step2: Add a sensor and token to start tracking the data for this given sensor.... |
4,377 | <ASSISTANT_TASK:>
Python Code:
from pylab import * # numpy, matplotlib, plt
from bregman.suite import * # Bregman audio feature extraction library
from soundscapeecology import * # 2D time-frequency shift-invariant convolutive matrix factorization
%matplotlib inline
rcParams['figure.figsize'] = (15.0, 9.0)
sound_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: <h2>Example audio</h2>
Step2: <h2>Spectrum Analysis Parameters</h2>
Step3: <h2>SoundscapeEcology Toolkit</h2>
|
4,378 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
from IPython.display import HTML
from ipywidgets import interact
HTML('../style/code_toggle.html')
def FS_coeffs(x, m, func, T=2.0*np.pi... | <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 section specific modules
Step3: 2.3. Fourier Series<a id='math
Step4: That should be good enough for our purposes here. Next we create ... |
4,379 | <ASSISTANT_TASK:>
Python Code:
from dx import *
import time
from pylab import plt
plt.style.use('seaborn')
%matplotlib inline
# constant short rate
r = constant_short_rate('r', 0.02)
# market environments
me_gbm = market_environment('gbm', dt.datetime(2015, 1, 1))
# geometric Brownian motion
me_gbm.add_constant('initi... | <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 Risk Factor
Step2: American Put Option
Step3: Large Portfolio
Step4: Sequential Valuation
Step5: The call of the get_values method to... |
4,380 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'hammoz-consortium', 'sandbox-2', 'atmos')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor(... | <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... |
4,381 | <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: 勾配ブースティング木
Step2: 特徴量の説明については、前のチュートリアルをご覧ください。
Step3: 入力パイプラインを構築する
Step4: モデルをトレーニングする
Step5: パフォーマンスの理由により、データがメモリに収まる場合は、tf.estimator.Bo... |
4,382 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
import numpy as np
np.random.seed(10)
a = tf.constant(np.random.rand(50, 100, 512))
def g(a):
return tf.expand_dims(a, 2)
result = g(a.__copy__())
<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:
|
4,383 | <ASSISTANT_TASK:>
Python Code:
print cobra.__version__
model_iEK = cobra.io.load_json_model("iEK1020.json")
print "# of reactions", len(model_iEK.reactions)
print "# of metabolites", len(model_iEK.metabolites)
print "# of genes", len(model_iEK.genes)
# for react in model_iEK_before.reactions:
# if react not in mod... | <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 you need to run this notebook...
Step2: Check default media condition
Step3: Check FBA solution
Step4: Check Mass Balance
Step5: Most o... |
4,384 | <ASSISTANT_TASK:>
Python Code:
import os
from datetime import datetime
import numpy as np
import pandas as pd
import sklearn as sk
from sklearn.externals import joblib
from sklearn.preprocessing import LabelEncoder
from sklearn.grid_search import GridSearchCV
import xgboost as xgb
def check_NaN_Values_in_df(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: 2. Re-Import the Check NaN Function from Part 2
Step2: 3. Loading in the Data from Part 3
Step3: 4. Getting a training dataset
Step4: 5. Trai... |
4,385 | <ASSISTANT_TASK:>
Python Code:
# built-in python modules
import os
import inspect
# scientific python add-ons
import numpy as np
import pandas as pd
# plotting stuff
# first line makes the plots appear in the notebook
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib as mpl
# finally, we import the ... | <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 TMY data
Step2: The file handling above looks complicated because we're trying to account for the many different ways that people will run... |
4,386 | <ASSISTANT_TASK:>
Python Code:
import os, sys
from matplotlib import pyplot as plt
%matplotlib inline
import time
import numpy as np
import pickle
import pandas
import gzip
sys.path.append("..")
import cca_core
def _plot_helper(arr, xlabel, ylabel):
plt.plot(arr, lw=2.0)
plt.xlabel(xlabel)
plt.ylabel(ylabel... | <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: In the introduction tutorial, we looked at a simple way of thinking of neural network representations (neurons as activation vectors and layers ... |
4,387 | <ASSISTANT_TASK:>
Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)
import os.path as op
import numpy as np
from scipy import stats as stats
import mne
from mne import spatial_src_connectivity
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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<USER_TASK:>
Description:
Step1: Set parameters
Step2: Compute statistic
Step3: Visualize the clusters
|
4,388 | <ASSISTANT_TASK:>
Python Code:
import statsmodels.formula.api as smf
import pandas as pd
import numpy as np
import scipy as sp
# Create some example data
cs= [12, 14, 16, 18] # classes of carbons
ds = [0, 1, 2, 3] # classes of double bonds
df = pd.DataFrame({'RT': np.random.uniform(low=0.1, high=15, size=1000), '... | <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 Example Datasets
Step2: Create OLS output
Step3: Access results attributes
Step4: There are two $R^2$ values that you can get from the... |
4,389 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division
import math
import rasterio
# This notebook explores a single 4 band (blue, green, red, NIR) PlanetScope scene in a UTM projection.
image_file = "example.tif"
satdat = rasterio.open(image_file)
# Minimum bounding box in projected units
print(satdat.bounds)... | <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 details
Step2: Bands
Step3: Because we know we're look at a PlanetScope 4-band analytic satellite image, we can define the bands by thei... |
4,390 | <ASSISTANT_TASK:>
Python Code:
def sort(L):
n = len(L)
if n < 2:
return L
L1, L2 = L[:n//2], L[n//2:]
return merge(sort(L1), sort(L2))
def merge(L1, L2):
if L1 == []:
return L2
if L2 == []:
return L1
x1, *R1 = L1
x2, *R2 = L2
if x1 <= x2:
return [x1] ... | <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 still need to specify how two sorted lists $L_1$ and $L_2$ are merged in a way that the resulting
Step2: Testing
Step3: We import the class... |
4,391 | <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: Simple TFX Pipeline for Vertex Pipelines
Step2: Restart the runtime
Step3: Check the package versions.
Step4: Set up variables
Step5: Set gc... |
4,392 | <ASSISTANT_TASK:>
Python Code:
using JuMP
m = Model()
# Definig variable
Classes = ["A", "B", "C"]
Shift = ["R", "O"]
@variable(m, x[Classes, Shift] >= 0)
# Define Constraints
@constraints m begin
2x["B","R"] + 3x["C","R"] <= 40
2x["B","O"] + 3x["C","O"] <= 35
3x["A","R"] + x["B","R"] + 3x["C","R"] + 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: PART A
Step2: Change Kernel to Python
|
4,393 | <ASSISTANT_TASK:>
Python Code:
#
# When the cursor is in this cell hit "shift enter" to execute the python code here
#
x=3 # x is assigned an integer value of 3
y=2.4 # y is assigned a floating point value of 2.4
print("x and y are:", x, 'and',y)
z=x+y
print("The value of z is:", z)
aList = [1,2,9,4,7]
print("W... | <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: Like most programming languages you can operate on these values using unary and binary operators, like so
Step2: Tuples and Lists
Step3: The d... |
4,394 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
%matplotlib inline
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
import shogun as sg
import numpy as np
# use scipy for generating samples
from scipy.stats import laplace, norm
def sample_gaussian_vs_laplace(n=220, mu=0.0, sigma2=1, b=np.sqrt(0.5))... | <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: Some Formal Basics (skip if you just want code examples)
Step2: Now how to compare these two sets of samples? Clearly, a t-test would be a bad ... |
4,395 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import numpy as np
from numpy import pi
%matplotlib inline
from pyqg import sqg_model
# create the model object
year = 1.
m = sqg_model.SQGModel(L=2.*pi,nx=512, tmax = 26.005,
beta = 0., Nb = 1., H = 1., rek = 0., rd = None, dt = 0.005,
... | <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: Surface quasi-geostrophy (SQG) is a relatively simple model that describes surface intensified flows due to buoyancy. One of it's advantages is ... |
4,396 | <ASSISTANT_TASK:>
Python Code:
# NBVAL_IGNORE_OUTPUT
import numpy as np
import matplotlib.pyplot as plot
import math as mt
import matplotlib.ticker as mticker
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib import cm
# NBVAL_... | <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: From Devito's library of examples we import the following structures
Step2: The mesh parameters that we choose define the domain $\Omega_{0}$ p... |
4,397 | <ASSISTANT_TASK:>
Python Code:
from dkrz_forms import form_widgets
form_widgets.show_status('form-generation')
from dkrz_forms import form_widgets
form_widgets.create_form()
from dkrz_forms import form_widgets
form_widgets.show_status('form-retrieval')
MY_LAST_NAME = "testsuite" # e.gl MY_LAST_NAME = "schulz"
#--... | <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 a data form
Step2: Retrieve your DKRZ data form
Step3: Please provide your last name
|
4,398 | <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.
_1={1:' ',2:"-"}
_9={1:"one",2:"two",3:"three",4:"four",5:'five',6:'six',7:'seven',8:"eight",9:'nine',0:'',11:'and'}
_100={0:'',1:'ten',2:"twenty",3:'thirty',4:'forty',... | <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... |
4,399 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'csiro-bom', 'access-1-0', '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
<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... |
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