Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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2,500 | <ASSISTANT_TASK:>
Python Code:
from collections import deque
queue = deque(["Eric", "John", "Michael"])
queue.append("Terry") # Terry 入队
queue.append("Graham") # Graham 入队
queue.pop() # 队尾元素出队
queue.popleft() # 队首元素出队
queue # 队列中剩下的元素
bas... | <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. Python的集合
Step2: 在我们的爬虫中, 只是用到其中的快速判断元素是否在集合内的功能, 以及集合的并运算.
Step3: re提供了众多模块方法用于完成正则表达式的功能。这些方法可以使用Pattern实例的相应方法替代，唯一的好处是少写一行re.compile()代... |
2,501 | <ASSISTANT_TASK:>
Python Code:
from varcode import Variant
from pyensembl import ensembl_grch37
myVariant = Variant(contig=7, start=140453136, ref="A", alt="T", ensembl=ensembl_grch37)
myVariant.short_description
myVariant.coding_genes
myEffects = myVariant.effects()
myEffects
topPriorityEffect = myEffects.top_pri... | <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: Now let's create a new Variant that will represent our variant of interest
Step2: Now that we defined this variant,
Step3: this is our variati... |
2,502 | <ASSISTANT_TASK:>
Python Code:
x = Variable(T.ones(2,2), requires_grad=True)
print x
y = T.exp(x + 2)
yy = T.exp(-x-2)
print y
z = (y + yy)/2
out = z.mean()
print z, out
make_dot(out)
out.backward(T.FloatTensor(1), retain_graph=True)
x.grad
T.randn(1,1)
from __future__ import print_function
xx = Variable(torch.randn(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: A simple numpy implementation of one hidden layer neural network.
Step2: with very slight modifications, we could end up with the implementati... |
2,503 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import SVG
SVG(filename='mass_spring_damper.svg')
import sympy as sym
import sympy.physics.mechanics as me
from sympy.physics.vector import init_vprinting
init_vprinting()
x, v = me.dynamicsymbols('x v')
m, c, k, g, t = sym.symbols('m c k g t')
ceiling = me.Refere... | <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: Start by loading in the core functionality of both SymPy and Mechanics.
Step2: We can make use of the pretty printing of our results by loading... |
2,504 | <ASSISTANT_TASK:>
Python Code:
%%bash
pip freeze | grep tensor
!pip3 install tensorflow-hub==0.7.0
!pip3 install --upgrade tensorflow==1.15.3
!pip3 install google-cloud-bigquery==1.10
import os
import tensorflow as tf
import numpy as np
import tensorflow_hub as hub
import shutil
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: Let's make sure you install the necessary version of tensorflow-hub. After doing the pip install below, click "Restart the kernel" on the notebo... |
2,505 | <ASSISTANT_TASK:>
Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
import numpy as np
import mne
from mne import io
from mne.stats import permutation_t_test
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
meg_path = data_path / 'MEG' / 'sa... | <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: View location of significantly active sensors
|
2,506 | <ASSISTANT_TASK:>
Python Code:
from sys import version
print(version)
from typing import List, Any, TypeVar
T = TypeVar("T")
tableau1 = [5, 4, 1, 2, 3]
tableau2 = [1, 1, 2, 3] # avec un doublon
def selection_naive(tableau: List[T], k: int) -> T:
Sélection du k-ième plus petit élément du tableau, récursivemen... | <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: J'ai pris l'habitude d'écrire des signatures de fonctions en python qui soient typées
Step2: Je ferai les premiers exemples avec ces deux tabl... |
2,507 | <ASSISTANT_TASK:>
Python Code:
from pyannote.core import Segment
# start time in seconds
s = 1.
# end time in seconds
e = 9.
segment = Segment(start=s, end=e)
segment
start, end = segment
print 'from %f to %f' % (start, end)
print 'Segment %s ends at %g seconds.' % (segment, segment.end)
print 'Its duration is %g sec... | <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: Segment instances are used to describe temporal fragments (e.g. of an audio file).
Step2: Segment instances are nothing more than 2-tuples augm... |
2,508 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
plt.rc("figure", figsize=(16,8))
plt.rc("font", size=14)
# First we'll simulate the synthetic data
def simulate_seasonal_term(periodicity, total_cycles, noise_std=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: Synthetic data creation
Step2: Unobserved components (frequency domain modeling)
Step3: Observe that the fitted variances are pretty close to ... |
2,509 | <ASSISTANT_TASK:>
Python Code:
# this line is required to see visualizations inline for Jupyter notebook
%matplotlib inline
# importing modules that we need for analysis
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import re
# read the data from file and print out first few rows
jeopardy = pd.... | <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: Apparently columns have a blank space in the beginning. Let's get rid of them
Step2: Hypothesis - "Value of the question is related to its leng... |
2,510 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import sympy as sy
from sympy.utilities.codegen import codegen
import control.matlab as cm
import re
import matplotlib.pyplot as plt
from scipy import signal
z = sy.symbols('z', real=False)
r1,s0,s1 = sy.symbols('r1,s0,s1', real=True)
hh = sy.symbols('h', real=True, pos... | <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: Determine sampling period and desired closed loop poles
Step2: Design a 2-DoF controller
|
2,511 | <ASSISTANT_TASK:>
Python Code:
# Author: Marijn van Vliet <w.m.vanvliet@gmail.com>
# Roman Goj <roman.goj@gmail.com>
# Denis Engemann <denis.engemann@gmail.com>
# Stefan Appelhoff <stefan.appelhoff@mailbox.org>
#
# License: BSD-3-Clause
import os.path as op
import numpy as np
import mne
from mne... | <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: Reading the raw data and creating epochs
Step2: We are interested in the beta band. Define a range of frequencies, using a
Step3: Computing th... |
2,512 | <ASSISTANT_TASK:>
Python Code:
!pygmentize moviesentiment.yaml
!kubectl apply -f moviesentiment.yaml
CLUSTER_IPS=!(kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.status.loadBalancer.ingress[0].ip}')
CLUSTER_IP=CLUSTER_IPS[0]
print(CLUSTER_IP)
SERVICE_HOSTNAMES=!(kubectl get inferenceservice mov... | <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: Get Explanation for Negative Prediction
Step2: Show precision. How likely predictions using the Anchor features would produce the same result.
... |
2,513 | <ASSISTANT_TASK:>
Python Code:
from pathlib import Path
Path.home()
import uuid
uuid.uuid4()
from har2tree import CrawledTree
har_path = Path() / '..' / 'tests' / 'capture_samples' / 'http_redirect' / '0.har'
my_first_crawled_tree = CrawledTree([har_path], str(uuid.uuid4()))
my_first_crawled_tree.root_url
print(my_... | <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: Great. Now let's try to create our first tree. As mentioned before, you will also need to pass a uuid as a parameter, but don't worry, python ha... |
2,514 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from statsmodels.compat import lmap
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import statsmodels.api as sm
norms = sm.robust.norms
def plot_weights(support, weights_func, xlabels, xticks):
fig = plt.figure(figsize=(12,8))
ax = 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: An M-estimator minimizes the function
Step2: Andrew's Wave
Step3: Hampel's 17A
Step4: Huber's t
Step5: Least Squares
Step6: Ramsay's Ea
St... |
2,515 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from __future__ import division
capital_base = 100000
r_p = 0.05 # Aggregate performance of assets in the portfolio
r_no_lvg = capital_base * r_p
print 'Portfolio returns without leverage: {0}'.format(r_no_lvg)
debt =... | <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 what portfolio returns look like without leverage. Let's add some debt, leveraging the portfolio, and see how the returns change.
Step2:... |
2,516 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
# check tf version
print(tf.__version__)
a = tf.constant(2)
b = tf.constant(5)
operation = tf.add(a, b, name='cons_add')
with tf.Session() as ses:
print ses.run(operation)
sub_operation = tf.subtract(a, b, name='cons_subtraction')
x = tf.constant([[-1.37 ... | <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: Config Contants
Step2: en la variable "b" vamos a asignar una constante con el valor inicial de "5"
Step3: En la siguiente variable "operation... |
2,517 | <ASSISTANT_TASK:>
Python Code:
# Выделяем outdoor'ы и indoor'ы.
sample_out = sample[result[:, 0] == 1]
sample_in = sample[result[:, 1] == 1]
result_out = result[result[:, 0] == 1]
result_in = result[result[:, 1] == 1]
# Считаем размер indoor- и outdoor-частей в train'е.
train_size_in = int(sample_in.shape[0] * 0.75)
tr... | <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: Для каждой картинки мы хотим найти вектор $(p_0, p_1)$, вероятностей такой, что $p_i$ - вероятность того, что картинка принадлежит классу $i$ ($... |
2,518 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%config InlineBackend.figure_format='retina'
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
names = ["ID","R","I","J","H","KS","TiO_7140","TiO_8465","NaI_8189","Spectral Type","EW_Ha","Gravity"]
tbl1 = pd.read_csv("http://iopscience.iop.org/1538-... | <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: Table 1- Measured Quantities for PMS Candidates with Observed Spectra
Step2: Table 2 - Derived Quantities for New USco Members
Step3: Save the... |
2,519 | <ASSISTANT_TASK:>
Python Code:
import collections
import os
import StringIO
import sys
import tarfile
import tempfile
import urllib
from IPython import display
from ipywidgets import interact
from ipywidgets import interactive
from matplotlib import gridspec
from matplotlib import pyplot as plt
import numpy as np
from ... | <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: Select and download models
Step5: Load model in TensorFlow
Step6: Helper methods
Step7: Run on sample images
Step8: Run on internet images
|
2,520 | <ASSISTANT_TASK:>
Python Code:
from atmPy.aerosols.instruments.POPS import mie
%matplotlib inline
import matplotlib.pylab as plt
plt.rcParams['figure.dpi'] = 200
d,amp = mie.makeMie_diameter(noOfdiameters=1000)
f,a = plt.subplots()
a.plot(d,amp)
a.loglog()
a.set_xlim((0.1,3))
a.set_ylabel('Signal intensity (arb. u.)')... | <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: standard settings
Step2: Wavelength dependence
Step3: refractive index dependence
|
2,521 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import itertools
import pandas as pd
# In Python 2.7 the division of integers is not float. Do this to have 1 / 2 = .5
from __future__ import division
# Number of simulations
S = 1000
# Number of observations in each sample
N = [10, 100, 1000]
# True parameter values
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: Define parameters of the simulation
Step3: Define the function that returns rejection probabilities
Step4: Run simulation study
|
2,522 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
A = np.array([1,1,2,3,3,3,4,5,6,7,8,8])
B = np.array([1,2,8])
C = A[np.in1d(A,B)]
<END_TASK> | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
|
2,523 | <ASSISTANT_TASK:>
Python Code:
%cd -q ~/neurokernel/examples/sensory_int/data
%run gen_vis_input.py
%run gen_olf_input.py
%run gen_integrate.py
%cd -q ~/neurokernel/examples/sensory_int/
%run sensory_int_demo.py
%run visualize_output.py
import IPython.display
IPython.display.YouTubeVideo('e-eUOtOF9fc')
<END_TASK> | <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: Once the input and the configuration are ready, we execute the entire model. Note that the interconnections between the integration LPU and both... |
2,524 | <ASSISTANT_TASK:>
Python Code:
print('abc')
print(1, 2, 3)
print(1, 2, 3, sep='--')
def fibonacci(N):
L = []
a, b = 0, 1
while len(L) < N:
a, b = b, a + b
L.append(a)
return L
fibonacci(10)
def real_imag_conj(val):
return val.real, val.imag, val.conjugate()
r, i, c = real_imag_co... | <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: Aqui, print() es el nombre de la función, y 'abc' es lo que se llama un argumento (de la función).
Step2: Cuando se usan argumentos y argumento... |
2,525 | <ASSISTANT_TASK:>
Python Code:
a_set = {1, 2, 3}
a_set
empty_set = set() # you have to use set() to create an empty set! (we will see why later)
print(empty_set)
a_set = {1, 2, 1, 1}
print(a_set)
a_set = {1, 3, 2}
print(a_set)
{1, 2, 3} == {2, 3, 1}
a_set = {1, 'a'}
print(a_set)
a_set = {1, []}
a_set = set()
a_se... | <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: Curly brackets surround sets, and commas separate the elements in the set
Step2: Please note that sets are unordered. This means that it can oc... |
2,526 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
raw_data = {'first_name': ['Jason', np.nan, 'Tina', 'Jake', 'Amy'],
'last_name': ['Miller', np.nan, 'Ali', 'Milner', 'Cooze'],
'age': [42, np.nan, 36, 24, 73],
'sex': ['m', np.nan, 'f', 'm', 'f'],
'preTestScore': ... | <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 dataframe with missing values
Step2: Drop missing observations
Step3: Drop rows where all cells in that row is NA
Step4: Create a new ... |
2,527 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
%matplotlib inline
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
# import all Shogun classes
from shogun import *
from matplotlib.patches import Ellipse
# a tool for visualisation
def get_gaussian_ellipse_artist(mean, cov, nstd=1.96, color="red", li... | <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: Gaussian Mixture Models and Expectation Maximisation in Shogun
Step2: Set up the model in Shogun
Step3: Sampling from mixture models
Step4: E... |
2,528 | <ASSISTANT_TASK:>
Python Code:
from pscript import py2js, evalpy
js = py2js('for i in range(10): print(i)')
print(js)
def foo(x):
res = []
for i in range(x):
res.append(i**2)
return res
js = py2js(foo)
print(js)
def foo(x):
return [i**2 for i in range(x)]
js = py2js(foo)
print(js)
class Bar:... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: We can transpile strings of Python code
Step2: Or actual Python functions
Step3: Let's try that again, but now with a list comprehension. (The... |
2,529 | <ASSISTANT_TASK:>
Python Code:
#import pandas for conviently labelled arrays
import pandas
# import numpy for SVD function
import numpy
# import matplotlib.pyplot for visualising arrays
import matplotlib.pyplot as plt
# create a simple word-document matrix as a pandas dataframe, the content values have been normalised... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: A Simple Word-Document Matrix
Step2: Word-Document Matrix is A
Step3: Now Take the SVD
Step4: We can see above that the values in the diagona... |
2,530 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
a = np.array(
[[[ 0, 1],
[ 2, 3],
[ 4, 5]],
[[ 6, 7],
[ 8, 9],
[10, 11]],
[[12, 13],
[14, 15],
[16, 17]]]
)
b = np.array(
[[0, 1, 1],
[1, 0, 1],
[1, 1, 0]]
)
result = np.take_along_axis(a, b[..., np.newaxis], ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
2,531 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import pymc3 as pm
import matplotlib.pyplot as plt
# from pandas_datareader import data
# prices = data.GoogleDailyReader(symbols=['GLD', 'GFI'], end='2014-8-1').read().loc['Open', :, :]
prices = pd.read_csv(pm.get_data('stock_pri... | <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: Lets load the prices of GFI and GLD.
Step2: Plotting the prices over time suggests a strong correlation. However, the correlation seems to chan... |
2,532 | <ASSISTANT_TASK:>
Python Code:
def execute_notebook(nbfile):
with io.open(nbfile) as f:
nb = current.read(f, 'json')
ip = get_ipython()
for cell in nb.worksheets[0].cells:
if cell.cell_type != 'code':
continue
ip.run_cell(cell.input)
#execute_notebook("POR... | <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: Notebook Execute
Step2: plot_graph(Cotistas , "Pretos Pardos e Indígenas")
Step3: IMPORT ALL CANDIDATES CSV
Step4: Get CPFs
Step5: DELETAR
S... |
2,533 | <ASSISTANT_TASK:>
Python Code:
from pyesgf.search import SearchConnection
conn = SearchConnection('https://esgf-data.dkrz.de/esg-search', distrib=True)
ctx = conn.new_context(
project='CMIP6',
source_id='UKESM1-0-LL',
experiment_id='historical',
variable='tas',
frequency='mon',
variant_labe... | <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: Subset single dataset with xarray
Step2: Subset over multiple datasets
Step3: Download dataset
|
2,534 | <ASSISTANT_TASK:>
Python Code:
!pip freeze | grep tensorflow-hub==0.7.0 || pip install tensorflow-hub==0.7.0
import os
import tensorflow as tf
import tensorflow_hub as hub
PROJECT = "your-gcp-project-here" # REPLACE WITH YOUR PROJECT NAME
BUCKET = "your-gcp-bucket-here" # REPLACE WITH YOUR BUCKET NAME
os.environ["PROJ... | <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: Replace by your GCP project and bucket
Step2: Setting up the Kubeflow cluster
Step3: It has very specialized language such as
Step 1
Step4: ... |
2,535 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
from scipy import stats
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.tsa.arima.model import ARIMA
from statsmodels.graphics.api import qqplot
print(sm.datasets.sunspots.NOTE)
dta = sm.datasets.suns... | <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: Sunspots Data
Step2: Does our model obey the theory?
Step3: This indicates a lack of fit.
Step4: Exercise
Step5: Let's make sure this model ... |
2,536 | <ASSISTANT_TASK:>
Python Code:
import naminggamesal.ngpop as ngpop
pop_cfg={
'voc_cfg':{
'voc_type':'matrix',
'M':5,
'W':10
},
'strat_cfg':{
'strat_type':'naive',
'vu_cfg':{'vu_type':'BLIS_epirob'}
},
'interact_cfg':{
'interact_type':'speakers... | <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 create a population. Agent creation is here dealt with automatically. Still, it is possible to manually add or remove agents (Hence the ID... |
2,537 | <ASSISTANT_TASK:>
Python Code:
#Importation des librairies utilisées
import time
import pandas as pd
import numpy as np
import collections
import itertools
import os
import warnings
warnings.filterwarnings('ignore')
from sklearn.cross_validation import train_test_split
data_valid_clean_stem = pd.read_csv("data/cdiscou... | <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: Téléchargement des données
Step2: On créé un dossier dans lequel nous allons sauvegarder les DataFrame constitués des features que l'on va cons... |
2,538 | <ASSISTANT_TASK:>
Python Code:
# Import the simulation function
from pymer4.simulate import simulate_lm
# Also fix the random number generator for reproducibility
import numpy as np
np.random.seed(10)
data, b = simulate_lm(
500, 3, coef_vals=[100, 1.2, -40.1, 3], mus=[10, 30, 1], noise_params=(0, 5)
)
print(f"True ... | <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: Here are some checks you might do to make sure the data were correctly generated
Step2: Check correlations between predictors
Step3: Check coe... |
2,539 | <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: Create a TFX pipeline using templates with Local orchestrator
Step2: NOTE
Step3: Let's check the version of TFX.
Step4: And, it's done. We ar... |
2,540 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['figure.figsize'] = (20.0, 10.0)
plt.rcParams['font.family'] = "serif"
df = pd.read_csv('../../datasets/movie_metadata.csv')
df.head()
# split each movie's genre list, then form a se... | <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: For the bar plot, let's look at the number of movies in each category, allowing each movie to be counted more than once.
Step2: Basic plot
Step... |
2,541 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('bmh')
%matplotlib inline
plt.figure(figsize = (12, 6))
for i in range(10):
x = np.arange(i * 10, i * 10 + 10)
y_var1 = np.random.randint(1, 5, 10)
y_var2 = np.random.randint(5, 8, 10)
plt.plot(x, y_var1, 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: Como véis, en la gráfica anterior hay varios problemas pero como esta es una MicroEntrada solo nos vamos a centrar en el problema de las etiquet... |
2,542 | <ASSISTANT_TASK:>
Python Code:
%load_ext cypher
%%cypher
MATCH
(t:Type)-[:ANNOTATED_BY]->()-[:OF_TYPE]->(a:Type)
WHERE
a.fqn="javax.persistence.Entity"
SET
t:Entity
RETURN
t.fqn AS Entity
%%cypher
MATCH (e:Entity)<-[:CONTAINS]-(p:Package)
WHERE p.name = "model"
RETURN e.fqn as Entity, p.name as Package
%%cyp... | <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: Define Concerns
Step2: Rule Definition
Step3: Rule Violations
|
2,543 | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.append('..')
import os
import json
from time import time
import numpy as np
from tqdm import tqdm
import theano
import theano.tensor as T
from theano.sandbox.cuda.dnn import dnn_conv
from PIL import Image
from lib import activations
from lib import updates
from lib im... | <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: N.B. The code from the following imports is lifted from the original dcgan project
Step2: Data Stuff
Step3: Check data looks sensible
Step4: ... |
2,544 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
@memory.cache
def g(x):
print('A long-running calculation, with parameter %s' % x)
return np.hamming(x)
@memory.cache
def h(x):
print('A second long-running calculation, using g(x)')
return np.vander(x)
a = g(3)
a
g(3)
h(a)
h(a)
cachedir2 = mkdtemp()
... | <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: using memmapping
|
2,545 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
mystr = "100110"
result = np.array(list(mystr), dtype = int)
<END_TASK> | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
|
2,546 | <ASSISTANT_TASK:>
Python Code:
from sklearn.datasets import load_digits
digits = load_digits()
%matplotlib inline
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(6, 6)) # figure size in inches
fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
# plot the digits: each image is 8x... | <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 re-use some of our code from before to visualize the data and remind us what
Step2: Visualizing the Data
Step3: Here we see that the dig... |
2,547 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
iris = pd.read_csv('../datasets/iris_without_classes.csv') # Read the file 'datasets/iris_without_classes.csv'
# Print the first entries using the head() method to check that there is no Class information anymore
iris.head()
# Use PCA's fit_transform() method to redu... | <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: Reducing dimensions
Step2: How many distinct groups can you see?
|
2,548 | <ASSISTANT_TASK:>
Python Code:
from pprint import pprint
from time import sleep
from pynq import PL
from pynq import Overlay
from pynq.drivers import Trace_Buffer
from pynq.iop import Pmod_TMP2
from pynq.iop import PMODA
from pynq.iop import PMODB
from pynq.iop import ARDUINO
ol = Overlay("base.bit")
ol.download()
tmp... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step 1
Step1: Step 2
Step2: Step 3
Step3: Step 4
Step4: Step 5
|
2,549 | <ASSISTANT_TASK:>
Python Code:
# Import the FISSA toolbox
import fissa
# For plotting our results, import numpy and matplotlib
import matplotlib.pyplot as plt
import numpy as np
# Fetch the colormap object for Cynthia Brewer's Paired color scheme
colors = plt.get_cmap("Paired")
# Define path to imagery and to the ROI... | <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 also need to import some plotting dependencies which we'll make use in this notebook to display the results.
Step2: Running FISSA
Step3: Th... |
2,550 | <ASSISTANT_TASK:>
Python Code:
import os
import numpy as np
import pandas as pd
import lmfit
from fretbursts import *
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
%config InlineBackend.figure_format='retina' # for hi-dpi displays
sns.set_style('whitegrid')
#bsearch_ph_sel = 'AND-gate'
bsea... | <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 Raw PR
Step2: These are the RAW proximity ratios for the 5 samples (only background correction, no leakage nor direct excitation)
Step3: ... |
2,551 | <ASSISTANT_TASK:>
Python Code:
import random
n = 10
data = [random.randint(1, 10) for _ in range(n)]
data # this print out the variable's content
def nsqrt(x): # do not change the heading of the function
pass # **replace** this line with your code
print(nsqrt(11), nsqrt(1369))
import matplotlib
import numpy... | <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 1
Step2: You can test your implementation using the following code.
Step3: Exercise 2
Step4: To find $x$ for the equation, we need t... |
2,552 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division, print_function
from sklearn.metrics import mean_absolute_error
import torch
import torch.nn as nn
from torch.autograd import Variable
import math
import matplotlib.pyplot as plt
import numpy as np
import os
import shutil
%matplotlib inline
def generate_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: Visualize Input
Step2: Define Constants
Step3: Generate Training Data
Step4: Define Network
Step5: Train Network
Step6: Test Model
|
2,553 | <ASSISTANT_TASK:>
Python Code:
environment_directory = "environments/"
identifier = "test_all_methods"
log_directory = "log/"
if not os.path.exists('log'):
os.makedirs('log')
logging.basicConfig(format='%(asctime)s %(message)s', datefmt='%m/%d/%Y %H:%M:%S',
filename=log_directory + identifier +... | <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: Then, after a log folder is created, if it doesn't exist, the logger will be initialized. The log files will contain information about how the s... |
2,554 | <ASSISTANT_TASK:>
Python Code:
from IPython.core.display import HTML
css_file = 'pynoddy.css'
HTML(open(css_file, "r").read())
import sys, os
import matplotlib.pyplot as plt
# adjust some settings for matplotlib
from matplotlib import rcParams
# print rcParams
rcParams['font.size'] = 15
# determine path of repository 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: Adding features to geological layers
Step2: ok, seems to work - now for all
|
2,555 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from __future__ import division
%config InlineBackend.figure_formats=['svg']
%matplotlib inline
plt.rc('pdf',fonttype=3) # for proper subsetting of fonts
plt.rc('axes',linewidth=0.5) # thin axes; the defaul... | <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 can see (at least qualitatively), from the plot of the objective funtion that on the interval $0.05 < \alpha < 0.15$ the optimum value lies s... |
2,556 | <ASSISTANT_TASK:>
Python Code:
from tensorflow import keras
import numpy
x = numpy.array([0, 1, 2, 3, 4])
y = x * 2 + 1
model = keras.models.Sequential()
model.add(keras.layers.Dense(1,input_shape=(1,)))
model.compile('SGD', 'mse')
model.fit(x[:2], y[:2], epochs=1000, verbose=0)
print(model.predict(x))
import tensorf... | <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: II. 케라스 인터페이스를 사용하는 텐서플로 2.0 사용법(Tensorflow 2.0 with Keras IO)
Step2: 간단한 구성에 진행 결과 보이기
Step3: 클래스를 이용한 네트웍 모델 구성하기
|
2,557 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
import tellurium as te
te.setDefaultPlottingEngine('matplotlib')
%matplotlib inline
import phrasedml
antimony_str = '''
model myModel
S1 -> S2; k1*S1
S1 = 10; S2 = 0
k1 = 1
end
'''
phrasedml_str = '''
model1 = model "myModel"
sim1 = simulate... | <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: Reading / Executing SED-ML
Step3: SED-ML L1V2 specification example
|
2,558 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn
seaborn.set()
import cPickle
import numpy as np
from keras import backend as K
from keras.models import Sequential, model_from_yaml
from keras.layers.recurrent import LSTM
from keras.layers.core import Activation, Dense, Dr... | <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: Read data
Step2: Train
Step3: Load previous model
|
2,559 | <ASSISTANT_TASK:>
Python Code:
from openpiv import tools, pyprocess, validation, filters, scaling
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import imageio
frame_a = tools.imread( '../../examples/test1/exp1_001_a.bmp' )
frame_b = tools.imread( '../../examples/test1/exp1_001_b.bmp' )
fig,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: Reading images
Step2: Processing
Step3: The function get_coordinates finds the center of each interrogation window. This will be useful later ... |
2,560 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import sys
import os
import shutil
import numpy as np
from subprocess import check_output
# Import flopy
import flopy
# Set the name of the path to the model working directory
dirname = "P4-5_Hubbertville"
datapath = os.getcwd()
modelpath = os.path.join(datapath, dirna... | <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 a New Directory and Change Paths
Step2: Define the Model Extent, Grid Resolution, and Characteristics
Step3: Create the MODFLOW Model Ob... |
2,561 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
%matplotlib inline
# include all Shogun classes
from modshogun import *
# generate some ultra easy training data
gray()
n=20
title('Toy data for binary classification')
X=hstack((randn(2,n), randn(2,n)+1))
Y=hstack((-ones(n), ones(n)))
_=scatter(X[0], X[1], c=Y , s=100)
p1 =... | <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: Types of splitting strategies
Step2: Stratified cross-validation
Step3: Leave One Out cross-validation
Step4: Stratified splitting takes care... |
2,562 | <ASSISTANT_TASK:>
Python Code:
# Load image
import cv2
import numpy as np
from matplotlib import pyplot as plt
# Load images
image_bgr = cv2.imread('images/plane_256x256.jpg')
image_gray = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2GRAY)
# Number of corners to detect
corners_to_detect = 10
minimum_quality_score = 0.05
min... | <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 image
Step2: Define Corner Parameters
Step3: Detect Corners
Step4: Mark Corners
Step5: View Image
|
2,563 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score, silhouette_samples
import matplotlib.pyplot as plt
import matplotlib.cm as cm
%matplotlib inline
# processing .csv containing county statistics
counties = pd.read_csv('... | <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: Importing the .csv files and processing them into a single dataframe.
Step2: This creates a dataframe containing all of the counties where Trum... |
2,564 | <ASSISTANT_TASK:>
Python Code:
#importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline
#reading in an image
#image = mpimg.imread('test_images/solidWhiteRight.jpg')
#printing out some stats and plotting
#print('This image is:', t... | <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: Read in an Image
Step9: Ideas for Lane Detection Pipeline
Step10: Test Images
Step11: Build a Lane Finding Pipeline
Step12: Test on Videos
S... |
2,565 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from quimb.tensor import *
from quimb import *
import numpy as np
# the initial state
n = 50
cyclic = False
chi = 4 # intial bond dimension
psi = MPS_rand_state(n, chi, cyclic=cyclic, tags='KET', dtype='complex128')
# the gates
n_gates = 5 * n
gates = [rand_uni(4) for... | <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 specify how sites we want, how many gates to apply, and some other parameters
Step2: We generate a unique tag for each gate we will ap... |
2,566 | <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: The Basics
Step2: Set up training data
Step3: Some Machine Learning terminology
Step4: Assemble layers into the model
Step5: Note
Step6: Th... |
2,567 | <ASSISTANT_TASK:>
Python Code:
from symbulate import *
%matplotlib inline
die = list(range(1, 6 + 1))
P = BoxModel(die, size=2)
X = RV(P, sum)
Y = RV(P, max)
die = list(range(1, 6 + 1))
P = BoxModel(die, size=2)
X = RV(P, sum)
Y = RV(P, max)
(X & Y).sim(10000).tabulate(normalize=True)
die = list(range(1, 6 + 1))
P =... | <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 id='joint'></a>
Step2: <a id='ampersand'></a>
Step3: <a id='plot'></a>
Step4: See the section on Symbulate graphics for more details on pl... |
2,568 | <ASSISTANT_TASK:>
Python Code:
Initialization
'''
Standard modules
'''
import os
import pickle
import sqlite3
import time
from pprint import pprint
'''
Analysis modules
'''
import pandas as pd
'''
Custom modules
'''
import config
import utilities
'''
Misc
'''
nb_name = '20171011-daheng-check_topics_basic_statistics'
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: Check basic statistics of manually selected topics
Step3: Manually compile a list of topics with keywords
Step5: Check number of associated ne... |
2,569 | <ASSISTANT_TASK:>
Python Code:
import rebound
sim = rebound.Simulation()
sim.add(m=1., x=1., vz = 2.)
sim.add(m=1., a=1.)
sim.status()
sim.add(m=1.e-3, a=100.)
sim.add(primary=sim.particles[1], a=0.01)
orbits = sim.calculate_orbits()
for orbit in orbits:
print(orbit)
print(sim.particles[3].calculate_orbit(sim,... | <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: Any components not passed automatically default to 0. REBOUND can also accept orbital elements.
Step2: We always have to pass a semimajor ax... |
2,570 | <ASSISTANT_TASK:>
Python Code:
from sympy import *
from sympy.abc import i
init_printing()
alpha, beta, gamma = symbols(r'\alpha \beta \gamma')
x_ave, y_ave = symbols(r'\langle{x}\rangle \langle{y}\rangle')
x2_ave, xy_ave = symbols(r'\langle{x^{2}}\rangle \langle{xy}\rangle')
x3_ave, x2y_ave = symbols(r'\langle{x^{3}}... | <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: Construct matrix A
Step2: Construct b
Step3: Solve for $\vec{a}$
Step4: The $a_0$ component
Step5: The $a_{1}$ component
Step6: The $a_{2}$... |
2,571 | <ASSISTANT_TASK:>
Python Code:
# Inicializamos una figura con el tamaño que necesitemos
# si no la queremos por defecto
# Creamos unos ejes con la proyección que queramos
# por ejemplo, Mercator
# Y lo que queremos representar en el mapa
# Tierra
# Océanos
# Líneas de costa (podemos modificar el color)
# Fronteras
# Rí... | <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: InterruptedGoodeHomolosine
Step2: Puede interesarnos poner etiquetas a los ejes. Podemos utilizar entonces las herramientas dentro de
Step3: ... |
2,572 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pyemu
la = pyemu.Schur("pest.jco",verbose=False,forecasts=[])
la.drop_prior_information()
jco_ord = la.jco.get(la.pst.obs_names,la.pst.adj_par_names)
ord_base = "pest_ord"
jco_ord.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: instaniate pyemu object and drop prior info. Then reorder the jacobian and save as binary. This is needed because the pest utilities require s... |
2,573 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'niwa', 'ukesm1-0-ll', 'aerosol')
# 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... |
2,574 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
from floweaver import *
df1 = pd.read_csv('holiday_data.csv')
dataset = Dataset(df1)
df1
partition_job = Partition.Simple('Employment Job', np.unique(df1['Employment Job']))
partition_activity = Partition.Simple('Activity', np.unique(df1['Activity'... | <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 take a look at the dataset we are using. This is a very insightful [made-up] dataset about how different types of people lose weight while o... |
2,575 | <ASSISTANT_TASK:>
Python Code:
class RegExp2NFA:
def __init__(self, Sigma):
self.Sigma = Sigma
self.StateCount = 0
def toNFA(self, r):
if r == 0:
return self.genEmptyNFA()
if r == '':
return self.genEpsilonNFA()
if isinstance(r, str) and len(r) == 1:
retu... | <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: The member function toNFA takes an object self of class RegExp2NFA and a regular expression r and returns a finite state machine
Step2: The <s... |
2,576 | <ASSISTANT_TASK:>
Python Code:
from adaptivemd import mongodb
mongodb.MongoDBStorage._db_url
mongodb.MongoDBStorage.set_port(27018)
mongodb.MongoDBStorage._db_url
mongodb.MongoDBStorage.set_host('128.219.191.255')
mongodb.MongoDBStorage._db_url
mongodb.MongoDBStorage.set_location('localhost:27017')
mongodb.MongoDBSt... | <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: To change the port number, use the set_port method of the MongoDBStorage interface class
Step2: Likewise, reset the host address with set_host
... |
2,577 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import scipy.sparse as sparse
np.random.seed(10)
max_vector_size = 1000
vectors = [np.random.randint(100,size=900),np.random.randint(100,size=max_vector_size),np.random.randint(100,size=950)]
result = sparse.lil_matrix((len(vectors), max_vector_size))
for i, v in enumer... | <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:
|
2,578 | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.append('C:\Anaconda2\envs\dato-env\Lib\site-packages')
import graphlab
def polynomial_sframe(feature, degree):
# assume that degree >= 1
# initialize the SFrame:
poly_sframe = graphlab.SFrame()
# and set poly_sframe['power_1'] equal to the passed featu... | <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: Polynomial regression, revisited
Step2: Let's use matplotlib to visualize what a polynomial regression looks like on the house data.
Step3: As... |
2,579 | <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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<USER_TASK:>
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:... |
2,580 | <ASSISTANT_TASK:>
Python Code:
Image(url="http://i.giphy.com/LY1DH1AMbG0tq.gif")
Image(url="http://i.giphy.com/12eayhW3TRPCjS.gif")
# Charger la lib
import pandas as pd
#Afficher l'aide
#pd.read_csv?
data = pd.read_csv('data/train.csv') # Chargement des données.
data.head()
data.tail()
data.shape
data.dtypes
da... | <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: Titanic dataset
Step2: Analyse data
Step3: Pour regarder les données
Step4: Signification des colonnes
Step5: 2) Connaitre les type des ... |
2,581 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as pl
from revrand.basis_functions import RandomRBF, RandomLaplace, RandomCauchy, RandomMatern32, RandomMatern52, \
FastFoodRBF, OrthogonalRBF, FastFoodGM, BasisCat
from revrand import Parameter, Positive
# Style
pl.style.... | <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: Settings
Step2: Kernel functions
Step3: Basis functions
Step4: Evaluate kernels and bases
Step5: Plot the kernel functions
|
2,582 | <ASSISTANT_TASK:>
Python Code:
# ionization degree alpha calculated from the Henderson-Hasselbalch equation for an ideal system
def ideal_alpha(pH, pK):
return 1. / (1 + 10**(pK - pH))
import matplotlib.pyplot as plt
import numpy as np
import setuptools
import pint # module for working with units and dimensions
a... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Constant pH Method
Step2: The package pint is intended to make handling physical quantities with different units easy. You simply create an ins... |
2,583 | <ASSISTANT_TASK:>
Python Code:
responses = {}
responses
type(responses)
responses["hello"] = "world"
responses
responses["hola"] = "mundo"
responses
def greet(salutation):
try:
print(salutation, responses[salutation])
except KeyError:
print("Sorry, don't know how to respond to", salutation)
gr... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: A dictionary can store values for a key. In this example, we will store the value "world", at the key "hello".
Step2: One nice property of dict... |
2,584 | <ASSISTANT_TASK:>
Python Code:
print("Most billionaires are from the following countries in descending order:")
df['countrycode'].value_counts().head(5)
us = 903 / 1000000000
ger = 160 / 1000000000
china = 153 / 1000000000
russia = 119 / 1000000000
japan = 96 / 1000000000
print("per billion for us is", us, "for germany... | <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. What's the average wealth of a billionaire? Male? Female?
Step2: 3. Most common source of wealth? Male vs. female?
Step3: 4. List top ten b... |
2,585 | <ASSISTANT_TASK:>
Python Code:
import matplotlib
import matplotlib.pyplot as pyplot
import numpy
import os
import time
# tensorflow
import tensorflow as tf
from tensorflow.python.training import adagrad
from tensorflow.python.training import adam
from tensorflow.python.training import gradient_descent
# python3-6 NCS. ... | <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: pyNCS analysis
Step2: pyCNCS analysis
Step3: Compare results to reference implementation.
Step4: Tensorflow
|
2,586 | <ASSISTANT_TASK:>
Python Code:
mu = [2, 3]
cov = [[2, -1],[2, 4]]
rv = sp.stats.multivariate_normal(mu, cov)
xx = np.linspace(-1, 5, 150)
yy = np.linspace(0, 6, 120)
XX, YY = np.meshgrid(xx, yy)
ZZ = rv.pdf(np.dstack([XX, YY]))
plt.contour(XX, YY, ZZ)
plt.xlabel("x")
plt.ylabel("y")
plt.title("Joint Probability Density... | <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: 동일한 결합 확률 밀도 함수를 3차원으로 그리면 아래와 같다.
Step2: 이산 확률 변수의 결합 확률 질량 함수
Step3: 주변 확률 밀도 함수
Step4: 위에서 예로 든 연속 확률 변수의 경우에 주변 확률 밀도 함수를 계산하면 다음과 같다.
St... |
2,587 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import xarray as xr
from netCDF4 import num2date
import matplotlib.pyplot as plt
print("numpy version : ", np.__version__)
print("pandas version : ", pd.__version__)
print("xarray version : ", xr.__version__)
dpm = {'noleap': [... | <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: Some calendar information so we can support any netCDF calendar.
Step4: A few calendar functions to determine the number of days in each month
... |
2,588 | <ASSISTANT_TASK:>
Python Code:
import rebound
import reboundx
sim = rebound.Simulation()
sim.add(m=1.)
sim.add(m=1.e-3, a=1., e=0.2)
ps = sim.particles
rebx = reboundx.Extras(sim)
cf = rebx.load_force("central_force")
rebx.add_force(cf)
ps[0].params["gammacentral"] = -1. # period needed after integer power
ps[0].par... | <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: Now we add REBOUNDx and our effect as usual
Step2: We need to choose a normalization Acentral and power gammacentral for our force law (see abo... |
2,589 | <ASSISTANT_TASK:>
Python Code:
# import libraries
# linear algebra
import numpy as np
# data processing
import pandas as pd
# library of math
import math
# data visualization
from matplotlib import pyplot as plt
# datasets
from sklearn import datasets
# Scikit Learning hierarchical clustering
from sklearn.cluster im... | <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: 1.1 Clusterização Hierárquica
Step2: 1.2 Dendrograma
Step3: É possível fazer um teste de permutação para validar o número de clusters escolhid... |
2,590 | <ASSISTANT_TASK:>
Python Code:
from sklearn.datasets import load_files
from keras.utils import np_utils
import numpy as np
from glob import glob
# define function to load train, test, and validation datasets
def load_dataset(path):
data = load_files(path)
dog_files = np.array(data['filenames'])
dog_t... | <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: Import Human Dataset
Step2: <a id='step1'></a>
Step 1
Step3: Before using any of the face detectors, it is standard procedure to convert the i... |
2,591 | <ASSISTANT_TASK:>
Python Code:
import pyspark.sql.functions as sql
import pyspark.sql.types as types
idb_df_version = "20170130"
idb_df = sqlContext.read.parquet("/guoda/data/idigbio-{0}.parquet".format(idb_df_version))
idb_df.count()
subset = (idb_df
.select(idb_df.catalognumber)
.where(idb_df.rec... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: To test our code, find one collection that seems to have numeric ids. Go to the search API and find the most common catalog number
Step2: Is th... |
2,592 | <ASSISTANT_TASK:>
Python Code:
import graphlab
products = graphlab.SFrame('amazon_baby_subset.gl/')
products['sentiment']
products.head(10)['name']
print '# of positive reviews =', len(products[products['sentiment']==1])
print '# of negative reviews =', len(products[products['sentiment']==-1])
import json
with open... | <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 review dataset
Step2: One column of this dataset is 'sentiment', corresponding to the class label with +1 indicating a review with positiv... |
2,593 | <ASSISTANT_TASK:>
Python Code:
# Load pickled data
import pickle
import tensorflow as tf
import numpy as np
# TODO: Fill this in based on where you saved the training and testing data
training_file = 'traffic-signs-data/train.p'
validation_file= 'traffic-signs-data/valid.p'
testing_file = 'traffic-signs-data/test.p'
wi... | <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: Include an exploratory visualization of the dataset
Step3: Step 2
Step4: Model Architecture
Step5: Train, Validate and Test th... |
2,594 | <ASSISTANT_TASK:>
Python Code:
from lxml import etree
tree = etree.parse("data/books.xml")
print(tree)
print(etree.tostring(tree))
print(etree.tostring(tree).decode())
print(etree.tostring(tree, pretty_print=True).decode())
print(len(list(tree.iterfind("//book"))))
for node in tree.iterfind("//book"):
print(no... | <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: For the record, we should mention that there exist many other libraries in Python to parse XML, such as minidom or BeautifulSoup which is an int... |
2,595 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import sys
import numpy as np
import pandas as pd
import json
import matplotlib.pyplot as plt
from io import StringIO
print(sys.version)
print("Pandas:", pd.__version__)
df = pd.read_csv('C:/Users/Peter/Documents/atlas/atlasdata/obs_types/transect.csv', parse_dates=['da... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: shift data to correct column
Step2: use groupby and transform to fill the row
Step3: shift data to correct row using a multi-Index
|
2,596 | <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
import matplotlib.image as mpimg
from IPython.display import HTML
HTML('../style/code_toggle.html')
#soccer = mpimg.imread('figures/WLA_... | <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
Step2: 5.1 Spatial Frequencies<a id='imaging
Step3: For simplicity convert the RGB-color images to grayscale
S... |
2,597 | <ASSISTANT_TASK:>
Python Code:
from lightning import Lightning
from numpy import random, asarray, concatenate
from sklearn import datasets
lgn = Lightning(ipython=True, host='http://public.lightning-viz.org')
imgs = datasets.load_sample_images().images
lgn.image(imgs[0])
imgs = datasets.load_sample_images().images
l... | <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: Connect to server
Step2: <hr> Basic image viewing
Step3: Single-channel images will automatically be presented as grayscale.
Step4: The usual... |
2,598 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tqdm import tqdm
# Model / data parameters
num_classes = 10
input_shape = (28, 28, 1)
n_residual_blocks = 5
# The data, split between train and test sets
(x, _), (y, _) = kera... | <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: Getting the data
Step2: Create two classes for the requisite Layers for the model
Step3: Build the model based on the original paper
Step4: D... |
2,599 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
import openpathsampling as paths
try:
import openmm as omm
import openmm.unit as u
except ImportError: # OpenMM < 7.6
import simtk.openmm as omm
import simtk.unit as u
import mdtraj as md
import openpathsampling.engines.openmm as eng
#... | <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 Alanine in Vacuum and run it using OPS.
Step2: Let's have a look at the content
Step3: An OpenMM simulation in OPS needs 3 ingredients ... |
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