Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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500 | <ASSISTANT_TASK:>
Python Code:
from google.cloud import storage
import pandas as pd
import torch
import torch.optim as optim
import torch.nn as nn
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
from torch.utils.data import random_split
# Public bucket holding the census data
bucket = stor... | <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: Add code to download the data from GCS (in this case, using the publicly hosted data). You will then be able to use the data when training your ... |
501 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import seaborn as snb
import numpy as np
import matplotlib.pyplot as plt
def create_plot():
x = np.arange(0.0, 10.0, 0.1)
plt.plot(x, x**2)
plt.xlabel("$x$")
plt.ylabel("$y=x^2$")
create_plot()
plt.show()
def save_to_file(filename, fig=None):
... | <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: Creating a quatratic plot
Step3: Save the figure
Step4: And it can be easily saved with
|
502 | <ASSISTANT_TASK:>
Python Code:
import math
import datetime ## to deal with dates
from IPython.display import Image
# will return True if a year is a leap year on Mars
def is_leap_year_mars(year):
if year % 3000 == 0:
return False
elif year % 1000 == 0:
return True
elif year % 100 == 0:
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 火星历法
Step2: 所以，一年的总天数，我们可以这样表示
Step3: 测试一下这两个函数。
Step4: 火星月日的计算
Step5: 测试此函数
Step6: 可以看到函数正确的返回了月日以及错误信息。
Step7: 火星元年与校准日期
Step8: 注意到 flo... |
503 | <ASSISTANT_TASK:>
Python Code:
!git clone https://bitbucket.org/luisfernando/html2pdf.git
%%!
echo "Install Xvfd:"
sudo apt-get install xvfb
echo "Install Fonts:"
sudo apt-get install xfonts-100dpi xfonts-75dpi xfonts-scalable xfonts-cyrillic
echo "Install wkhtmltopdf:"
sudo apt-get install wkhtmltopdf
%%!
source acti... | <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: python-wkhtmltopdf (Any Platform)
Step2: Render a URL
Step4: Render a HTML string
|
504 | <ASSISTANT_TASK:>
Python Code:
import larch, numpy, pandas, os
from larch import P, X
larch.__version__
hh, pp, tour, skims, emp = larch.example(200, ['hh', 'pp', 'tour', 'skims', 'emp'])
logsums_file = larch.example(202, output_file='logsums.pkl.gz')
logsums = pandas.read_pickle(logsums_file)
raw = tour.merge(hh, o... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: In this example notebook, we will walk through the estimation of a tour
Step2: For this destination choice model, we'll want to use the mode c... |
505 | <ASSISTANT_TASK:>
Python Code:
# scientific python
import numpy as np
import scipy as sp
# interactive plotting
%pylab inline
# Create a random array of size 3 x 5
X = np.random.random((3, 5))
# Create an array of zeros of size 3 x 5
np.zeros((3, 5))
# Create an array of ones of size 3 x 5
np.ones((3, 5))
# Create th... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The previous command is one of the "magics" of Jupyter. As indicated by the message you have gotten, it imports numpy and matplotlib.
Step2: Ac... |
506 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import lsst.sims.maf.db as db
import lsst.sims.maf.metrics as metrics
import lsst.sims.maf.slicers as slicers
import lsst.sims.maf.metricBundles as metricBundles
from lsst.sims.maf.metrics import BaseMetric
class Coad... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step3: Writing a new metric
Step5: To understand this, you need to know a little bit about "classes" and "inheritance".
Step6: So then how do we use... |
507 | <ASSISTANT_TASK:>
Python Code:
# As usual, a bit of setup
from __future__ import absolute_import, division, print_function
from __future__ import unicode_literals
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs... | <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: Batch Normalization
Step2: Batch normalization
Step3: Batch Normalization
Step4: Batch Normalization
Step5: Fully Connected Nets with Batch ... |
508 | <ASSISTANT_TASK:>
Python Code:
df.dtypes #dtype: Data type for data or columns
print("The data type is",(type(df['Plate ID'][0])))
df['Vehicle Year'] = df['Vehicle Year'].replace("0","NaN") #str.replace(old, new[, max])
df.head()
# Function to use for converting a sequence of string columns to an array of datetime in... | <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. I don't think anyone's car was built in 0AD. Discard the '0's as NaN.
Step2: 3. I want the dates to be dates! Read the read_csv documentatio... |
509 | <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: On-Device Training with TensorFlow Lite
Step2: Note
Step3: The train function in the code above uses the GradientTape class to record operatio... |
510 | <ASSISTANT_TASK:>
Python Code:
# You can use any Python source file as a module by executing an import statement in some other Python source file.
# The import statement combines two operations; it searches for the named module, then it binds the results of that search
# to a name in the local scope.
import numpy as np... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Lab Task 1
Step2: Split the dataframe into train, validation, and test
Step3: Lab Task 2
Step4: Understand the input pipeline
Step5: Lab Tas... |
511 | <ASSISTANT_TASK:>
Python Code:
from pylab import *
from copy import deepcopy
from matplotlib import animation, rc
from IPython.display import HTML
%matplotlib inline
rc('text', usetex=True)
font = {'family' : 'normal',
'weight' : 'bold',
'size' : 15}
matplotlib.rc('font', **font)
E1, E2, E3 = 0., 20.... | <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: 1. Superexchange in a three-level system.
Step2: (b)
Step3: 2. The one-dimensional soft-core potential.
Step4: 3. Ionization from a one-dimen... |
512 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import seaborn as sns
pd.options.display.max_columns = 999
%matplotlib inline
matplotlib.rcParams['savefig.dpi'] = 1.5 * matplotlib.rcParams['savefig.dpi']
# Read the data inside:
loan2011 = pd.read_... | <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: Underwrting
Step2: Let's take a peek at the data.
Step3: How many morgages have been prepaid in these three years?
Step4: Remember prepay inc... |
513 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('Sample-HRM-p50-genotyping.csv')
plt.plot(df.iloc[:, 0],df.iloc[:,1:])
plt.show()
df_melt=df.loc[(df.iloc[:,0]>75) & (df.iloc[:,0]<88)]
df_data=df_melt.iloc[:,1:]
plt.plot(df_melt.... | <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 and Plot Melting Data
Step2: Select melting range
Step3: Normalizing
Step4: Melting Temp
Step5: Calculate and Show Diff Plot
Step6: Cl... |
514 | <ASSISTANT_TASK:>
Python Code:
import autofig
import numpy as np
#autofig.inline()
t = np.linspace(0,10,31)
x = np.random.rand(31)
y = np.random.rand(31)
z = np.random.rand(31)
autofig.reset()
autofig.plot(x, y, z, i=t,
xlabel='x', ylabel='y', zlabel='z')
mplfig = autofig.draw()
autofig.reset()
autofig.p... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: By default, autofig uses the z dimension just to assign z-order (so that positive z appears "on top")
Step2: To instead plot using a projected ... |
515 | <ASSISTANT_TASK:>
Python Code:
try:
import cirq
except ImportError:
print("installing cirq...")
!pip install --quiet cirq
print("installed cirq.")
import cirq
qubit = cirq.NamedQubit("myqubit")
# creates an equal superposition of |0> and |1> when simulated
circuit = cirq.Circuit(cirq.H(qubit))
# 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: A qubit is the basic unit of quantum information, a quantum bit
|
516 | <ASSISTANT_TASK:>
Python Code:
# numpy is generally imported as 'np'
import numpy as np
print(np)
print(np.__version__)
# an explicit list of numbers
anarray = np.array([2, 3, 5, 7, 11, 13, 17, 19, 23])
# an array of zeros of shape(3, 4)
zeroarray = np.zeros((3, 4))
# a range from 0 to n-1
rangearray = np.arange(12)
#... | <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: Documentation
Step2: Experiment
Step3: Exercise
Step4: You can also index multidimensional arrays in a logical way using an enhanced indexing... |
517 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import chap01soln
resp = chap01soln.ReadFemResp()
resp.columns
import thinkstats2
hist = thinkstats2.Hist(resp.totincr)
import thinkplot
thinkplot.Hist(hist, label='totincr')
thinkplot.Show()
hist = thinkstats2.Hist(resp.age_r)
thinkplot.Hist(hist, label='age_r')
thi... | <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: 응답자 가족에 대한 총소득 <tt>totincr</tt> 히스토그램을 생성하시오. 코드를 해석하기 위해서, codebook을 살펴보시오.
Step2: 히스토그램을 화면에 표시하시오.
Step3: 인터뷰 당시 응답자 나이 변수, <tt>age_r</tt>에... |
518 | <ASSISTANT_TASK:>
Python Code:
products = pd.read_csv('../../data/amazon_baby_subset.csv')
products['sentiment']
products['sentiment'].size
products.head(10).name
print ('# of positive reviews =', len(products[products['sentiment']==1]))
print ('# of negative reviews =', len(products[products['sentiment']==-1]))
# The ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Train-Validation split
Step2: Convert Frame to NumPy array
Step3: Building on logistic regression with no L2 penalty assignment
Step4: Adding... |
519 | <ASSISTANT_TASK:>
Python Code:
import os
import fiona
import matplotlib.pyplot as plt
from planet import api
import rasterio
from rasterio import features as rfeatures
from rasterio.enums import Resampling
from rasterio.plot import show
import shapely
from shapely.geometry import shape as sshape
# if your Planet API Ke... | <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: Obtain Analytics Raster
Step2: Download Quad Raster
Step3: We want to save each all of the images in one directory. But all of the images for ... |
520 | <ASSISTANT_TASK:>
Python Code:
import striplog
striplog.__version__
text = "wet silty fine sand with tr clay"
from striplog import Lexicon
lex_dict = {
'lithology': ['sand', 'clay'],
'grainsize': ['fine'],
'modifier': ['silty'],
'amount': ['trace'],
'moisture': ['wet', 'dry'],
'abbreviati... | <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 have some text
Step2: To read this with striplog, we need to define a Lexicon. This is a dictionary-like object full of regular expressions,... |
521 | <ASSISTANT_TASK:>
Python Code:
labVersion = 'cs190_week2_word_count_v_1_0'
wordsList = ['cat', 'elephant', 'rat', 'rat', 'cat']
wordsRDD = sc.parallelize(wordsList, 4)
# Print out the type of wordsRDD
print type(wordsRDD)
# TODO: Replace <FILL IN> with appropriate code
def makePlural(word):
Adds an 's' to `word`.... | <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: Part 1
Step3: (1b) Pluralize and test
Step4: (1c) Apply makePlural to the base RDD
Step5: (1d) Pass a lambda function to map
Step6: (1e) ... |
522 | <ASSISTANT_TASK:>
Python Code:
import csv
import re
with open('../data/bee_list.txt') as f:
csvr = csv.DictReader(f, delimiter = '\t')
species = []
authors = []
for r in csvr:
species.append(r['Scientific Name'])
authors.append(r['Taxon Author'])
len(species)
len(authors)
au = authors... | <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, we read the file, and store the columns Scientific Name and Taxon Author in two lists
Step2: How many species?
Step3: Pick one of the au... |
523 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
sigma = np.array([1/3, 1/2, 0, 0, 1/6])
np.where(sigma > 0) # Recall Python indexing starts at 0
sigma = np.array([0, 0, 1, 0])
np.where(sigma > 0) # Recall Python indexing starts at 0
A = np.array([[1, 1, 0], [2, 3, 0]])
sigma_c = np.array([0, 0, 1])
(np.dot(A, sigm... | <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: Definition of nondegenerate games
Step2: This leads to the following algorithm for identifying Nash equilibria
Step3: If you recall the degene... |
524 | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.append('C:\Anaconda2\envs\dato-env\Lib\site-packages')
import graphlab
sales = graphlab.SFrame('kc_house_data_small.gl/')
import numpy as np # note this allows us to refer to numpy as np instead
def get_numpy_data(data_sframe, features, output):
data_sframe['cons... | <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 in house sales data
Step2: Import useful functions from previous notebooks
Step3: We will also need the normalize_features() function fro... |
525 | <ASSISTANT_TASK:>
Python Code:
f = spot.formula('a U Gb')
a = f.translate('ba')
a
propset = spot.atomic_prop_collect_as_bdd(f, a)
ta = spot.tgba_to_ta(a, propset, True, True, False, False, True)
ta.show('.A')
ta = spot.tgba_to_ta(a, propset, True, True, False, False, False)
ta.show('.A')
spot.minimize_ta(ta).show('.... | <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, gather all the atomic proposition in the formula, and create an automaton with changesets
Step2: Then, remove dead states, and remove stu... |
526 | <ASSISTANT_TASK:>
Python Code:
import os
import numpy as np
from vertebratesLib import *
split = "SPLIT1"
summaryTree,summarySpecies,splitPositions = get_split_data(split)
print summaryTree.shape
def get_sentence(position,splitPositions,summary,ignore=False):
splitIndex = np.where(splitPositions==position)[0]
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: a sentence of words is represented as the transitions for a given position
Step2: Simple test run with lda package
Step3: Recall that the Diri... |
527 | <ASSISTANT_TASK:>
Python Code:
g.plot_reward(smoothing=100)
g.__class__ = KarpathyGame
np.set_printoptions(formatter={'float': (lambda x: '%.2f' % (x,))})
x = g.observe()
new_shape = (x[:-2].shape[0]//g.eye_observation_size, g.eye_observation_size)
print(x[:-4].reshape(new_shape))
print(x[-4:])
g.to_html()
%pwd
<END_... | <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: Visualizing what the agent is seeing
|
528 | <ASSISTANT_TASK:>
Python Code:
from numpy import *
v = array([1,2,3,4])
v
M = array([[1, 2], [3, 4]])
M
type(v), type(M)
v.shape
M.shape
v.size, M.size
shape(M)
size(M)
M.dtype
M[0,0] = "hello"
M[0,0]=5
M = array([[1, 2], [3, 4]], dtype=complex)
M
x = arange(0, 10, 1) # argumenti: početak, kraj, korak
x # 10 nij... | <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: Kreiranje nizova pomoću numpy modula
Step2: Možemo koristiti i funkcije numpy.shape, numpy.size
Step3: Koja je razlika između numpy.ndarray ti... |
529 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
pd.set_option('display.max_columns', 999)
import pandas.io.sql as psql
# plot a figure directly on Notebook
import matplotlib.pyplot as plt
%matplotlib inline
a = pd.read_csv("data/ADMISSIONS.csv")
a.columns = map(str.lower, a.columns)
a.groupby(['marital_status']).co... | <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: Agenda
Step2: Load the admissions table (2/3)
Step3: Profile the table (3/3)
Step4: Agenda
Step5: Agenda
Step6: Prepare an input text in st... |
530 | <ASSISTANT_TASK:>
Python Code:
import string
import pandas as pd
import numpy as np
import seaborn as sns
def get_random_numerical_data(size, *amplitudes):
n = len(amplitudes)
data = np.random.random((size, n)) * np.array(amplitudes).reshape(1, n)
return pd.DataFrame(data=data, columns=pd.Series(list(strin... | <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 some random data
Step2: Plotting all features directly with Seaborn
Step3: Changing the y-scale to log doesn't help much
Step5: Plotting ... |
531 | <ASSISTANT_TASK:>
Python Code:
# Ensure compatibility with Python 2 and 3
from __future__ import print_function, division
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import xarray as xr
import climlab
from climlab import constants as const
import cartopy.crs as ccrs # use cartopy to make so... | <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: Contents
Step2: Make two maps
Step3: Make a contour plot of the zonal mean temperature as a function of time
Step4: <a id='section2'></a>
Ste... |
532 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data_path = 'Bike-Sharing-Dataset/hour.csv'
rides = pd.read_csv(data_path)
rides.head()
rides[:24*10].plot(x='dteday', y='cnt')
dummy_fields = ['seas... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load and prepare the data
Step2: Checking out the data
Step3: Dummy variables
Step4: Scaling target variables
Step5: Splitting the data into... |
533 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import keras
from keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train.shape
import matplotlib.pyplot as plt
%matplotlib inline
randix = np.random.randint(0,60000)
plt.imshow(x_train[randix])
print("Label 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: Let us load up a sample dataset.
Step6: Now construct a KNN classifier
Step7: Calculate accuracy on this very small subset.
Step8: Let's time... |
534 | <ASSISTANT_TASK:>
Python Code:
import pyspark
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.classification import LogisticRegressionWithSGD
from pyspark.mllib.tree import DecisionTree
sc = pyspark.SparkContext()
raw_rdd = sc.textFile("datasets/COUNT/titanic.csv")
raw_rdd.count()
raw_rdd.tak... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: First we create a SparkContext, the main object in the Spark API. This call may take a few seconds to return as it fires up a JVM under the cove... |
535 | <ASSISTANT_TASK:>
Python Code:
stuff = {
'apple': 1.97,
'banana': 2.99,
'cherry': 3.99,
}
# Common pattern of .format use: use numerical indexes
for name, price in stuff.items():
print('The price of {0} is {1}.'.format(name, price))
# Common pattern of .format use: use parameter names
for name, price i... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: In 'The price of {0} is {1}' above,
Step2: Something that sucks about the above print,
Step3: It reminds me of shell syntax. For example,
Step... |
536 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
x = tf.Variable(0)
x.assign(114514)
<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:
|
537 | <ASSISTANT_TASK:>
Python Code:
# == Basic import == #
# No annoying warnings
import warnings
warnings.filterwarnings('ignore')
# plot within the notebook
%matplotlib inline
import numpy as np
from scipy import stats
import matplotlib.pyplot as mpl
def plot_guassians(loc=1, scale=2):
plot the pdf and the cdf of g... | <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: Reminder
Step3: => Probability of random drawing a point within given limits [a,b]= CDF[b] - CDF[a]
Step4: Note about error bars
Step5: let's... |
538 | <ASSISTANT_TASK:>
Python Code:
import pandas
tss = pandas.read_csv("NSQD_Res_TSS.csv")
medians = (
tss.groupby(by=['parameter', 'units', 'season'])
.median()['res']
.reset_index()
)
medians
index_cols = [
'epa_rain_zone', 'location_code', 'station_name', 'primary_landuse',
'start_date', '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: Compute the medians for each season without dropping duplicates
Step2: Compute the medians for each season after dropping duplicate records
|
539 | <ASSISTANT_TASK:>
Python Code:
import gym
import tensorflow as tf
import numpy as np
# Create the Cart-Pole game environment
env = gym.make('CartPole-v0')
env.reset()
rewards = []
for _ in range(100):
env.render()
state, reward, done, info = env.step(env.action_space.sample()) # take a random action
rewar... | <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: Note
Step2: We interact with the simulation through env. To show the simulation running, you can use env.render() to render one frame. Passing ... |
540 | <ASSISTANT_TASK:>
Python Code:
y = [2, 3, 1]
x = np.arange(len(y))
xlabel = ['A', 'B', 'C']
plt.bar(x, y, align='center') #보통은 이 명령어를 쳐야 가운데를 기준으로 x가 정렬, 설정 없으면 left가 디폴트
plt.xticks(x, xlabel);
people = ('Tom', 'Dick', 'Harry', 'Slim', 'Jim')
y_pos = np.arange(len(people))
performance = 3 + 10 * np.random.rand(len(pe... | <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: xerr 인수나 yerr 인수를 지정하면 에러 바(error bar)를 추가할 수 있다.
Step2: 두 개 이상의 바 차트를 한번에 그리는 경우도 있다.
Step3: 또는 bottom 인수로 바의 위치를 조정하여 겹친 바 차트(stacked bar ch... |
541 | <ASSISTANT_TASK:>
Python Code:
def area(p0, p1, p2):
"Calculate the area of a triangle given three points coordinates in the format (x, y)"
# Check if all the points have two coordinates
if len(p0) == 2 and len(p1) == 2 and len(p2) == 2:
return abs((p0[0]*(p1[1]-p2[1]) + p1[0]*(p2[1]-p0[1]) + p2[0]*... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step2: Exercise 06.2 (selecting data structures)
Step3: Exercise 06.3 (indexing)
Step4: Optional (advanced)
Step5: Exercise 06.4 (dictionaries)
Step... |
542 | <ASSISTANT_TASK:>
Python Code:
import importlib
autograd_available = True
# if automatic differentiation is available, use it
try:
import autograd
except ImportError:
autograd_available = False
pass
if autograd_available:
import autograd.numpy as np
from autograd import elementwise_grad as egrad... | <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: Specify the function to minimize as a simple python function.<br>
Step2: Plot the function and its derivative
Step3: Simple gradient descent s... |
543 | <ASSISTANT_TASK:>
Python Code:
from sklearn import datasets
import pandas as pd
from sklearn.datasets import load_digits
digits = load_digits() #dataset de clasificacion
brio= pd.read_csv('C:/Users/Alex/Documents/eafit/semestres/X semestre/programacion/briofitos.csv') #dataset regresion
#digits.DESCR
digits.target
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: Brio es un dataframe que entrega informacion de numero promedio y numero total de especies de briofitos que se encuentran a lo largo de un gradi... |
544 | <ASSISTANT_TASK:>
Python Code:
from dx import *
import seaborn as sns; sns.set()
# constant short rate
r = constant_short_rate('r', 0.02)
# market environments
me_gbm = market_environment('gbm', dt.datetime(2015, 1, 1))
me_jd = market_environment('jd', dt.datetime(2015, 1, 1))
me_sv = market_environment('sv', dt.date... | <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: Risk Factors
Step2: Three risk factors ares modeled
Step3: Assumptions for the geometric_brownian_motion object.
Step4: Assumptions for the j... |
545 | <ASSISTANT_TASK:>
Python Code:
from nltk.corpus import stopwords as nltk_stop_words
from nltk.corpus import words
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
#from sklearn.model_selection import cross_val_score
fr... | <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: Определим необходимые функции.
Step2: Обучение и классификация выбранным классификатором, сохранение результатов в файл с временной меткой в н... |
546 | <ASSISTANT_TASK:>
Python Code:
import os
import pandas as pd
from google.cloud import bigquery
PROJECT = !(gcloud config get-value core/project)
PROJECT = PROJECT[0]
BUCKET = PROJECT # defaults to PROJECT
REGION = "us-central1" # Replace with your REGION
os.environ["PROJECT"] = PROJECT
os.environ["BUCKET"] = BUCKET
... | <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 the variable values in the cell below. Note, AutoML can only be run in the regions where it is available.
Step2: Create a Dataset from ... |
547 | <ASSISTANT_TASK:>
Python Code:
import os
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG
! pip3 install -U google-cloud-storage $USER_FLAG
! pip3 install $USER_FLAG kfp go... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Install the latest GA version of google-cloud-storage library as well.
Step2: Install the latest GA version of google-cloud-pipeline-components... |
548 | <ASSISTANT_TASK:>
Python Code:
import os
import sys
import urllib2
import collections
import matplotlib.pyplot as plt
import math
from time import time, sleep
%pylab inline
spark_home = os.environ.get('SPARK_HOME', None)
if not spark_home:
raise ValueError("Please set SPARK_HOME environment variable!")
# Add 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: Prepare the pySpark Environment
Step2: Initialize Spark Context
Step3: Load and Analyse Data
Step4: Ratings Histogram
Step5: Most popular mo... |
549 | <ASSISTANT_TASK:>
Python Code:
## Import libraries necessary for monitor data processing. ##
from matplotlib import pyplot as plt
import numpy as np
import os
import pandas as pd
import pickle
from spins.invdes.problem_graph import log_tools
## Define filenames. ##
# `save_folder` is the full path to the directory cont... | <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: Option 1
Step2: Option 2
Step3: Option 3
|
550 | <ASSISTANT_TASK:>
Python Code:
import graphlab
products = graphlab.SFrame('amazon_baby_subset.gl/')
products.head()
products['sentiment']
products.head(10)['name']
print '# of positive reviews =', len(products[products['sentiment']==1])
print '# of negative reviews =', len(products[products['sentiment']==-1])
impor... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Load review dataset
Step2: One column of this dataset is 'sentiment', corresponding to the class label with +1 indicating a review with positiv... |
551 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import networkx as nx
Gu = nx.Graph()
for i, j in [(1, 2), (1, 4), (4, 2), (4, 3)]:
Gu.add_edge(i,j)
nx.draw(Gu, with_labels = True)
import networkx as nx
Gd = nx.DiGraph()
for i, j in [(1, 2), (1, 4), (4, 2), (4, 3)]:
Gd.add_edg... | <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: Directed
Step2: <img src = './img/networks.png' width = 1000>
Step3: Undirected network
Step4: Directed network
Step5: For a sample of N val... |
552 | <ASSISTANT_TASK:>
Python Code:
choice = raw_input("Choose option 1, 2, or 3: ") #prompts user to input something on the command line, saves it in a variable. see below!
if (choice == "1"):
print "You have chosen option 1: cake"
elif (choice == "2"):
print "You have chosen option 2: ice cream"
elif (choice == "3... | <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: Nested if/else
|
553 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
import numpy as np
import pandas as pd
def make_data(N, f=0.3, rseed=1087):
rand = np.random.RandomState(rseed)
x = rand.randn(N)
x[int(f*N):] += 5
return x
x = make_data(1000)
hist = plt... | <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: Motivation for KDE - Histograms
Step2: Standard count-based histogram can be viewed from the plt.hist() function. normed parameter of this func... |
554 | <ASSISTANT_TASK:>
Python Code:
def findSum(n , a , b ) :
sum = 0
for i in range(0 , n , 1 ) :
if(i % a == 0 or i % b == 0 ) :
sum += i
return sum
if __name__== ' __main __' :
n = 10
a = 3
b = 5
print(findSum(n , a , b ) )
<END_TASK>
| <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
555 | <ASSISTANT_TASK:>
Python Code:
from systemml import MLContext, dml, jvm_stdout
ml = MLContext(sc)
print (ml.buildTime())
prog =
holdOut = 1/3
kFolds = 1/holdOut
nRows = 6; nCols = 3;
X = matrix(seq(1, nRows * nCols), rows = nRows, cols = nCols) # X data
y = matrix(seq(1, nRows), rows = nRows, cols = 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:
Step2: Cross Validation<a id="CrossValidation" />
Step4: Value-based join of two Matrices<a id="JoinMatrices"/>
Step6: Filter Matrix to include only ... |
556 | <ASSISTANT_TASK:>
Python Code:
import datetime
print( "packages imported at " + str( datetime.datetime.now() ) )
%pwd
%ls
%run ../django_init.py
from context_text.models import Article
# how many articles in "grp_month"?
article_qs = Article.objects.filter( tags__name__in = [ "grp_month" ] )
grp_month_count = article... | <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 - virtualenv jupyter kernel
Step2: Data characterization
Step3: Reliability data creation - prelim_month
Step4: Example snapshot of con... |
557 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import pymc3 as pm
from pymc3.gp.util import plot_gp_dist
import theano.tensor as tt
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('dark')
seasonal_pitch_raw = pd.read_csv('../private_data/seasonal_pitch_data... | <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 import and cleaning
Step2: The data are messed up; name fields contain commas in a comma-separated file so two extra columns are created.
... |
558 | <ASSISTANT_TASK:>
Python Code:
# Ejemplo de lista, los valores van entre corchetes
una_lista = [4, "Hola", 6.0, 99 ]
# Ejemplo de tupla, los valores van entre paréntesis
una_tupla = (4, "Hola", 6.0, 99)
print ("Lista: " , una_lista)
print ("Tupla: " , una_tupla)
# Las tuplas y las listas aceptan operadores de comparaci... | <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: <br />
Step2: Los elementos de las secuencias, tanto listas como tuplas, son hetereogéneos, así que es posible definir listas que contienen val... |
559 | <ASSISTANT_TASK:>
Python Code:
import os
import datetime
import numpy
import scipy.signal
from astropy.io import fits
import matplotlib.pyplot as plt
import matplotlib.dates as md
%matplotlib inline
paths = ['/home/roman/mnt/server-space/storage/bolidozor/ZVPP/ZVPP-R6/snapshots/2017/09/']
times = numpy.ndarray((0,2))... | <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: SYSDATE1
Step2: Plotter
Step3: <br>
|
560 | <ASSISTANT_TASK:>
Python Code:
import boto3
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import io
import os
import sys
import time
import json
from IPython.display import display
from time import strftime, gmtime
import sagemaker
from sagemaker.predictor import csv_serializer
from sagemaker i... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Now let's set the S3 bucket and prefix that you want to use for training and model data. This bucket should be created within the same region as... |
561 | <ASSISTANT_TASK:>
Python Code:
import music21
from music21.chord import Chord
from music21.duration import Duration
from music21.instrument import Instrument
from music21.note import Note, Rest
from music21.stream import Stream
from music21.tempo import MetronomeMark
from music21.volume import Volume
import os
data_dir... | <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're about to create a Note object which represents a single note and both its pitch and duration.
Step2: If we have MuseScore installed, we c... |
562 | <ASSISTANT_TASK:>
Python Code:
range(0,10)
x =range(0,10)
type(x)
start = 0 #Default
stop = 20
x = range(start,stop)
x
x = range(start,stop,2)
#Show
x
for num in range(10):
print num
for num in xrange(10):
print num
<END_TASK> | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Great! Notice how it went up to 20, but doesn't actually produce 20. Just like in indexing. What about step size? We can specify that as a third... |
563 | <ASSISTANT_TASK:>
Python Code:
import graphlab
graphlab.canvas.set_target("ipynb")
sf = graphlab.SFrame.read_csv("/Users/chengjun/bigdata/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['bow'] = bow
type(sf['bo... | <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: Initializing from other models
Step6: Seeding the mo... |
564 | <ASSISTANT_TASK:>
Python Code:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# Import data
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_string('data_dir', '/tmp/data... | <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: Definition of the neural network
Step2: Train the network
Step3: Learning exercise
Step4: Extracting the test images and labels as numpy arra... |
565 | <ASSISTANT_TASK:>
Python Code:
import json
import urllib.request
from time import sleep
def search_magazine(key='JUMPrgl', n_pages=25):
「ユニークID」「雑誌巻号ID」あるいは「雑誌コード」にkey含む雑誌を，
n_pages分取得する関数です．
url = 'https://mediaarts-db.bunka.go.jp/mg/api/v1/results_magazines?id=' + \
key + '&page='
... | <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: 雑誌巻号検索結果の取得
Step3: Web APIでは，パラメータidで「ユニークID」「雑誌巻号ID」あるいは「雑誌コード」を，pageで検索結果の取得ページ番号（1ページあたり100件，デフォルトは1）を指定することができます．ここで，週刊少年ジャンプは「雑誌巻号ID」にJUMP... |
566 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data_path = 'Bike-Sharing-Dataset/hour.csv'
rides = pd.read_csv(data_path)
rides.head()
rides[:24*10].plot(x='dteday', y='cnt')
dummy_fields = ['seas... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Load and prepare the data
Step2: Checking out the data
Step3: Dummy variables
Step4: Scaling target variables
Step5: Splitting the data into... |
567 | <ASSISTANT_TASK:>
Python Code:
# Import numpy, pandas, linearsolve, scipy.optimize, matplotlib.pyplot
import numpy as np
import pandas as pd
import linearsolve as ls
from scipy.optimize import root,fsolve,broyden1,broyden2
import matplotlib.pyplot as plt
plt.style.use('classic')
%matplotlib inline
alpha = 0.36
beta = ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Set parameters
Step2: Compute exact steady state
Step3: Linear model
Step4: Nonlinear model
|
568 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import statsmodels.formula.api as smf
from pandas.io import wb
file1 = '/users/susan/desktop/PISA/PISA2012clean.csv' # file location
df1 = pd.read_csv(file1)
#pandas remote data access API for World... | <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: Creating the Dataset
Step2: Excluding Outliers
Step3: Plotting the Data
Step4: Regression Analysis
|
569 | <ASSISTANT_TASK:>
Python Code:
[telepyth]
token = 3916589616287113937
import telepyth
%telepyth 'line magic'
%%telepyth 'cell magic'
'some code here'
%telepyth
%%telepyth raise Exception('in title.')
raise Exception('in cell')
%%telepyth ' '.join(('Title', 'message'))
forty_two = '42'
pi = 3.1415926
int(forty_two)... | <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: Once telepyth package is imported, it tries to load settings from .telepythrc.
Step2: Actually telepyth provides both line magic and cell magic... |
570 | <ASSISTANT_TASK:>
Python Code:
# from kmapper import jupyter
import kmapper as km
import numpy as np
from sklearn.datasets import fetch_20newsgroups
from sklearn.cluster import AgglomerativeClustering
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import TruncatedSVD
from sklearn... | <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: Projection
Step3: Mapping
Step4: Interpretable inverse X
Step5: Visualization
|
571 | <ASSISTANT_TASK:>
Python Code:
get_ipython().magic('load_ext autoreload')
get_ipython().magic('autoreload 2')
import logging
import matplotlib.pyplot as plt
import numpy as np
import os
import timeit
logging.basicConfig(format=
"%(relativeCreated)12d [%(filename)s:%(funcName)20s():%(lineno)s] ... | <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: Below is a function that will compute and apply the transformation and its inverse. The underlying noise model is scaled Poisson plus Gaussian, ... |
572 | <ASSISTANT_TASK:>
Python Code:
import re
# List of patterns to search for
patterns = [ 'term1', 'term2' ]
# Text to parse
text = 'This is a string with term1, but it does not have the other term.'
for pattern in patterns:
print 'Searching for "%s" in: \n"%s"' % (pattern, text),
#Check for match
if re.s... | <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 we've seen that re.search() will take the pattern, scan the text, and then returns a Match object. If no pattern is found, a None is returne... |
573 | <ASSISTANT_TASK:>
Python Code:
import sys
from packages.learntools.deep_learning.exercise_1 import load_my_image, apply_conv_to_image, show, print_hints
# Detects light vs. dark pixels:
horizontal_line_conv = [[1, 1],
[-1, -1]]
vertical_line_conv = [[-1, -1],
[1, 1]]
co... | <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 Convolution
Step2: Vertical Line Detector
Step3: Now create a list that contains your convolutions, then apply them to the image data
... |
574 | <ASSISTANT_TASK:>
Python Code:
#from IPython.core.display import display, HTML
#display(HTML("<style>.container { width:95% !important; }</style>"))
%%time
import pandas as pd
import functions as f
import list_builder as lb
%%time
%run build_program_files sample3
%%time
%run make_skeleton
%%time
%run standalone pre... | <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: RESTART kernel prior to running after any changes to spreadsheet input files...
Step2: build program files
Step3: generate skeleton
Step4: ca... |
575 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import GPyOpt
from numpy.random import seed
func = GPyOpt.objective_examples.experimentsNd.alpine1(input_dim=5)
mixed_domain =[{'name': 'var1', 'type': 'continuous', 'domain': (-5,5),'dimensionality': 3},
{'name': 'var2', 'type': 'discrete', 'domain': (3,8,... | <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 we define the domain of the function to optimize as usual.
Step2: Now, we run the optimization for 20 iterations or a maximum of 60 seconds... |
576 | <ASSISTANT_TASK:>
Python Code:
!pwd
dbf_path = ps.examples.get_path('NAT.dbf')
print(dbf_path)
csv_path = ps.examples.get_path('usjoin.csv')
shp_path = ps.examples.get_path('NAT.shp')
print(shp_path)
f = ps.open(shp_path)
f.header
f.by_row(14) #gets the 14th shape from the file
all_polygons = f.read() #reads in a... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: PySAL has a command that it uses to get the paths of its example datasets. Let's work with a commonly-used dataset first.
Step2: For the purpos... |
577 | <ASSISTANT_TASK:>
Python Code:
# Lasagne is pre-release, so it's interface is changing.
# Whenever there's a backwards-incompatible change, a warning is raised.
# Let's ignore these for the course of the tutorial
import warnings
warnings.filterwarnings('ignore', module='lasagne')
import theano
import theano.tensor as 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: Toy example
Step2: Ingredients
Step3: DenseLayer
Step4: get_output
Step5: Tasting
Step6: Baking
Step7: Real-world example (MNIST ConvNet)
... |
578 | <ASSISTANT_TASK:>
Python Code:
import gammalib
import ctools
import cscripts
obsfile = 'obs_crab_selected.xml'
select = ctools.ctselect()
select['usethres'] = 'DEFAULT'
select['inobs'] = '$HESSDATA/obs/obs_crab.xml'
select['emin'] = 'INDEF' # no manual energy selection
select['rad'] = 2 # by default selec... | <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 first step of your analysis consists in selecting the relevant events from the observations. In this step you can select a specific energy r... |
579 | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.append('..')
from twords.twords import Twords
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
# this pandas line makes the dataframe display all text in a line; useful for seeing entire tweets
pd.set_option('display.max_colwidth', -1)
twit_mars ... | <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: Collect Tweets by search term
Step2: Collect Tweets from user
Step3: If you want to sort the tweets by retweets or favorites, you'll need to c... |
580 | <ASSISTANT_TASK:>
Python Code:
# Data for manual OHE
# Note: the first data point does not include any value for the optional third feature
#from pyspark import SparkContext
#sc =SparkContext()
sampleOne = [(0, 'mouse'), (1, 'black')]
sampleTwo = [(0, 'cat'), (1, 'tabby'), (2, 'mouse')]
sampleThree = [(0, 'bear'), (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: (1b) Vetores Esparsos
Step2: (1c) Atributos OHE como vetores esparsos
Step4: (1d) Função de codificação OHE
Step5: (1e) Aplicar OHE em uma... |
581 | <ASSISTANT_TASK:>
Python Code:
class DoppelDict(dict):
def __setitem__(self, key, value):
super().__setitem__(key, [value] * 2)
dd = DoppelDict(one=1)
dd # 继承 dict 的 __init__ 方法忽略了我们覆盖的 __setitem__方法，'one' 值没有重复
dd['two'] = 2 # `[]` 运算符会调用我们覆盖的 __setitem__ 方法
dd
dd.update(three=3) #继承自 dict 的 update 方法也不会调用... | <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: 原生类型的这种行为违背了面向对象编程的一个基本原则：始终应该从实例（self）所属的类开始搜索方法，即使在超类实现的类中调用也是如此。在这种糟糕的局面中，__missing__ 却能按照预期工作（3.4 节），但这是特例
Step2: 直接子类化内置类型（如 dict，list，str... |
582 | <ASSISTANT_TASK:>
Python Code:
# Import deriva modules and pandas DataFrame (for use in examples only)
from deriva.core import ErmrestCatalog, get_credential
from pandas import DataFrame
# Connect with the deriva catalog
protocol = 'https'
hostname = 'www.facebase.org'
catalog_number = 1
credential = None
# If you need... | <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: Implicit DataPaths
Step2: DataPath-like methods
Step3: It is important to remember that the attributes(...) method returns a result set based ... |
583 | <ASSISTANT_TASK:>
Python Code:
# Authors: Olaf Hauk <olaf.hauk@mrc-cbu.cam.ac.uk>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
import mne
from mne.datasets import sample
from mne.minimum_norm import (make_inverse_resolution_matrix, get_cross_talk,
... | <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
Step2: CTF
|
584 | <ASSISTANT_TASK:>
Python Code:
%config InlineBackend.figure_format = "retina"
from matplotlib import rcParams
rcParams["savefig.dpi"] = 100
rcParams["figure.dpi"] = 100
rcParams["font.size"] = 20
import numpy as np
def log_prob(x, mu, cov):
diff = x - mu
return -0.5 * np.dot(diff, np.linalg.solve(cov, diff))
... | <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 easiest way to get started with using emcee is to use it for a project. To get you started, here’s an annotated, fully-functional example th... |
585 | <ASSISTANT_TASK:>
Python Code:
## from __future__ import print_function # uncomment if using python 2
from os.path import join
import pandas as pd
import numpy as np
from datetime import datetime
%matplotlib inline
url = 'http://casas.wsu.edu/datasets/twor.2009.zip'
zipfile = url.split('/')[-1]
dirname = '.'.join(zi... | <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 up various parameters and variables that will be used in this script
Step2: Download the dataset, and unzip it using the following commands... |
586 | <ASSISTANT_TASK:>
Python Code:
!pip install -q git+https://github.com/tensorflow/docs
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow_docs.vis import embed
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
import imageio
batch_size = 64
num_channels = 1
num_cl... | <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: Imports
Step2: Constants and hyperparameters
Step3: Loading the MNIST dataset and preprocessing it
Step4: Calculating the number of input cha... |
587 | <ASSISTANT_TASK:>
Python Code:
import matplotlib as mpl
mpl
# I normally prototype my code in an editor + ipy terminal.
# In those cases I import pyplot and numpy via
import matplotlib.pyplot as plt
import numpy as np
# In Jupy notebooks we've got magic functions and pylab gives you pyplot as plt and numpy as np
# %pyl... | <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: Where's the plot to this story?
Step2: Interactive mode on or off is a preference. See how it works for your workflow.
Step3: Some Simple Plot... |
588 | <ASSISTANT_TASK:>
Python Code:
# Required imports and setup
from rootpy.plotting import Hist, Canvas, set_style
import rootpy.plotting.root2matplotlib as rplt
from root_numpy import array2hist
from IPython.parallel import Client
client = Client('ipcontroller-client.json', sshserver="--redacted--.unimelb.edu.au")
set_st... | <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: Write the analysis code
Step2: Execute the analysis on a single local core
Step3: That took about 1.5 minutes... Looking at the plot of the di... |
589 | <ASSISTANT_TASK:>
Python Code:
from pynq import Overlay
from pynq.iop import Pmod_OLED
from pynq.iop import PMODA
ol = Overlay("base.bit")
ol.download()
pmod_oled = Pmod_OLED(PMODA)
pmod_oled.clear()
pmod_oled.write('Welcome to the\nPynq-Z1 board!')
pmod_oled.clear()
pmod_oled.write('Python and Zynq\nproductivity & p... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: You should now see the text output on the OLED, so let's try another message
Step2: Finally, capture some text from IPython shell calls and pri... |
590 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import numpy as np
import matplotlib.pyplot as plt
#Import the curve fitter from the scipy optimize package
from scipy.optimize import curve_fit
#create the data to be plotted
x = np.linspace(0, 2*np.pi, 300)
y = np.sin(x)
#Now plot it
plt.plot(x,y,'b--')
plt.plot(x[110:18... | <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 an array of points that represent a sine curve between 0 and 2$\pi$.
Step2: Plot the data over the full range as a dashed line and then ... |
591 | <ASSISTANT_TASK:>
Python Code:
def aquire_audio_data():
D, T = 4, 10000
y = np.random.normal(size=(D, T))
return y
y = aquire_audio_data()
start = time.perf_counter()
x = wpe(y)
end = time.perf_counter()
print(f"Time: {end-start}")
channels = 8
sampling_rate = 16000
delay = 3
iterations = 5
taps = 10
file... | <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 with real audio recordings
Step2: Audio data
Step3: iterative WPE
Step4: Power spectrum
|
592 | <ASSISTANT_TASK:>
Python Code:
try:
from google.cloud import aiplatform
except ImportError:
!pip3 install -U google-cloud-aiplatform --user
print("Please restart the kernel and re-run the notebook.")
import os
import shutil
import pandas as pd
import tensorflow as tf
from datetime import datetime
from matp... | <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: If the above command resulted in an installation, please restart the notebook kernel and re-run the notebook.
Step2: Load raw data
Step3: Use ... |
593 | <ASSISTANT_TASK:>
Python Code:
f = numpy.exp(1)
f_hat = 2.71
# Error
print "Absolute Error = ", numpy.abs(f - f_hat)
print "Relative Error = ", numpy.abs(f - f_hat) / numpy.abs(f)
# Precision
p = 3
n = numpy.floor(numpy.log10(f_hat)) + 1 - p
print "%s = %s" % (f_hat, numpy.round(10**(-n) * f_hat) * 10**n)
import sympy... | <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: Truncation Error and Taylor's Theorem
Step2: Lets plot this numerically for a section of $x$.
Step3: Example 2
Step5: Symbols and Definitions... |
594 | <ASSISTANT_TASK:>
Python Code:
import modeled.netconf
modeled.netconf.__requires__
import modeled
from modeled import member
class Input(modeled.object):
The input part of a Turing Machine program rule.
state = member[int]()
symbol = member[str]()
class Output(modeled.object):
The output part of a... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step7: To install in development mode
Step8: To check if the Turing Machine works, it needs an actual program.
Step9: Instantiate the Turing Machine ... |
595 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pycuda.gpuarray as gpuarray
from pycuda.curandom import rand as curand
from pycuda.compiler import SourceModule
import pycuda.driver as cuda
try:
ctx.pop()
ctx.detach()
except:
print ("No CTX!")
cuda.... | <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: Jądro Elementwise
Step3: Algorytm z pętlą wewnątrz jądra CUDA
Step4: Porównanie z wersją CPU
Step5: Wizualizacja wyników
|
596 | <ASSISTANT_TASK:>
Python Code:
mean = np.array([0.05/252 + 0.02/252, 0.03/252 + 0.02/252])
volatility = np.array([0.2/np.sqrt(252), 0.05/np.sqrt(252)])
variance = np.power(volatility,2)
correlation = np.array(
[
[1, 0.25],
[0.25,1]
]
)
covariance = np.zeros((2,2))
for i in range(len(variance)):
... | <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: Build and run ERC Strategy
|
597 | <ASSISTANT_TASK:>
Python Code:
baselines= Unique cookies to view page per day: 40000
Unique cookies to click "Start free trial" per day: 3200
Enrollments per day: 660
Click-through-probability on "Start free trial": 0.08
Probability of enrolling, given click: 0.20625
Probability of payment, given enroll: 0.53
Probabili... | <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: Overview
Step2: Since we have 5000 sample cookies instead of the original 40000, we can adjust accordingly using calculate probability. For the... |
598 | <ASSISTANT_TASK:>
Python Code:
offset = [-190., -47.]*u.arcsec
for ind, orbit in enumerate(orbits):
midTime = (0.5*(orbit[1] - orbit[0]) + orbit[0])
sky_pos = planning.get_skyfield_position(midTime, offset, load_path='./data', parallax_correction=True)
print("Orbit: {}".format(ind))
print("Orbit start:... | <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: Loop over each orbit and correct the pointing for the same heliocentric pointing position.
|
599 | <ASSISTANT_TASK:>
Python Code:
import phoebe
from phoebe import u
b = phoebe.default_binary()
print(b.filter(qualifier='teff'))
lhs = b.get_parameter(qualifier='teff', component='secondary')
rhs = 0.5 * b.get_parameter(qualifier='teff', component='primary')
rhs
b.add_constraint(lhs, rhs)
print(b.filter(qualifier='teff... | <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: In this case, the two positional arguments to b.add_constraint must be the left-hand side of the expression (which will become the constrained p... |
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