Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
|---|---|---|
10,800 | <ASSISTANT_TASK:>
Python Code:
# import the required libraries
import numpy as np
import time
import random
import cPickle
import codecs
import collections
import os
import math
import json
import tensorflow as tf
from six.moves import xrange
# libraries required for visualisation:
from IPython.display import SVG, disp... | <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: Encode and Decode Sample Drawings
Step2: Temperature Interpolation
Step3: Latent Space Interpolation
Stepping through latent space between two... |
10,801 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
df = pd.read_csv('data/mlb.csv')
# use head to check it out
df.head()
df.count()
df.SALARY.sum()
df.TEAM.unique()
df[['TEAM', 'SALARY']].groupby('TEAM') \
.sum() \
.reset_index() \
.set_index('TEAM'... | <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 in the data into a data frame
Step2: How many players?
Step3: Total MLB payroll
Step4: Get a list of teams
Step5: Total payroll by team... |
10,802 | <ASSISTANT_TASK:>
Python Code:
%%bash
sudo pip freeze | grep google-cloud-bigquery==1.6.1 || \
sudo pip install google-cloud-bigquery==1.6.1
from google.cloud import bigquery
query =
SELECT
weight_pounds,
is_male,
mother_age,
plurality,
gestation_weeks,
FARM_FINGERPRINT(
CONCAT(
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: The source dataset
Step3: Let's create a BigQuery client that we can use throughout the notebook.
Step4: Let's now examine the result of a Biq... |
10,803 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
def is_int(value):
try:
int(value)
return True
except ValueError:
return False
def is_float(value):
try:
float(value)
return True
except ValueError:
return ... | <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: Utility functions
Step2: Read and clean SPINE data
Step3: Read and clean census data
Step4: Read and clean workforce data
Step5: Read and cl... |
10,804 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (12, 8)
# Load the train dataset
train = pd.read_csv("../Data/train.csv")
# Load the test dataset
test = 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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Description:
Step1: Attribute Information
Step2: Find basic summary metrics for the train dataframe
Step3: *Where did the remaining columns go? *
Step4: Bi-varia... |
10,805 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
from sklearn.datasets import load_boston
boston = load_boston()
dataset = pd.DataFrame(boston.data, columns=boston.feature_names)
dataset['target'] = boston.target
observations = len(dataset)
variables = dataset.columns[:-1]
X = dataset.ix[:,:-1]
y = dataset['target']... | <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: Cross validation
Step3: Valid options are ['accuracy', 'adjusted_rand_score', 'average_precision', 'f1', 'f1_macro', 'f1_micro', 'f1_samples', ... |
10,806 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
view_sentence_range = (0, 10)
DON'T MODIFY AN... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Language Translation
Step3: Explore the Data
Step6: Implement Preprocessing Function
Step8: Preprocess all the data and save it
Step10: Chec... |
10,807 | <ASSISTANT_TASK:>
Python Code:
%reset
# Import the required modules
import pandas as pd
import numpy as np
import scipy as sp
# simple function to read in the user data file.
# the argument parse_dates takes in a list of colums, which are to be parsed as date format
user_data_raw_csv = pd.read_csv("/home/eyebell/local_... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Data Clean up
Step2: As is visible from the last column (age_on_platform) data type, Pandas is not recognising it as date type format.
Step3: ... |
10,808 | <ASSISTANT_TASK:>
Python Code:
import os
GCP_PROJECT = 'dp-workspace'
GCS_BUCKET = 'gs://cluster19-bkt'
GCS_REGION = 'us-central1'
os.putenv("REGION", GCS_REGION)
TF_RECORD_SEQEX = GCS_BUCKET+'/synthea/serv/seqex*'
os.putenv("SEQEX_IN_GCS", TF_RECORD_SEQEX)
MODEL_PATH = GCS_BUCKET+'/synthea/model/'
os.putenv("MODEL_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: <b>Import dependencies. </b>
Step2: <b>Optionally, enable logging for debugging.</b>
Step3: <b> Previous step saved Sequence Examples into GCS... |
10,809 | <ASSISTANT_TASK:>
Python Code:
from k2datascience import movies
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
%matplotlib inline
mov = movies.BoxOffice()
print(f'Data Types:\n{mov.data.dtypes}\n\n')
print(f'Data Shape:\n{mov.data.shape}\n\n')
print(f'Missing... | <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: Data Prep
Step2: Exercise 1
Step3: Y-Axis Labels
Step4: Benefits and Liabilities
Step5: Findings
Step6: Findings
Step7: Exercise 6
|
10,810 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import os, sys, time
import pickle as pkl
import numpy as np
import pandas as pd
import sklearn as sk
import cython
import itertools
from scipy.io import arff
from scipy.optimize import minimize
from scipy.optimize import check_grad
from sklearn.linear_model import Logi... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Data loading
Step2: Features
Step3: Labels
Step4: Data analysis
Step5: Histogram of #positive labels.
Step8: Dataset creation
Step9: Evalu... |
10,811 | <ASSISTANT_TASK:>
Python Code:
# install Pint if necessary
try:
import pint
except ImportError:
!pip install pint
# download modsim.py if necessary
from os.path import exists
filename = 'modsim.py'
if not exists(filename):
from urllib.request import urlretrieve
url = 'https://raw.githubusercontent.com/A... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Case studies
Step2: Select tables[1], which is the second table on the page.
Step3: Not all researchers provide estimates for the same dates.
... |
10,812 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from contact_map import ContactTrajectory, RollingContactFrequency
import mdtraj as md
traj = md.load("data/gsk3b_example.h5")
print(traj) # to see number of frames; size of system... | <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'll use MDTraj's atom selection language to split out the protein and the ligand, which has residue name YYG in the input files. We're ... |
10,813 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
x = np.array([0, 1, 1, 1, 3, 1, 5, 5, 5])
y = np.array([0, 2, 3, 4, 2, 4, 3, 4, 5])
a = 1
b = 4
idx_list = ((x == a) & (y == b))
result = idx_list.nonzero()[0]
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
|
10,814 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import pickle
import statsmodels.api as sm
from sklearn import cluster
import matplotlib.pyplot as plt
%matplotlib inline
from bs4 import BeautifulSoup as bs
import requests
import time
# from ggplot import *
base_url = "http://www.mywebsite.com/dat... | <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: Other Useful Packages (not used today)
Step2: Prepare another URL on your own
Step3: Where did the ? go?
Step4: View Returned Data
Step5: Et... |
10,815 | <ASSISTANT_TASK:>
Python Code:
dataset = Dataset(config=dict(dataset_name='MNIST', data_dir='~/nta/datasets',
batch_size_train=256, batch_size_test=1024))
# torch cross_entropy is log softmax activation + negative log likelihood
loss_func = F.cross_entropy
# a custom Lambda module
class Lam... | <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: Extend for the k-Winners simulation
Step2: Model accuracy with 1% of active neurons, with and without boosting
Step3: Extend to CNNs
Step4: E... |
10,816 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
a = np.arange(5)
a
a.size
a.shape
a[0]
a[-1]
a[2:4]
a[2:]
a[:4]
a[:]
a[1::2]
a.max()
a.min()
a.sum()
a + 3
a * 5
a + 3 * 5
a + 15
(a + 3) * 5
a * 5 + 3
a * (5 + 3)
%matplotlib inline
import matplotlib.pyplot as plt
a = np.linspace(-np.pi, np.pi, 100)
plt.plot(a)
plt.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: Indexing
Step2: Play
Step3: Dimensions
Step4: Indexing
Step5: Play
Step6: ... have a look at y and predict what the next expression will ou... |
10,817 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
# RBP PWM's
from concise.data import attract
dfa = attract.get_metadata()
dfa
# TF PWM's
from concise.data import encode
dfe = encode.get_metadata()
dfe
# TF PWM's
from concise.data import hocomoco
dfh = hocomoco.get_metadata()
dfh
dfa_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: Let's choose PUM2 PWM (RBP in Human)
Step2: Visualization - PWM class
Step3: We can select the PWM with id 129.
Step4: Initialize the conv fi... |
10,818 | <ASSISTANT_TASK:>
Python Code:
import matplotlib
import warnings
warnings.filterwarnings("ignore", category=matplotlib.cbook.MatplotlibDeprecationWarning)
%matplotlib inline
# The archive of data on S3 URL did not work for me, despite .edu domain
#url = 'http://thredds-aws.unidata.ucar.edu/thredds/radarServer/nexrad/l... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: First we'll create an instance of RadarServer to point to the appropriate radar server access URL.
Step2: Next, we'll create a new query object... |
10,819 | <ASSISTANT_TASK:>
Python Code:
f = open('filespython.txt', 'r')
for line in f:
print(line)
f.close()
f = open('filespython.txt', 'w')
f.write('This is another line\n')
f.close()
f = open('filespython.txt', 'r')
print(f.read())
f.close()
f = open('filespython.txt', 'a')
f.write('This is another line\n')
f.clo... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: The output of each iteration of the for-loop is a string which contains the entire line of the file.
Step2: Notice that all of the file content... |
10,820 | <ASSISTANT_TASK:>
Python Code:
data_in_shape = (6, 6, 3)
L = AveragePooling2D(pool_size=(2, 2), strides=None, padding='valid', data_format='channels_last')
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
np.r... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: [pooling.AveragePooling2D.1] input 6x6x3, pool_size=(2, 2), strides=(1, 1), padding='valid', data_format='channels_last'
Step2: [pooling.Averag... |
10,821 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import nltk
from nltk.corpus import stopwords
from nltk.stem import SnowballStemmer
from collections import Counter
bloboftext =
This little piggy went to market,
This little piggy stayed home,
This little piggy had roast beef,
This little piggy had none,
And this li... | <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: Input
Step3: Workflow
Step4: About stemmers and lemmatisation
Step5: Count & POS tag of each stemmed/non-stop word
Step6: Proportion of POS ... |
10,822 | <ASSISTANT_TASK:>
Python Code:
!ffmpeg -i LlamaDrama.mp4 -movflags faststart -b:v 256000 -maxrate 256000 -x264opts "fps=24:keyint=48:min-keyint=48:no-scenecut" -hls_list_size 0 -hls_time 4 -hls_base_url http://192.168.3.14:8000/low/ low/LlamaDrama.m3u8
!ffmpeg -i LlamaDrama.mp4 -movflags faststart -b:v 512000 -maxrate... | <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: Llama Drama Low (1920x1080)
Step2: Llama Drama Medium (1920x1080)
Step3: Llama Drama High (1920x1080)
Step4: Playlist
Step5: StreamEngine
St... |
10,823 | <ASSISTANT_TASK:>
Python Code:
'eggs' < 'spam'
8 in (1,8,7,3,9,'spam')
assert(8>5)
assert(8<5)
assert(8<5), 'wicked helpful assertion string'
def K2C(tK):
Convert degrees K to C.
Argument: tK is a temperature in degrees K.
Precondition: (tK > 0)
assert tK > 0, 'tK is negative'
return ... | <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: $...$ or a membership test and the like.
Step2: Assertions, preconditions, postconditions
Step3: If an assertion is true, nothing happens. All... |
10,824 | <ASSISTANT_TASK:>
Python Code:
# If we're running on Colab, install empiricaldist
# https://pypi.org/project/empiricaldist/
import sys
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
!pip install empiricaldist
# Get utils.py
from os.path import basename, exists
def download(url):
filename = basename(url)
... | <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 previous chapter we used Bayes's Theorem to solve a cookie problem; then we solved it again using a Bayes table.
Step2: If that doesn't ... |
10,825 | <ASSISTANT_TASK:>
Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
l = [ 4, 3, 0, 2, 1 ]
i = 0
while l[i] != 0 :
i = l[i]
print (i) # que vaut l[i] à la fin ?
from IPython.display import Image
Image("td2_1.png")
l = [ 3, 6, 2 , 7, 9 ]
x = 7
for i,v in enumerate(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: Partie 3
Step2: Cet exercice montre une façon curieuse de se déplacer dans un tableau puisqu'on commence à la première position puis on va la ... |
10,826 | <ASSISTANT_TASK:>
Python Code:
# Change these to try this notebook out
BUCKET = "cloud-training-demos-ml"
PROJECT = "cloud-training-demos"
REGION = "us-central1"
SEQ_LEN = 50
import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT
os.environ['REGION'] = REGION
os.environ['SEQ_LEN'] = str(SEQ_LEN)
os.env... | <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: <h3> Simulate some time-series data </h3>
Step2: <h3> Train model locally </h3>
Step3: <h3> Cloud ML Engine </h3>
Step4: Monitor training wit... |
10,827 | <ASSISTANT_TASK:>
Python Code:
# This will plot a simple scatter graph of points.
# The points will have all different sizes just for visual appearance, as well as varied colours
%matplotlib inline
# Import the required libraries
import numpy as np
import matplotlib.pyplot as plt
# Lets say we want to plot 50 points
N... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Source
Step2: 2. numpy.genfromtxt()
Step3: Note
Step4: Formatting
Step5: Try looking at the data from the 1960's to the present day. You can... |
10,828 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
# Needed to include the figures in this notebook, you can remove it
# to work with a normal script
import numpy as np
import csv
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsRegressor
from sklearn.preprocessing import StandardScaler
from ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 1. NUMPY
Step2: Define a new 3x2 array named my_array2 with [1, 2, 3] in the first row and [4,5,6] in the second.
Step3: There are a number o... |
10,829 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
a_true = 0.5
b_true = 2.0
c_true = -4.0
# YOUR CODE HERE
x = np.linspace(-5, 5, 30)
y = a_true*(x**2) + b_true*(x) + [c_true]*30 + 2*np.random.randn(30)
plt.scatter(x, y)
assert True # lea... | <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: Fitting a quadratic curve
Step2: First, generate a dataset using this model using these parameters and the following characteristics
Step3: No... |
10,830 | <ASSISTANT_TASK:>
Python Code:
from learntools.core import binder
binder.bind(globals())
from learntools.data_cleaning.ex1 import *
print("Setup Complete")
# modules we'll use
import pandas as pd
import numpy as np
# read in all our data
sf_permits = pd.read_csv("../input/building-permit-applications-data/Building_Per... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: 1) Take a first look at the data
Step2: Use the code cell below to print the first five rows of the sf_permits DataFrame.
Step3: Does the data... |
10,831 | <ASSISTANT_TASK:>
Python Code:
# Author: Denis A. Engemann <denis.engemann@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Jean-Remi King <jeanremi.king@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD-3-Clause
import numpy as np
import matplotlib.pyplot as 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: Set parameters
Step2: Compute inverse solution
Step3: Decoding in sensor space using a logistic regression
Step4: To investigate weights, we ... |
10,832 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
from classifiers import read_spam_data, transform_log, transform_binary
#from sklearn.linear_model import LogisticRegression # reference sklearn implementation
from classifiers import LogisticRegression
train_data, test_data = read... | <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 logistic regression, we let
Step2: The default settings work pretty well and give us a 7% misclassification rate. Let us try various transf... |
10,833 | <ASSISTANT_TASK:>
Python Code:
from imdb import IMDb
from datetime import datetime
from elasticsearch import Elasticsearch
es = Elasticsearch()
ia = IMDb()
listaPelis = ia.get_top250_movies()
listaPelis
for i in range(10,250):
peli = listaPelis[i]
peli2 = ia.get_movie(peli.movieID)
string = peli2.summary()... | <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: Sacar toda la info de una peli para poder meterla en un diccionario y usarla en ElasticSearch, indexandola (metodo todo en 1)
Step2: Pruebas
St... |
10,834 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Graph regularization for image classification using synthesized graphs
Step2: Dependencies and imports
Step3: Flowers dataset
Step4: After do... |
10,835 | <ASSISTANT_TASK:>
Python Code:
from IPython.core.display import Image, display
display(Image(url='images/taller-sept-27/fig-4-1-9.png'))
from IPython.core.display import Image, display
display(Image(url='images/taller-sept-27/fig-4-1-12.png'))
from IPython.core.display import Image, display
display(Image(url='images/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: Bogotá 20 de Noviembre de 2015
Step2: SIMULACION CON NIVEL=1
Step3: SIMULACION CON NIVEL=3
Step4: Simulacion del ejercicio 4.1.13
Step5: SIM... |
10,836 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
view_sentence_range = (0, 10)
DON'T MODIFY AN... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Language Translation
Step3: Explore the Data
Step6: Implement Preprocessing Function
Step8: Preprocess all the data and save it
Step10: Chec... |
10,837 | <ASSISTANT_TASK:>
Python Code:
# Perform standard imports
import spacy
nlp = spacy.load('en_core_web_sm')
# Import the displaCy library
from spacy import displacy
# Create a simple Doc object
doc = nlp(u"The quick brown fox jumped over the lazy dog's back.")
# Render the dependency parse immediately inside Jupyter:
dis... | <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 dependency parse shows the coarse POS tag for each token, as well as the dependency tag if given
Step2: Creating Visualizations Outside of ... |
10,838 | <ASSISTANT_TASK:>
Python Code:
def sumOfDigit(K ) :
sod = 0
while(K ) :
sod = sod + K % 10
K = K // 10
return sod
def totalNumbersWithSpecificDifference(N , diff ) :
low = 1
high = N
while(low <= high ) :
mid =(low + high ) // 2
if(mid - sumOfDigit(mid ) < diff ) :
low = mid + 1
els... | <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:
|
10,839 | <ASSISTANT_TASK:>
Python Code:
from database import Database
database = Database(
'<host name>',
'<database name>',
'<user name>',
'<password>',
'utf8mb4'
)
connection = database.connect_with_pymysql()
from preprocessor import Decoder, Cleaner
# decoder instance
decoder = Decoder()
if connecti... | <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 will import the questions from the database and clean those data. The cleaning includes the following steps
|
10,840 | <ASSISTANT_TASK:>
Python Code:
z = np.random.normal(0, 1, 500)
def r_scatter(xs, r):
Generate y-values for a scatter plot with correlation approximately r
return r*xs + (np.sqrt(1-r**2))*z
corr_opts = {
'aspect_ratio': 1,
'xlim': (-3.5, 3.5),
'ylim': (-3.5, 3.5),
}
nbi.scatter(np.random.no... | <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: Correlation
Step2: Calculating $r$
Step3: Based on the scatter diagram, we expect that $r$ will be positive but not equal to 1.
Step4: Step 1... |
10,841 | <ASSISTANT_TASK:>
Python Code:
import pixiedust
pixiedust.enableJobMonitor()
# @hidden_cell
# Enter your DashDB JDBC URL (e.g. 'jdbc:db2://dashdb-entry-yp-dal00-00.services.dal.bluemix.net:50000/BLUDB')
jdbcurl = 'jdbc:db2://...'
# Enter your DashDB user name (e.g. 'dash0815')
user = '...'
# Enter your DashDB password... | <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: Configure database connectivity
Step2: Load data from table
Step3: Explore the loaded data using PixieDust
|
10,842 | <ASSISTANT_TASK:>
Python Code:
message = "Hello world"
print ("My message is:", message)
message = "Hello world"
print ("My 1st message is:", message)
print ("My 2nd message is: %s" % message)
message = "Hello world"
print ("My 1st message is:", message, end=(". "))
print ("My 2nd message is: %s " % message)
Name = ... | <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: Vanilla use of print
Step2: The second print uses a “formatter” (%s, %r and %d)
Step3: Python adds a newline character (\n) after every print... |
10,843 | <ASSISTANT_TASK:>
Python Code:
def compute_cost_MSE(y, tx, beta):
compute the loss by mse.
e = y - tx.dot(beta)
mse = e.dot(e) / (2 * len(e))
return mse
def compute_cost_MAE(y, tx, w):
y = np.array(y)
return np.sum(abs(y - np.dot(tx, w))) / y.shape[0]
def least_squares(y, tx):
calculate the ... | <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:
Step2: Least squares and linear basis functions models
Step3: Load the data
Step6: Least squares with a linear basis function model
Step8: Let us pl... |
10,844 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cas', 'sandbox-1', 'landice')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "ema... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
10,845 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.sankey import Sankey
Sankey(flows=[0.25, 0.15, 0.60, -0.20, -0.15, -0.05, -0.50, -0.10],
labels=['', '', '', 'First', 'Second', 'Third', 'Fourth', 'Fifth'],
orientations=[-1, 1, 0, 1, 1, 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: Example 1 -- Mostly defaults
Step2: Example 2
Step3: Example 3
|
10,846 | <ASSISTANT_TASK:>
Python Code:
tah_cloveka = 'kámen'
tah_pocitace = 'papír'
if tah_cloveka == 'kámen' and tah_pocitace == 'kámen'or tah_cloveka == 'nůžky' and tah_pocitace == 'nůžky' or tah_cloveka == 'papír' and tah_pocitace == 'papír':
print('Plichta.')
elif tah_cloveka == 'kámen' and tah_pocitace == 'nůžky' or 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: Ano, je
Step2: Najdi chyby 1
Step3: Správné řešení
Step4: Najdu chybu 2
Step5: Šťastná - bohatá
Step6: Řešení 2
Step7: Řešení 3
|
10,847 | <ASSISTANT_TASK:>
Python Code:
# Authors: Mainak Jas <mainak.jas@telecom-paristech.fr>
# Eric Larson <larson.eric.d@gmail.com>
# Jaakko Leppakangas <jaeilepp@student.jyu.fi>
#
# License: BSD (3-clause)
import os.path as op
import pandas as pd
import numpy as np
import mne
from mne import combine_evoke... | <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 reduce memory consumption and running time, some of the steps are
Step2: The data was collected with a CTF 275 system at 2400 Hz and low-pas... |
10,848 | <ASSISTANT_TASK:>
Python Code:
# imports
import os
from pypit import traceslits
print(os.getenv('PYPIT_DEV'))
mstrace_root = os.getenv('PYPIT_DEV')+'Cooked/Trace/MasterTrace_KeckLRISr_150420_402'
Tslits = traceslits.TraceSlits.from_master_files(mstrace_root)
Tslits.show('edges')
# left edge, right edge, row on 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: To run this Notebook, you must have an environmental variable pointed at the PYPIT Development suite
Step2: Checking edges
Step3: PYPYIT Scrip... |
10,849 | <ASSISTANT_TASK:>
Python Code:
if 1:
used_tab = True
if used_tab:
used_spaces = True
print("Something Something")
a = 10
if a == 11:
a += "abc" # a (an int) + "abc" should be a type error!
10/0 # dividing by zero should raise a zero division error!
dave + ha... | <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: <img src= attachment
Step2: In the above code snippet we have three lines of error prone code; we cant add ints to strings and we cant divide b... |
10,850 | <ASSISTANT_TASK:>
Python Code:
# As usual, a bit of setup
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 cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from cs231n.solver impo... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Fully-Connected Neural Nets
Step4: Affine layer
Step5: Affine layer
Step6: ReLU layer
Step7: ReLU layer
Step8: "Sandwich" layers
Step9: Lo... |
10,851 | <ASSISTANT_TASK:>
Python Code:
# use the `empty_init` flag to create an empty object
hp = houseprint.Houseprint(empty_init=True)
hp
site1 = houseprint.Site(key="Your house name")
# consult the docs to see what fields are available!
# houseprint.Site??
# houseprint.Fluksometer??
# houseprint.Fluksosensor??
device1 = ho... | <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 sites, devices and sensors
Step2: Add everything together
Step3: Save Houseprint to file
Step4: Share this file with your friends!
|
10,852 | <ASSISTANT_TASK:>
Python Code:
# Read data and results from the Excel macro
in_xlsx = (r'C:\Data\James_Work\Staff\Heleen_d_W\ICP_Waters\TOC_Trends_Analysis_2015'
r'\Data\mk_sen_test_data.xlsx')
raw_df = pd.read_excel(in_xlsx, sheetname='input')
res_df = pd.read_excel(in_xlsx, sheetname='results')
raw_df
res_... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step3: 2. Statistical functions
Step4: 3. Perform comparison
Step5: And below is the output from the Excel macro for comparison.
Step6: My code give... |
10,853 | <ASSISTANT_TASK:>
Python Code:
referencia_inicial = 440.0 # Hz
frequencias = [] # Esta lista recebera todas as frequencias de uma escala
f = referencia_inicial
while len(frequencias) < 12:
if f > (referencia_inicial * 2):
f /= 2.
frequencias.append(f)
f *= (3/2.)
frequencias.sort()
print frequencias... | <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: Veja que há um fenômeno interessante que acontece. A nota que deveria ter frequência de 880.0 Hz (um intervalo de oitava em relação à referência... |
10,854 | <ASSISTANT_TASK:>
Python Code:
def isDivisible(n ) :
while n // 100 :
d = n % 10
n //= 10
n = abs(n -(d * 14 ) )
return(n % 47 == 0 )
if __name__== "__main __":
n = 59173
if(isDivisible(n ) ) :
print("Yes ")
else :
print("No ")
<END_TASK>
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Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
10,855 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 1
%matplotlib inline
from pystyl.corpus import Corpus
corpus = Corpus(language='en')
corpus.add_directory(directory='data/dummy')
ls data/dummy
print(corpus)
corpus.preprocess(alpha_only=True, lowercase=True)
print(corpus)
corpus.tokenize()
print(cor... | <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: Specifying a language such as 'en' (English) is optional. Adding texts from a directory to the corpus is easy
Step2: By default, this function ... |
10,856 | <ASSISTANT_TASK:>
Python Code:
from astropy.time import Time
import matplotlib.pyplot as plt
from poliastro.plotting import StaticOrbitPlotter
from poliastro.frames import Planes
from poliastro.bodies import Earth, Mars, Jupiter, Sun
from poliastro.twobody import Orbit
epoch = Time("2018-08-17 12:05:50", scale="tdb")
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: Here we get hold of the lines list from the OrbitPlotter.plot method this is a list of lines. The first is the orbit line. The second is the cur... |
10,857 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import simtk.openmm as mm
import simtk.openmm.app as app
import simtk.unit as unit
sys11 = mm.openmm.System()
sys12 = mm.openmm.System()
sys22 = mm.openmm.System()
sys00 = mm.openmm.System()
for sys in [sys11, sys12, sy... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Running a trajectory using just a single surface
Step2: Running a trajectory using MMST with no coupling
Step3: Running a trajectory with the ... |
10,858 | <ASSISTANT_TASK:>
Python Code:
train_data_files = ['data/train-data.csv']
test_data_files = ['data/test-data.csv']
model_name = 'clust-model-02'
resume = False
train = True
preprocess_features = False
extend_feature_colums = False
HEADER = ['key', 'x1', 'x2', 'x3', 'cluster']
HEADER_DEFAULTS = [[0], [0.0], [0.0], [0... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 1. Define Dataset Metadata
Step2: 2. Define Data Input Function
Step3: b. data pipeline input function
Step4: 3. Create Estimator
Step5: 4. ... |
10,859 | <ASSISTANT_TASK:>
Python Code:
# change these to try this notebook out
BUCKET = 'cloud-training-demos-ml'
PROJECT = 'cloud-training-demos'
REGION = 'us-central1'
import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT
os.environ['REGION'] = REGION
%%bash
if ! gsutil ls | grep -q gs://${BUCKET}/; then
... | <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: <h2> Create ML dataset by sampling using BigQuery </h2>
Step3: Lab Task #1
|
10,860 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
f = np.load('decay_osc.npz')
tdata = np.array(f['tdata'])
ydata = np.array(f['ydata'])
dy = np.array(f['dy'])
plt.figure(figsize=(8,6))
plt.errorbar(tdata, ydata, dy, fmt='.k', ecolor='ligh... | <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: Fitting a decaying oscillation
Step2: Now, using curve_fit to fit this model and determine the estimates and uncertainties for the parameters
|
10,861 | <ASSISTANT_TASK:>
Python Code:
import sys
import os
sys.path.insert(0,'..')
sys.path.insert(0,'../layeredneuralnetwork/')
from layered_neural_network import LayeredNeuralNetwork
input_dimension = 2
lnn = LayeredNeuralNetwork(input_dimension=input_dimension)
from school.binary import Binary
Binary.teach_and(lnn)
Binar... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Let us create an LNN with 2 dimension input.
Step2: Schooling
Step3: Looks like our LNN was a good student scoring perfect F1 score of 1.0 in ... |
10,862 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.cm as cmap
cm = cmap.inferno
import numpy as np
import scipy as sp
import theano
import theano.tensor as tt
import theano.tensor.nlinalg
import sys
sys.path.insert(0, "../../..")
import pymc3 as pm
np.random.seed(20090... | <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: Example 1
Step2: Since there isn't much data, there will likely be a lot of uncertainty in the hyperparameter values.
Step3: The results show ... |
10,863 | <ASSISTANT_TASK:>
Python Code:
# Preparations
import math
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import numpy as np
from scipy import stats
from scipy.optimize import curve_fit
import seaborn as sns
from IPython.display import Latex
import warnings
from PrettyTable imp... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Durchführung
Step2: Verwendete Messgeräte
Step3: Auswertung
Step4: Resonanzmethode
Step5: Gasgemische
Step6: Fehlerrechnung
Step7: Gasgemi... |
10,864 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 模型平均
Step2: 构建模型
Step3: 准备数据集
Step4: 我们在这里比较三个优化器：
Step5: MovingAverage 和 StocasticAverage 优化器均使用 ModelAverageCheckpoint。
Step6: 训练模型
Step7... |
10,865 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'noresm2-lmec', 'toplevel')
# 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: 2... |
10,866 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 训练后 float16 量化
Step2: 训练并导出模型
Step3: 在此示例中，您只对模型进行了一个周期的训练，因此只训练到约 96% 的准确率。
Step4: 将其写入 .tflite 文件：
Step5: 要改为在导出时将模型量化为 float16，首先将 optimi... |
10,867 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
pd.read_csv("../dataset/google_trends_datascience.csv", index_col=0).plot();
log = pd.read_csv("../dataset/git_log_intellij.csv.gz")
log.head()
log.info()
log['timestamp'] = pd.to_datetime(log['timestamp'])
log.head()
# use log['timestamp'].max(... | <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: Einführung in<br/> Software Analytics
Step2: "100" == max. Beliebtheit!
Step3: Wir sehen uns Basisinfos über den Datensatz an.
Step4: <b>1</b... |
10,868 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
view_sentence_range = (0, 10)
DON'T MODIFY ANYTHING IN THIS CELL
import num... | <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: TV Script Generation
Step3: Explore the Data
Step6: Implement Preprocessing Functions
Step9: Tokenize Punctuation
Step11: Preprocess all the... |
10,869 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# 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 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: Introduction to TensorFlow Part 2 - Debugging and Control Flow
Step2: What this notebook covers
Step3: tf.print
Step4: If you're using eager ... |
10,870 | <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
if os.environ["IS_TESTING"]:
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
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Description:
Step1: Install the latest GA version of google-cloud-storage library as well.
Step2: Restart the kernel
Step3: Before you begin
Step4: Region
Step5:... |
10,871 | <ASSISTANT_TASK:>
Python Code:
# Importation des librairies.
import numpy as np
import pandas as pd
import random as rd
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
# Lecture d'un data frame
vispremv = pd.read_ta... | <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: Vérifier ci-dessous que la plupart des variables ont deux versions, l'une quantitative et l'autre qualitative. La version en R de ce calepin com... |
10,872 | <ASSISTANT_TASK:>
Python Code:
# Demonstração algébrica, sem código.
# Desenho da parábola.
#
import math
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid.axislines import SubplotZero
def prep_axis():
Preparação dos eixos do gráfico
fig = plt.fig... | <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: Lista de Exercícios #2 de Computação Gráfica
Step2: Questão #2
Step3: Questão #3
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10,873 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
%matplotlib inline
from nn_src.imports import *
from nn_src.utils import get_datasets
#DATA_DIR = '/Users/stephanrasp/data/'
# DATA_DIR = '/scratch/srasp/ppnn_data/'
DATA_DIR = '/Volumes/SanDisk/data/ppnn_data/'
aux_train_set, aux_test_set = get_datasets... | <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: Feature importance from from linear model
Step2: Get random permutation scores and compute feature importance
Step3: With embeddings
Step4: G... |
10,874 | <ASSISTANT_TASK:>
Python Code:
import bruges as bg
m, top, base, ref = bg.models.wedge(width=120)
import matplotlib.pyplot as plt
plt.imshow(m)
plt.plot(top, 'r', lw=4)
plt.plot(base, 'r', lw=4)
plt.axvline(ref, c='k', ls='--')
plt.show()
import numpy as np
vps = np.array([2320, 2350, 2350])
vss = np.array([1150, 125... | <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 wedge function returns a tuple containing the model m, as well as the a top, base and ref denoting some key boundaries in the model. Let's p... |
10,875 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stas
%matplotlib inline
x = np.arange(0.01, 1, 0.01)
values = [(0.5, 0.5),(5, 1),(1, 3),(2, 2),(2, 5)]
for i, j in values:
y = stas.beta.pdf(x,i,j)
plt.plot(x,y)
plt.show()
md = []
mn = []
mo = []
kur = []
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: Ejercicio 2
|
10,876 | <ASSISTANT_TASK:>
Python Code:
gmms = [GMM(i).fit(X) for i in range(1,10)]
aics = [g.aic(X) for g in gmms]
bics = [g.bic(X) for g in gmms]
plt.plot(aics)
plt.plot(bics)
# Data x_i
x = np.linspace(-6,6,1000)
pdf = gmms[2].score_samples(x.reshape(-1,1))
plt.plot(np.linspace(-6,6,1000),np.exp(pdf[0]))
plt.hist(X,bins=... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Calculate the AIC and BIC for each of these 10 models, and find the best model.
Step2: Plot the AIC and BIC
Step3: Define your PDF by evenly d... |
10,877 | <ASSISTANT_TASK:>
Python Code:
# import libraries
import pandas as pd
import numpy as np
# data paths
xwalkPath = ''
blsPath = ''
# import list of 'technology intensive' occupations from Hecker (2005), Table 3
stemOcc = pd.read_csv(xwalkPath+'hecker2005_table3.txt')
stemOcc = stemOcc[['occupationcode']]
stemOcc.column... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Import list of 2000 SOC occupations identified in Hecker (2005) as technology intensive. These occupations are then concorded to 2010 SOC codes ... |
10,878 | <ASSISTANT_TASK:>
Python Code:
# Importing GemPy
import gempy as gp
# Importing aux libraries
from ipywidgets import interact
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
# Embedding matplotlib figures in the notebooks
%matplotlib qt5
geo_model = gp.create_model('Model1')
geo_mod... | <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: Initializing the model
Step2: GemPy core code is written in Python. However for efficiency (and other reasons) most of heavy computations happe... |
10,879 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import medfilt
import math
import gitInformation
from neo.io import NeuralynxIO
import sklearn
from scipy.interpolate import Rbf
import fastdtw
import time
%matplotlib inline
gitInformation.printInformation()
# Session ... | <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, save and filter all the data
Step2: Plot data and the thresholds
|
10,880 | <ASSISTANT_TASK:>
Python Code:
from keras.datasets import imdb
from keras.preprocessing import sequence
max_features = 10000 # number of words to consider as features
max_len = 500 # cut texts after this number of words (among top max_features most common words)
print('Loading data...')
(x_train, y_train), (x_test, y... | <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: 1D convnets are structured in the same way as their 2D counter-parts that you have used in Chapter 5
Step2: Here are our training and validatio... |
10,881 | <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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<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... |
10,882 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import os
dirPath = os.path.realpath('.')
fileName = 'assets/coolingExample.xlsx'
filePath = os.path.join(dirPath, fileName)
df = pd.read_excel(filePath,header=0)
df.head()
df[df.columns[0]]
try:
df[1]
except KeyError:
print("KeyError: 1 - not a valid key")... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Step 2
Step2: Step 3
Step3: Our data is now accessible by a key value. The keys are the column headers in the dataframe. In this example case,... |
10,883 | <ASSISTANT_TASK:>
Python Code:
import pygem as pg
params = pg.params.FFDParameters()
params.read_parameters(filename='../tests/test_datasets/parameters_test_ffd_iges.prm')
iges_handler = pg.igeshandler.IgesHandler()
mesh_points = iges_handler.parse('../tests/test_datasets/test_pipe.iges')
free_form = pg.freeform.FFD(... | <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 have to load the iges file on which we will perform the FFD. Since it is an iges we use the IgesHandler() class. The parse method extract... |
10,884 | <ASSISTANT_TASK:>
Python Code:
import os, lzma
#basedir = os.path.join("/media/disk", "OSM_Data")
basedir = os.path.join("e:\\", "OSM_Data")
filename = "isle-of-wight-latest.osm.xz"
with lzma.open(os.path.join(basedir, filename), mode="rt", encoding="utf-8") as f:
print(next(f), end="")
print(next(f), 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: Look at the generated data
Step2: Parse the data in a reduced way
Step3: Use xml.etree instead
Step4: Via a callback
Step5: Convert the call... |
10,885 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer, StandardScaler
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
stats = ... | <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: Data preparation
Step2: The file Seasons_Stats.csv contains the statics of all players since 1950. First, we drop a couple of blank columns, an... |
10,886 | <ASSISTANT_TASK:>
Python Code:
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(min_df=1)
vectorizer.fit([
"The cat sat on the mat.",
])
vectorizer.vocabulary_
X = vectorizer.transform([
"The cat sat on the mat.",
"This cat is a nice cat.",
]).toarray()
print(len(vec... | <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 vocabulary is used at transform time to build the occurrence matrix
Step2: Let's refit with a slightly larger corpus
Step3: The vocabulary... |
10,887 | <ASSISTANT_TASK:>
Python Code:
from bsym import SymmetryOperation
SymmetryOperation([[ 1, 0, 0 ],
[ 0, 1, 0 ],
[ 0, 0, 1 ]])
SymmetryOperation([[ 1, 0, 0 ],
[ 0, 1, 0 ],
[ 0, 0, 1 ]], label='E' )
e = SymmetryOperation([[ 1, 0, 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: Each SymmetryOperation has an optional label attribute. This can be set at records the matrix representation of the symmetry operation and an op... |
10,888 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from __future__ import print_function
import tellurium as te
# to get the tellurium version use
print('te.__version__')
print(te.__version__)
# or
print('te.getTelluriumVersion()')
print(te.getTelluriumVersion())
# to print the full version info use
print('-' * 80)
te.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:
Step2: Repeat simulation without notification
Step3: File helpers for reading and writing
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10,889 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
from skimage import data, io, segmentation, color
from skimage.future import graph
%matplotlib inline
import requests
from PIL import Image
from io import StringIO
url = 'https://mycarta.files.wordpress.com/2015/04/jet_tight.png'
r = req... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Import test image. The colormap is Matlab's Jet
Step2: Reduce number of colours
Step3: Convert from RGB to HSL, get unique values of H, S, an... |
10,890 | <ASSISTANT_TASK:>
Python Code:
from datasets import *
from qiskit_aqua.utils import split_dataset_to_data_and_labels
from qiskit_aqua.input import get_input_instance
from qiskit_aqua import run_algorithm
import numpy as np
n = 2 # dimension of each data point
sample_Total, training_input, test_input, class_labels = W... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Here we choose the Wine dataset which has 3 classes.
Step2: Now we setup an Aqua configuration dictionary to use the classical SVM algorithm an... |
10,891 | <ASSISTANT_TASK:>
Python Code:
import rebound
sim = rebound.Simulation()
sim.add(m=1.) # Star
sim.add(m=1e-3, a=1) # Jupiter mass planet
sim.move_to_com()
sim.integrator = "whfast"
sim.dt = sim.particles[1].P/34.5678 # About 30 steps per orbit
import reboundx
rebx = reboundx.Extras(sim)
sto = rebx.load_force("stocha... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: We will be using the WHFast integrator with a fixed timestep. It's important to point out that the default IAS15 integrator is not well suited f... |
10,892 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: <table class="tfo-notebook-buttons" align="left">
Step2: Some normal imports we've seen before.
Step3: Part 1
Step4: The images in the Dogs v... |
10,893 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sn
%matplotlib inline
nfl_data = pd.read_csv('/home/max/nfl_stats/data/pbp_2009_2015.csv', low_memory=False)
#Print (Rows, Columns) of Data
print(nfl_data.shape)
#Print Variable-Names and First Two Va... | <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: How cool is this?! Almost any question I can think of regarding NFL play outcomes is suddenly queryable. Although first, we'll clarify exactly w... |
10,894 | <ASSISTANT_TASK:>
Python Code:
import os
from mpl_toolkits.basemap import Basemap
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
data_folder = os.path.join('data')
file_names = []
for f in os.listdir(data_folder):
file_names.append(os.path.join(data_folder,f))
del file_names[file_names.index(... | <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: II- Predictive Analysis
Step2: Now, let's find the most popular male and female names of all times
Step3: And the winner for most popular male... |
10,895 | <ASSISTANT_TASK:>
Python Code:
class Solution:
# @param {int} n an integer
# @param {int[][]} edges a list of undirected edges
# @return {boolean} true if it's a valid tree, or false
def validTree(self, n, edges):
# Write your code here
dic = {i: [] for i in range(n)}
for i, j in... | <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: Clone Graph
Step2: Search Graph Nodes
Step4: Topological Sorting
Step6: BFS in Matrix
Step7: Zombie in Matrix
Step8: Knight Shortest Path
|
10,896 | <ASSISTANT_TASK:>
Python Code:
# Load library
import numpy as np
# Create matrix
matrix = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
# Create a function that adds 100 to something
add_100 = lambda i: i + 100
# Create a vectorized function
vectorized_add_100 = np.vectorize(add_10... | <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 Matrix
Step2: Create Vectorized Function
Step3: Apply Function To Elements
|
10,897 | <ASSISTANT_TASK:>
Python Code:
!pip install -r requirements.txt --quiet
import os
import datetime
import numpy as np
import pandas as pd
import pickle
import zipfile
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
i... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Imports
Step2: Set random seed for reproducibility and ignore warning messages.
Step3: Download data
Step4: Load and preprocess data
Step5: ... |
10,898 | <ASSISTANT_TASK:>
Python Code:
# install Pint if necessary
try:
import pint
except ImportError:
!pip install pint
# download modsim.py if necessary
from os.path import exists
filename = 'modsim.py'
if not exists(filename):
from urllib.request import urlretrieve
url = 'https://raw.githubusercontent.com/A... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Yo-yo
Step2: The results are
Step3: Rmin is the radius of the axle. Rmax is the radius of the axle plus rolled string.
Step4: Based on these... |
10,899 | <ASSISTANT_TASK:>
Python Code:
def gaussian_model(xaxis, amplitude, offset, width):
amplitude = u.Quantity(amplitude, u.K)
offset = u.Quantity(offset, u.km/u.s)
width = u.Quantity(width, u.km/u.s)
return amplitude*np.exp(-(xaxis-offset)**2/(2.*width**2))
from specutils.io import fits
spec = fits.rea... | <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: Fitting Tools
Step2: astropy.modeling
|
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