Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
|---|---|---|
5,900 | <ASSISTANT_TASK:>
Python Code:
%load_ext ferretmagic
for i in [100,500,1000]:
%ferret_run -s 300,300 'set text/font=arial ; plot/thick=2 sin(i[i=1:%(i)s]*0.1)' % locals()
%%ferret
use levitus_climatology
for i in range(1,3):
%ferret_run -q -s 400,300 'set text/font=arial ; fill salt[k=%(i)s] ; go land' % loca... | <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: Explore usage of %ferret_run line magic
Step2: Another example
|
5,901 | <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 kfp --upgra... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Install the latest GA version of google-cloud-storage library as well.
Step2: Install the latest GA version of KFP SDK library as well.
Step3: ... |
5,902 | <ASSISTANT_TASK:>
Python Code:
import nltk
nltk.download('gutenberg')
# nltk.download('maxent_treebank_pos_tagger')
from nltk.corpus import gutenberg
# 저장되어 있는 데이터 로드 및 파일 제목 확인
gutenberg_files = gutenberg.fileids()
gutenberg_files
# 특정 텍스트 확인
gutenberg_doc = gutenberg.open('austen-emma.txt').read()
print(gutenberg_... | <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: nltk에서 제공되는 gutenberg data read
Step3: Tokenize
Step4: PoS tagging
Step5: Alphabetical list of part-of-speech tags used in the Penn Treebank ... |
5,903 | <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
<END_TASK>
<USER_TASK:>
Description:
Step1: Language Translation
Step3: Explore the Data
Step6: Implement Preprocessing Function
Step8: Preprocess all the data and save it
Step10: Chec... |
5,904 | <ASSISTANT_TASK:>
Python Code:
import kmeans; reload(kmeans)
from kmeans import Kmeans
n_clusters=6
n_samples =250
centroids = np.random.uniform(-35, 35, (n_clusters, 2))
slices = [np.random.multivariate_normal(centroids[i], np.diag([5., 5.]), n_samples)
for i in range(n_clusters)]
data = np.concatenate(sl... | <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 easiest way to demonstrate how clustering works is to simply generate some data and show them in action.
Step2: To generate our data, we're... |
5,905 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import scipy.sparse as ss
import numpy as np
from sklearn.decomposition import TruncatedSVD
import sklearn.manifold
import tsne
import re
raw_data = pd.read_csv('subreddit-overlap')
raw_data.head()
subreddit_popularity = raw_data.groupby('t2_subreddit')['NumOverlaps'].... | <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: I hadn't bothered to look if the relevant scikit-learn functions actually accepted sparse matrices when I was just playing, so I did the row nor... |
5,906 | <ASSISTANT_TASK:>
Python Code:
from math import log
def entropia(p):
return -p*log(p,2) - (1.0-p)*log(1.0-p,2)
print(entropia(1.0), entropia(0.0))
def entropia(p):
if p == 0 or p == 1:
return 0.0
else:
return -p*log(p,2) - (1.0-p)*log(1.0-p,2)
print(entropia(0.0), entropia(1.0), entropia(0.... | <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: Por conveniência, o valor da entropia para esses dois casos é definida como 0.0.
Step2: Nos dois primeiros casos, entropia(0.0) e entropia(1.0)... |
5,907 | <ASSISTANT_TASK:>
Python Code:
import vcsn
%%automaton a
context = "lan_char, b"
$ -> s
s -> A \e
A -> s \e
s -> $
ctx = vcsn.context('lal_char, q')
aut = lambda e: ctx.expression(e).standard()
aut('a+b').star("standard")
aut('a+b').star("general")
<END_TASK> | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: This is what the general algorithm for star outputs, given an automaton A and s being a new state.
Step2: Examples
Step3: General
|
5,908 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import tensorflow as tf
import helper
from tensorflow.examples.tutorials.mnist import input_data
print('Getting MNIST Dataset...')
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
print('Data Extracted.')
# Save the shapes of weights for each layer
layer_... | <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: Neural Network
Step2: Initialize Weights
Step3: As you can see the accuracy is close to guessing for both zeros and ones, around 10%.
Step4: ... |
5,909 | <ASSISTANT_TASK:>
Python Code:
Image('./res/iterative_policy_evaluation.png')
Image('./res/ex4_1.png')
class Action(enum.Enum):
EAST = enum.auto()
WEST = enum.auto()
SOUTH = enum.auto()
NORTH = enum.auto()
@staticmethod
def move(x, y, action):
if action == Action.EAST:
r... | <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: 4.2 Policy Improvement
Step2: 4.3 Policy Iteration
Step3: 4.4 Value Iteration
Step4: 4.5 Asynchronouse Dynamic Programming
|
5,910 | <ASSISTANT_TASK:>
Python Code:
## Step 1. Import pyopencga dependecies
from pyopencga.opencga_config import ClientConfiguration # import configuration module
from pyopencga.opencga_client import OpencgaClient # import client module
from pprint import pprint
from IPython.display import JSON
import matplotlib.pyplot as 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: Define some common variables
Step2: 1. Comon Queries for Clinical Analysis
Step3: Proband information
Step4: Check the interpretation id of a... |
5,911 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import numpy as np
from chemview import enable_notebook
from matscipy.visualise import view
enable_notebook()
from ase.lattice import bulk
from ase.optimize import LBFGSLineSearch
from quippy.potential import Potential
si = bulk('Si', a=5.44, cubic=True)
sw_pot = Potential(... | <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
Step2: Inline visualisation
Step3: DFT example - persistent connection, checkpointing
Step4: File-based interfaces vs. Native interf... |
5,912 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import xgboost as xgb
from xgboost.sklearn import XGBClassifier
from sklearn import cross_validation, metrics
from sklearn.grid_search import GridSearchCV
import matplotlib.pylab as plt
%matplotlib inline
from matplotlib.pylab import rcParams
rcParam... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Load Data
Step2: Define a function for modeling and cross-validation
Step3: Step 1- Find the number of estimators for a high learning rate
Ste... |
5,913 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Multi-task recommenders
Step2: Preparing the dataset
Step3: And repeat our preparations for building vocabularies and splitting the data into ... |
5,914 | <ASSISTANT_TASK:>
Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
%matplotlib inline
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
data = load_boston()
X, y = data.data, data.target
X_train, X_test, y_train, y_test = train_test_split(X, y)
f... | <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: Données
Step2: Premiers modèles
Step3: Pour le modèle, il suffit de copier coller le code écrit dans ce fichier lasso_random_forest_regressor.... |
5,915 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
test = np.random.randn(11,11,4,100)
test.shape
test_flat = test.flatten()
test_flat.shape
np.savetxt('test.txt', test_flat)
test_back = np.loadtxt('test.txt').reshape((11,11,4,100))
test_back.shape
np.mean(test - test... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: first just checking that the flattening and reshaping works as expected
|
5,916 | <ASSISTANT_TASK:>
Python Code:
import json
import pymongo
from pprint import pprint
conn=pymongo.MongoClient()
db = conn.mydb
conn.database_names()
collection = db.my_collection
db.collection_names()
doc = {"class":"xbus-502","date":"03-05-2016","instructor":"bengfort","classroom":"C222","roster_count":"25"}
collec... | <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: Connect
Step2: Create and access a database
Step3: Collections
Step4: Insert data
Step5: You can put anything in
Step6: A practical example... |
5,917 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import oandapyV20
import oandapyV20.endpoints.positions as positions
import configparser
config = configparser.ConfigParser()
config.read('../config/config_v20.ini')
accountID = config['oanda']['account_id']
access_token = config['oanda']['api_key']
client = oandapyV20... | <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: List all Positions for an Account.
Step2: List all open Positions for an Account.
Step3: Get the details of a single instrument’s position in ... |
5,918 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
##################
%matplotlib inline
# Compute the x and y coordinates for points on a sine curve
x = np.arange(0, 3 * np.pi, 0.1)
y = np.sin(x)
# Plot the points using matplotlib
plt.plot(x, y)
plt.show()
a = np.array([1, 4, 5, 66, 77,... | <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: NOTE
Step2: Subplots
Step3: Reading csv file and plotting the data.
Step4: Simple plot
Step5: Plotting with default settings
Step6: Instant... |
5,919 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'thu', 'sandbox-2', 'ocnbgchem')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "e... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
5,920 | <ASSISTANT_TASK:>
Python Code:
# Authors: Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from mne.viz import plot_topomap
import mne
from mne.stats import spatio_temporal_cluster_test
from m... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Set parameters
Step2: Read epochs for the channel of interest
Step3: Load FieldTrip neighbor definition to setup sensor connectivity
Step4: C... |
5,921 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'awi', 'awi-cm-1-0-lr', 'atmos')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "e... | <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... |
5,922 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import scipy.misc
from scipy.stats import signaltonoise
from scipy.stats import norm # Gaussian distribution
lena=scipy.misc.lena().astype(float)
lena+= norm.rvs(loc=0,scale=16,size=lena.shape)
signaltonoise(lena,axis=None)
import numpy
from scipy.stats import pa... | <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: Descriptive statistics
Step2: Interval estimation, correlation measures, and statistical tests
Step3: Distribution fitting
Step4: Distances
S... |
5,923 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import Image
Image(filename='images/06_03.jpg', width=1000)
Image(filename='images/07_01.png', width=500)
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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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<USER_TASK:>
Description:
Step1: <br>
|
5,924 | <ASSISTANT_TASK:>
Python Code:
# %load script.py
from pyomo.environ import *
from pyomo.opt import SolverFactory, TerminationCondition
def create_model():
model = ConcreteModel()
model.x = Var()
model.o = Objective(expr=model.x)
model.c = Constraint(expr=model.x >= 1)
model.x.set_value(1.0)
retu... | <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: The basic work flow that takes place above can be summarized as
Step3: The first argument to this function is the Pyomo model. The second argum... |
5,925 | <ASSISTANT_TASK:>
Python Code:
skchem.data.BursiAmes.available_sets()
skchem.data.BursiAmes.available_sources()
kws = {'sets': ('train', 'valid', 'test'), 'sources':('X_morg', 'y')}
(X_train, y_train), (X_valid, y_valid), (X_test, y_test) = skchem.data.BursiAmes.load_data(**kws)
print('train shapes:', X_train.shape,... | <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: And many sources
Step2: For this example, we will load the X_morg and the y sources for all the sets. These are circular fingerprints, and the... |
5,926 | <ASSISTANT_TASK:>
Python Code:
import sys
import os
sys.path.append(os.environ.get('NOTEBOOK_ROOT'))
import matplotlib.pyplot as plt
import xarray as xr
from utils.data_cube_utilities.dc_display_map import display_map
from utils.data_cube_utilities.dc_rgb import rgb
from utils.data_cube_utilities.urbanization import ND... | <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: <span id="Urbanization_Using_NDBI_plat_prod">Choose Platform and Product ▴</span>
Step2: Choose the platforms and products
Step3: <span ... |
5,927 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
%matplotlib inline
import os
# Connect to the database backend and initalize a Snorkel session
from lib.init import *
# Here, we just set how many documents we'll process for automatic testing- you can safely ignore this!
n_docs = 1000 if 'CI' in os.envi... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Loading the Corpus
Step2: Running a CorpusParser
Step3: We can then use simple database queries (written in the syntax of SQLAlchemy, which Sn... |
5,928 | <ASSISTANT_TASK:>
Python Code:
# using Tensorflow 2
%tensorflow_version 2.x
import math
import numpy as np
from matplotlib import pyplot as plt
import tensorflow as tf
print("Tensorflow version: " + tf.__version__)
#@title Data formatting and display utilites [RUN ME]
def dumb_minibatch_sequencer(data, batch_size, sequ... | <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: An stateful RNN model to generate sequences
Step3: Generate fake dataset [WORK REQUIRED]
Step4: Hyperparameters
Step5: Visualize training seq... |
5,929 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import matplotlib.pyplot as plt
#shape = (97, 2)
data = pd.read_csv('ex1data1.txt', header=None)
plt.scatter(data[0], data[1])
plt.xlabel('population')
plt.ylabel('profit')
plt.close()
import numpy as np
# Now we want to have our hypothesis function: h_theta = theta' ... | <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 preparation
Step2: Defining cost function
Step3: Defining gradient descent
Step4: Now, lets plot fit line on the training data
|
5,930 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nims-kma', 'sandbox-3', '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... |
5,931 | <ASSISTANT_TASK:>
Python Code:
# Python packages used
import numpy as np # for array operations
from matplotlib import pyplot as plt # for graphic output
from math import sqrt
# parameters
tolerance = 2.5 # max distance from the plane to accept point
rep = 1000 # number ... | <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: Az ismétlési szám elég magas.
Step2: Jelenítsük meg a generált pontokat és az egyenest.
Step3: Futtasa többször a fenti kódblokkot és vegye és... |
5,932 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import math
# Example of ranking data
l = [10, 9, 5, 7, 5]
print 'Raw data: ', l
print 'Ranking: ', list(stats.rankdata(l, method='average'))
## Let's see an example of this
n = 100
def compare_correlation_and... | <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: Spearman Rank Correlation
Step2: Let's take a look at the distribution of measured correlation coefficients and compare the spearman with the r... |
5,933 | <ASSISTANT_TASK:>
Python Code:
!pip install Pillow
!pip install pdf2image
!pip install pytesseract
!pip install opencv-python
from PIL import Image
import sys
from pdf2image import convert_from_path
import os
# Path of the pdf
PDF_file = "Lista inscrisi Admitere Licenta sept 11.09.2015.pdf"
if not os.path.exist... | <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: Olvasd el
Step2: Olvasd el
Step3: 1
Step4: Készítünk egy mappát, ahová a PDF oldalait exportáljuk képként.
Step5: 2
Step6: 3
Step7: Felism... |
5,934 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
def hat(x,a,b):
return -1*a*(x**2) + b*(x**4)
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(1.0, 10.0, 1.0)==-9.0
a = 5.0
b = 1.0
x = np.linspace(-3,3,100)
v... | <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: Hat potential
Step2: Plot this function over the range $x\in\left[-3,3\right]$ with $b=1.0$ and $a=5.0$
Step3: Write code that finds the two l... |
5,935 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import re
val = 'a, b, guido , bajo'
print(val)
# splitting the data by , and strip the whitespace
val2 = [x.strip() for x in val.split(',')]
val2
# tuple assignment
first, second, third, four = val2
first + "::" + second + "::" + third
# 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:
Step2: Python built-in string methods
Step3: Regular expression methods
|
5,936 | <ASSISTANT_TASK:>
Python Code:
labVersion = 'cs190.1x-lab4-1.0.4'
print labVersion
# Data for manual OHE
# Note: the first data point does not include any value for the optional third feature
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: Part 1
Step2: WARNING
Step3: (1b) Sparse vectors
Step4: (1c) OHE features as sparse vectors
Step7: (1d) Define a OHE function
Step8: (1e... |
5,937 | <ASSISTANT_TASK:>
Python Code:
from pycqedscripts.init.xld.virtual_ATC75_M136_S17HW02_PQSC import *
import warnings
warnings.filterwarnings("ignore")
# If running into problems with the initalization, it could be the AWG wave loading lines at the end of the init file.
# Commenting out:
# for AWG in AWGs:
# pulsar.... | <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: Example of use in v3 is based on do_state_tomo_analysis function in pycqedscripts.scripts.characterization.state_tomo
Step3: Load two experimen... |
5,938 | <ASSISTANT_TASK:>
Python Code:
import nltk
from nltk import word_tokenize
from nltk.corpus import stopwords
import string
punctuations = list(string.punctuation)
#read the two text files from your hard drive, assign first mystery text to variable 'text1' and second mystery text to variable 'text2'
text1 = open('../01-I... | <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: Lesson 2
Step2: Lesson 3
Step3: Lesson 4
Step4: Lesson 5
Step7: Lesson 6
Step8: Lesson 6
|
5,939 | <ASSISTANT_TASK:>
Python Code:
!gunzip ../data/2017-superbowl-tweets.tsv.gz
!ls ../data
tweets = []
RUTA = '../data/2017-superbowl-tweets.tsv'
for line in open(RUTA).readlines():
tweets.append(line.split('\t'))
ultimo_tweet = tweets[-1]
print('id =>', ultimo_tweet[0])
print('fecha =>', ultimo_tweet[1])
print('auto... | <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: Fíjate en la estructura de la lista
Step2: Al lío
|
5,940 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from __future__ import print_function
import numpy as np
from scipy import stats
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.graphics.api import qqplot
print(sm.datasets.sunspots.NOTE)
dta = sm.datasets.sunspots.loa... | <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: Sunpots Data
Step2: Does our model obey the theory?
Step3: This indicates a lack of fit.
Step4: Exercise
Step5: Let's make sure this model i... |
5,941 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
try:
np
except NameError:
print('Numpy not correctly imported')
Z = np.zeros(10)
print(Z)
assert type(Z).__module__ == np.__name__
assert len(Z) == 10
assert sum(Z) == 0
Z = np.zeros(10)
Z[4] = 1
print(Z)
assert type(Z).__module__ == np.__name__
assert len(Z) ... | <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. Create a null vector Z of size 10. Don't use [0, 0, ...] notation.
Step2: 3. Create a null vector of size 10 but the fifth value which is 1... |
5,942 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
df = pd.read_csv('data/human_body_temperature.csv')
df.info()
df.head()
# Plots the histogram of temperatures
import matplotlib.pyplot as plt
import seaborn as sns
temperature = df['temperature']
sns.set()
plt.hist(temperature, bins='auto', normed=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: 1) Is the distribution of body temperatures normal?
Step3: It is difficult to conclude whether this data is normally distributed from this hist... |
5,943 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
reviews = pd.read_csv("../input/wine-reviews/winemag-data-130k-v2.csv", index_col=0)
pd.set_option("display.max_rows", 5)
from learntools.core import binder; binder.bind(globals())
from learntools.pandas.indexing_selecting_and_assigning import *
print("Setup complete."... | <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 an overview of your data by running the following line.
Step2: Exercises
Step3: Follow-up question
Step4: 2.
Step5: 3.
Step6: 4.
St... |
5,944 | <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: Customizing AdaNet
Step2: Fashion MNIST dataset
Step6: Supply the data in TensorFlow
Step7: Launch TensorBoard
Step8: Establish baselines
St... |
5,945 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
from scipy import stats
import statsmodels.api as sm
import statsmodels.tsa as tsa
import matplotlib.pyplot as plt
# ensures experiment runs the same every time
np.random.seed(100)
# This function simluates an AR process, generating a new value based... | <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: Note how this process fluctuates around some central value. This value is the mean of our time series. As we have a constant mean throughout tim... |
5,946 | <ASSISTANT_TASK:>
Python Code:
# Ensure compatibility with Python 2 and 3
from __future__ import print_function, division
import xarray as xr
url = "http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis.derived/surface_gauss/air.2m.mon.1981-2010.ltm.nc"
ncep_air2m = xr.open_dataset(url, decode_times=Fal... | <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: Homework questions
Step2: The temperature data is called air. Take a look at the details
|
5,947 | <ASSISTANT_TASK:>
Python Code:
num_samples_left = 50
num_samples_right = 50
delta = 0.5 # width of measure's support per dimension
L = unit_center_set(2, num_samples_left, delta)
R = unit_center_set(2, num_samples_right, delta)
plt.scatter(L._values[:,0], L._values[:,1], c=L._probabilities)
plt.xlim([0,1])
plt.ylim([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: Define Metric
Step2: Clip and compare
Step3: Observe how these are distinctly different objects in memory
Step4: Density Emulation
Step5: Cl... |
5,948 | <ASSISTANT_TASK:>
Python Code:
%%capture --no-stderr
KFP_PACKAGE = 'https://storage.googleapis.com/ml-pipeline/release/0.1.14/kfp.tar.gz'
!pip3 install $KFP_PACKAGE --upgrade
import kfp.components as comp
mlengine_deploy_op = comp.load_component_from_url(
'https://raw.githubusercontent.com/kubeflow/pipelines/01a23... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load the component using KFP SDK
Step2: Sample
Step3: Example pipeline that uses the component
Step4: Compile the pipeline
Step5: Submit the... |
5,949 | <ASSISTANT_TASK:>
Python Code:
import os
import numpy as np
from filelock import FileLock
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import ray
ray.init()
# The number of sets of random hyperparameters to try.
num_evaluatio... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step4: Setup
Step5: Evaluating the Hyperparameters
Step6: Synchronous Evaluation of Randomly Generated Hyperparameters
Step7: Launch asynchronous pa... |
5,950 | <ASSISTANT_TASK:>
Python Code:
import sympy
sympy.init_printing()
x = sympy.Symbol('x')
y, z0 = sympy.symbols(('y', 'z_0'))
z0
a = x + y
b = y * z0
print("a={}. b={}.".format(a, b))
a
f = sympy.Function('f')
c = sympy.sin(x)**2 + sympy.cos(x)**2
c
c.simplify()
d = sympy.cosh(x)**2 - sympy.sinh(x)**2
d.simplify(... | <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 standard import command is used. The init_printing command looks at your system to find the clearest way of displaying the output; this isn'... |
5,951 | <ASSISTANT_TASK:>
Python Code:
import xarray as xr
import numpy as np
%matplotlib inline
path = 'http://apdrc.soest.hawaii.edu:80/dods/public_data/Argo_Products/monthly_mean/monthly_mixed_layer'
ds = xr.open_dataset(path, use_cftime=True)
ds
ds.load()
from xarrayutils.utils import linear_trend
# create an array
salin... | <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: Lets find out how much the salinity in each grid point changed over the full period (20 years)
Step2: Now we can plot the slope as a map
Step3:... |
5,952 | <ASSISTANT_TASK:>
Python Code:
# 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 writing, sof... | <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: Download buildings data for a region in Africa [takes up to 15 minutes for large countries]
Step4: Visualise the data
Step5: For some countrie... |
5,953 | <ASSISTANT_TASK:>
Python Code:
plot_approximation()
print("Pi was approximated at %.5f, when the real value is %.5f..." % (best_pi_approximation, real_pi_value))
plot_approximation_evolution_graph()
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Results
Step2: However, this method isn't very fast and can only approximate Pi, never truly compute the exact value.
|
5,954 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import Image
Image(url="http://docs.opencv.org/2.4/_images/separating-lines.png")
Image(url="http://docs.opencv.org/2.4/_images/optimal-hyperplane.png")
#Imports
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn import datasets
# lo... | <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 the above picture you can see that there exists multiple lines that offer a solution to the problem. Is any of them better than the others? W... |
5,955 | <ASSISTANT_TASK:>
Python Code:
%cd ../..
# Some libraries need to be installed that are not part of Gensim
! pip install click>=6.7 nltk>=3.2.5 prettytable>=0.7.2 pygtrie>=2.2
import csv
from collections import OrderedDict
from IPython.display import display, HTML
import logging
import os
import pickle
import random
im... | <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: Please set the variable parent_directory below to change the directory to which the repositories are cloned.
Step2: You might need to install a... |
5,956 | <ASSISTANT_TASK:>
Python Code:
PROJECT_ID = "[your-project-id]" #@param {type:"string"}
! gcloud config set project $PROJECT_ID
import sys
# If you are running this notebook in Colab, run this cell and follow the
# instructions to authenticate your GCP account. This provides access to your
# Cloud Storage bucket and l... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Authenticate your GCP account
Step2: Create a Cloud Storage bucket
Step3: Only if your bucket doesn't already exist
Step4: Finally, validate ... |
5,957 | <ASSISTANT_TASK:>
Python Code:
import torch
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
df = pd.read_csv('../Data/iris.csv')
df.head()
df.shape
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10,7))
fig.tight_layout()
plots = [(0,1),(2,3),(0,2),(1,3)]
colors = ['b', '... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Loading data from files
Step2: Plot the data
Step3: The iris dataset consists of 50 samples each from three species of Iris (<em>Iris setosa</... |
5,958 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'hadgem3-gc31-hm', 'land')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
5,959 | <ASSISTANT_TASK:>
Python Code:
# Authors: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
import mne
from mne import find_events, fit_dipole
from mne.datasets.brainstorm import bst_phantom_elekta
from mne.io import read_raw_fif
print(__doc__)
data_path = bst_ph... | <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 data were collected with an Elekta Neuromag VectorView system at 1000 Hz
Step2: Data channel array consisted of 204 MEG planor gradiometers... |
5,960 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
%matplotlib inline
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
import numpy as np
import scipy.io
dataset = scipy.io.loadmat(os.path.join(SHOGUN_DATA_DIR, 'ocr/ocr_taskar.mat'))
# patterns for training
p_tr = dataset['patterns_train']
# patterns f... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Few examples of the handwritten words are shown below. Note that the first capitalized letter has been removed.
Step3: Define Factor Types and ... |
5,961 | <ASSISTANT_TASK:>
Python Code:
df = pd.read_csv('npr_articles.csv')
df.head()
df.info()
df['date_published'] = pd.to_datetime(df['date_published'])
df.info()
# Let's create a mask for all rows that have a non-null value
mask = df['author'].notnull()
# When the data was saved to a csv, these lists were converted int... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: We can now checkout what our data consists of by using the .head() method on our DataFrame. By default, this will show the top 5 rows.
Step2: ... |
5,962 | <ASSISTANT_TASK:>
Python Code:
# Import packages to visualize the classifer
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
import warnings
# Import packages to do the classifying
import numpy as np
from sklearn.svm import SVC
def versiontuple(v):
return tuple(map(int, (v.split("."))))... | <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 Function To Visualize Classification Regions
Step2: Generate Data
Step3: Classify Using a Linear Kernel
Step4: Classify Using a RBF Ke... |
5,963 | <ASSISTANT_TASK:>
Python Code:
from sklearn import datasets
import numpy as np
iris = datasets.load_iris()
X = iris.data[:, [2, 3]]
y = iris.target
print('Class labels:', np.unique(y))
# Splitting data into 70% training and 30% test data:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_... | <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. Training a perceptron via scikit-learn
Step2: 2. Training a logistic regression model with scikit-learn
Step3: 3. Training a support vector... |
5,964 | <ASSISTANT_TASK:>
Python Code:
import os
import subprocess
if os.path.exists("/var/run/secrets/kubernetes.io/serviceaccount"):
subprocess.check_call(["pip", "install", "--user", "-r", "requirements.txt"], stderr=subprocess.STDOUT, bufsize=1)
# NOTE: The RuntimeWarnings (if any) are harmless. See ContinuumIO/anacond... | <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: Setup Authorization
Step3: Unique PR Creators
Step4: Number Prs
Step6: Release stats per release (quarter)
Step8: Get a list of distinct act... |
5,965 | <ASSISTANT_TASK:>
Python Code:
!conda install -y torchvision
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.datasets as dsets
import matplotlib.pylab as plt
import numpy as np
def show_data(data_sample):
plt.imshow(data_sample[0].numpy().reshape(28,28),cmap='gray... | <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: Use the following function to visualize data
Step2: <a id="ref1"></a>
Step3: Load the testing dataset by setting the parameters train <code>Fa... |
5,966 | <ASSISTANT_TASK:>
Python Code:
m
n
# m
grid_plot.sort_values(ascending=False, by='uni').head()
# n
freeGrid2.sort_values(ascending=False, by='median level').head()
check1 = grid_bssid.dropna(subset=['unique_bssid_list']).sort_values(ascending=False, by='uni').head().iloc[0,3]
len(check1)
<END_TASK> | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: 2. free wifi
Step2: DataFrame
Step3: <br/><br>
|
5,967 | <ASSISTANT_TASK:>
Python Code:
import helper
source_path = 'data/letters_source.txt'
target_path = 'data/letters_target.txt'
source_sentences = helper.load_data(source_path)
target_sentences = helper.load_data(target_path)
source_sentences[:50].split('\n')
target_sentences[:50].split('\n')
def extract_character_voca... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Let's start by examining the current state of the dataset. source_sentences contains the entire input sequence file as text delimited by newline... |
5,968 | <ASSISTANT_TASK:>
Python Code:
result_with_lat_long = result_with_lat_long[result_with_lat_long.decimallatitude.notnull() & result_with_lat_long.decimallongitude.notnull()]
result_with_lat_long['species'].unique().size
grouped_lat_long_year_or_eventdate = pd.DataFrame()
grouped_lat_long_year_or_eventdate['count'] = r... | <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 many unique species have occurrence records with latitude/longitude?
Step2: Best to take into account all observations which have either "y... |
5,969 | <ASSISTANT_TASK:>
Python Code:
# Import custom functions and connect to db
resa2_basic_path = (r'C:\Data\James_Work\Staff\Heleen_d_W\ICP_Waters\Upload_Template'
r'\useful_resa2_code.py')
resa2_basic = imp.load_source('useful_resa2_code', resa2_basic_path)
engine, conn = resa2_basic.connect_to_resa2(... | <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: 1.2. Check sites against John's spreadsheet
Step2: This looks promising. With a bit of luck, I can just move the four sites listed above from t... |
5,970 | <ASSISTANT_TASK:>
Python Code:
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("data/MNIST",one_hot=True)
import tensorflow as tf
# Setup our Input placeholder
x = tf.placeholder(tf.float32, [None, 784])
# Our Weights and Biases
W = tf.Variable(tf.zeros([784, 10]))
b = tf.V... | <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: Softmax regression
Step2: Training
Step3: Validation
|
5,971 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import csv
import matplotlib.pyplot as plt
income_df = pd.read_csv('LifeExpectancyIncome.csv')
income_df
income_df = income_df.set_index('Country').transpose()
income_df
income_df['Income / Person'] = income_df['Income / Person'].str.replace(r'[^-+\d.]', '').astype(... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Reading CSV File into Pandas DataFrame
Step2: I have a few things to do, first i want to flip the positions of columns with the of row index va... |
5,972 | <ASSISTANT_TASK:>
Python Code:
# Authors: Clemens Brunner <clemens.brunner@gmail.com>
# Felix Klotzsche <klotzsche@cbs.mpg.de>
#
# License: BSD-3-Clause
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import TwoSlopeNorm
import pandas as pd
import seaborn as sns
import mne
from mne.d... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: As usual, we import everything we need.
Step2: First, we load and preprocess the data. We use runs 6, 10, and 14 from
Step3: Now we can create... |
5,973 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0
# 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 writing, software
# dis... | <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: TensorFlowアドオンオプティマイザ：ConditionalGradient
Step2: モデルの構築
Step3: データの準備
Step5: カスタムコールバック関数の定義
Step6: トレーニングと評価
Step7: トレーニングと評価
Step8: 重みのフ... |
5,974 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import math
import random
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import load_boston
'''Since this is a classification problem, we will need to
represent our targets as one-hot encoding vectors (see previous lab).... | <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. Target data format
Step2: 2. Target data encoding
Step3: Perfomance measure
Step4: 4. Model definition
Step5: Now that we have replaced t... |
5,975 | <ASSISTANT_TASK:>
Python Code:
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
names = ["Name_2MASS", "RA", "Dec", "Spectral Type", "Membership",
"Teff", "AJ", "Lbol", "I", "I-zp","J-H","H-Ks", "Ks", "inIMF", "Night"]
tbl3 = pd.read_csv("http://iopscience.iop.org/0004-637X/617/2/1216/ful... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Table 3- New Members of Taurus
Step2: Save the data tables locally.
|
5,976 | <ASSISTANT_TASK:>
Python Code:
def urn_to_dict(urn_list):
urn_dict = {}
### BEGIN SOLUTION
### END SOLUTION
return urn_dict
u1 = ["green", "green", "blue", "green"]
a1 = set({("green", 3), ("blue", 1)})
assert a1 == set(urn_to_dict(u1).items())
u2 = ["red", "blue", "blue", "green", "yello... | <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
Step2: C
Step3: D
Step4: E
|
5,977 | <ASSISTANT_TASK:>
Python Code:
!head ../data/temperaturas.csv # Esta línea no funciona en Windows
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
datos = np.loadtxt("../data/temperaturas.csv",
skiprows=1, # Saltamos una línea
usecols=(1, 2, 3), # Solo colum... | <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: La primera columna es un entero con formato "AAAAMMDD" que vamos a ignorar. Las temperaturas están medidas en décimas de grado Celsius, así que ... |
5,978 | <ASSISTANT_TASK:>
Python Code:
#IMPORT
import numpy as np
import copy
import operator
from tqdm import tqdm
file_train = open("cascades_train.txt")
file_test = open("cascades_test.txt")
data_train=[]
for i in file_train.readlines():
tab = [[float(j.split(":")[0]),float(j.split(":")[1])] for j in i.split(";")[:-1]]... | <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: Objectif
Step2: Creation des liste de successeurs
Step3: Création des graphes
Step4: Apprentissage
Step5: Poids du graphe assignés en random... |
5,979 | <ASSISTANT_TASK:>
Python Code:
import myutils
raw_data_training, raw_data_testing = myutils.load_CIFAR_dataset(shuffle=False)
# raw_data_training = raw_data_training[:5000]
class_names = myutils.load_CIFAR_classnames()
n_training = len( raw_data_training )
n_testing = len( raw_data_testing )
print('Loaded CIFAR10 datab... | <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: Preprocessing
Step2: Lets look how images looks like
Step3: Feature extraction
Step4: Visualization of HOG histograms
Step5: Short look at t... |
5,980 | <ASSISTANT_TASK:>
Python Code:
import pynucastro as pyrl
al_mg = pyrl.Rate("al28--mg28-toki")
print(al_mg)
al_mg.eval(T=1.e8,rhoY=1.e9)
al_mg.plot()
ne_f = pyrl.Rate("ne23--f23-toki")
print(ne_f)
ne_f.plot()
files = ["c13-pg-n14-nacr",
"n13--c13-wc12",
"c12-c12n-mg23-cf88",
"o14-ap-f17-... | <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 a tabulated rate
Step2: A human readable string describing the rate, and the nuclei involved
Step3: Evaluate the electron capture rate [s... |
5,981 | <ASSISTANT_TASK:>
Python Code:
'''This is a definition script, so we do not have to rewrite code'''
import numpy as np
import cv2
import matplotlib.pyplot as mplt
# set matplotlib to print inline (Jupyter)
%matplotlib inline
# path prefix
pth = '../data/'
# files to be used as samples
# list *files* holds the names of ... | <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: <b>1. </b>Load image from a file and display the image. Determine the size of the image. Finally, save a new copy of the image in a new file.<br... |
5,982 | <ASSISTANT_TASK:>
Python Code:
from evoml.subsampling import BasicSegmenter_FEMPO, BasicSegmenter_FEGT, BasicSegmenter_FEMPT
df = pd.read_csv('datasets/ozone.csv')
df.head(2)
X, y = df.iloc[:,:-1], df['output']
print(BasicSegmenter_FEGT.__doc__)
from sklearn.tree import DecisionTreeRegressor
clf_dt = DecisionTreeRegres... | <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: 2. Subspacing - sampling in the domain of features - evolving and mutating columns
|
5,983 | <ASSISTANT_TASK:>
Python Code:
# Put your code here!
import random as rand
import math
def f(x):
return 2.0*(x**2) + 3.0
# x min, max: -2, 4 (delta_x = 6)
# y min, max: 0, 35
Area = (35-0)*(4+2)
real_area = 66.0
samples = []
errors = []
for i in range(1,7):
N_samples = 10**i
N_below = 0
f... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Part 2
Step3: Assignment wrapup
|
5,984 | <ASSISTANT_TASK:>
Python Code:
import os
import numpy as np
import imageio
import matplotlib
from matplotlib import pyplot as plt
import pandas as pd
# from skimage import img_as_ubyte
%matplotlib inline
matplotlib.rcParams['figure.figsize'] = (20.0, 10.0)
np.random.seed(1)
#deal = 2 * np.random.binomial(1,.5,size=(5,... | <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 load in a meme. I'm partial to 'Deal with it'.
Step2: To convert this to a 1 bit image, I convert everything darker than some threshold t... |
5,985 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from pylab import *
from utils import *
#-------------------------------------------------
# Training
# Constants
# Number of input elements
n = 2
# Learning rate
eta = 0.0001
# number of training patterns
n_patterns = 2000
# Number of repetitions of
# the pa... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Let us start by implementing a very simple network. Our network will only have two input units plus a bias unit, as in the figure.
Step2: Let'... |
5,986 | <ASSISTANT_TASK:>
Python Code:
# A bit of setup
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.neural_net import TwoLayerNet
from __future__ import print_function
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] =... | <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: Implementing a Neural Network
Step2: We will use the class TwoLayerNet in the file cs231n/classifiers/neural_net.py to represent instances of o... |
5,987 | <ASSISTANT_TASK:>
Python Code:
# import the free sample of the dataset
from quantopian.interactive.data.estimize import estimates_free
# or if you want to import the full dataset, use:
# from quantopian.interactive.data.estimize import estimates
# import data operations
from odo import odo
# import other libraries we w... | <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 go over the columns
Step2: How many records do we have now?
Step3: Let's break it down by user
Step4: Let's convert it over to a Pandas... |
5,988 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
#As the datatypes in all of the columns vary, I decided to to make all the values, except for the ones I specify
#into str. This also takes care of questions 1. -> dtype=str
# The syntax for this is really very nice and clear, an example na_values= {'Vehicle Year' : ... | <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. I want to make sure my Plate ID is a string. Can't lose the leading zeroes!
Step2: 2. I don't think anyone's car was built in 0AD. Discard t... |
5,989 | <ASSISTANT_TASK:>
Python Code:
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
%%bash
sudo pip3 freeze | grep google-cloud-bigquery==1.6.1 || \
sudo pip3 install google-cloud-bigquery==1.6.1
from google.cloud import bigquery
query =
SELECT
weight_pounds,
is_male,
mother_age,
plurali... | <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: 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... |
5,990 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division, unicode_literals
import pandas as pd
import numpy as np
import matplotlib
%matplotlib inline
matplotlib.style.use('ggplot')
df = pd.read_excel('./input/complete_data.xls')
df.head()
import datetime
import numpy as np
import matplotlib.pyplot as plt
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: 2.) Preview the raw data
Step2: 3.) Now for the charts
Step3: 3.2) Pandas
Step4: Bar Chart
Step5: Histogram
Step6: Stacked Bar Chart
Step7:... |
5,991 | <ASSISTANT_TASK:>
Python Code:
4*2
import numpy as np
print(np.sin(.5))
print(np.random.random(3))
import os
# Load the os library
import os
# Load the request module
import urllib.request
# Create a directory
os.mkdir('img_align_celeba')
# Now perform the following 10 times:
for img_i in range(1, 11):
# create a... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Now press 'a' or 'b' to create new cells. You can also use the toolbar to create new cells. You can also use the arrow keys to move up and dow... |
5,992 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
def random_line(m, b, sigma, size=10):
Create a line y = m*x + b + N(0,sigma**2) between x=[-1.0,1.0]
Param... | <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: Line with Gaussian noise
Step5: Write a function named plot_random_line that takes the same arguments as random_line and creates a random line ... |
5,993 | <ASSISTANT_TASK:>
Python Code:
#@test {"skip": true}
!pip install --quiet --upgrade tensorflow-federated
!pip install --quiet --upgrade nest-asyncio
import nest_asyncio
nest_asyncio.apply()
import collections
import time
import tensorflow as tf
import tensorflow_federated as tff
source, _ = tff.simulation.datasets.emn... | <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: 훈련할 모델 정의하기
Step2: 원격 실행기 설정하기
Step3: 훈련 실행하기
|
5,994 | <ASSISTANT_TASK:>
Python Code:
import json
import numpy as np
import torchvision
import torch
import torch.nn as nn
import shap
from PIL import Image
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = torchvision.models.mobilenet_v2(pretrained=True, progress=False)
model.to(device)
model.e... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Loading Model and Data
Step2: Explain one image
Step3: Explain multiple images
|
5,995 | <ASSISTANT_TASK:>
Python Code:
import os
#os.chdir('/Users/q6600sl/IPython_NB')
from lifemodels import s_models
%matplotlib inline
#Read the data
original_df = pd.read_csv('/Users/q6600sl/Downloads/SP_12-22-15.txt', sep=' ')
#1st step: create a survival object
surv_df = s_models.survt_df(original_df)
original_df.he... | <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: · Read the data and create a survival object using .survt_df() method
Step2: · Specify the model and create the model fit object using .distfit... |
5,996 | <ASSISTANT_TASK:>
Python Code:
# Initialize PySpark
APP_NAME = "Debugging Prediction Problems"
# If there is no SparkSession, create the environment
try:
sc and spark
except NameError as e:
import findspark
findspark.init()
import pyspark
import pyspark.sql
sc = pyspark.SparkContext()
spark = pyspark.sql.... | <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: Hello, World!
Step2: Creating Objects from CSV
Step3: GroupBy
Step4: Map vs FlatMap
Step5: Creating Rows
Step7: Creating DataFrames from RD... |
5,997 | <ASSISTANT_TASK:>
Python Code:
import glob
file_list = glob.glob(r'C:/dev/forensic/data/**/*.txt', recursive=True)
file_list = [x.replace("\\", "/") for x in file_list]
file_list[:5]
import pandas as pd
dfs = []
for files_file in file_list:
try:
files_df = pd.read_csv(files_file, names=['sha', 'timest... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: We can then import the data by looping through all the files and read in the corresponding files' content. We further extract the information it... |
5,998 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
data_df = pd.read_csv('data/hourly_wages.csv')
data_df.head()
data_df.describe()
target = data_df.wage_per_hour.as_matrix()
predictors = data_df.drop(['wage_per_hour'], axis=1).as_matrix()
n_cols = predictors.shape[1]
from keras.models import Sequential
from keras.lay... | <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: My First Model
Step2: Validation and Early Stopping
Step3: Classification
Step4: Convolutional Networks (Working with Images)
Step5: Transfe... |
5,999 | <ASSISTANT_TASK:>
Python Code:
import platform
if platform.system() == "Windows" : # create directory on Windows
!mkdir output-01-naive
if platform.system() == "Linux" : # create directory on Linux
!mkdir -p ./output-01-naive
!hybridizer-cuda ./01-naive/01-naive-csharp.cs graybitmap.cs -o ./01-naive/01-naive-... | <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: Distributing on CPU Threads
Step2: Running on the GPU
Step3: Memory Allocation
Step4: Feeding the Beast
Step5: Distributing 1960 lines by bl... |
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