Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
|---|---|---|
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Python Code:
import requests
import json
import pandas as pd
from pandas.io.json import json_normalize
import copy
from datetime import datetime
import plotly
import plotly.graph_objs as go
from plotly import tools
plotly.__version__
plotly.offline.init_notebook_mode(connected=True)
headers={'User-Age... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step 1
Step1: Pagecounts
Step2: Get pagecounts of en.wikipedia.org through mobile.
Step3: Pageview
Step4: Get page viewcounts of en.wikipedia.org th... |
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Python Code:
weeks = ['150815','150822','150829','150919','150926',
'151003','151024','151121','151212','151219',
'160130','160206','160227','160305','160312',
'160326','160409','160416','160430','160507',
'160514','160521','160611','160618','160625']
urls = ['''htt... | <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 it in and taking a look.
Step2: Time is in four hour intervals (generally). The time listed is when a particular time interval ends. En... |
8,302 | <ASSISTANT_TASK:>
Python Code:
import requests
from BeautifulSoup import *
url = "http://www.bloomberg.com/quote/SPX:IND"
response = requests.get(url)
page = response.text
soup = BeautifulSoup(page)
soup.findAll('h1')
index_name = soup.findAll('h1', attrs={'class': 'name'})
print(index_name)
print(index_name[0].text... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: If you check use the Inspect element feature from Google Chrome, you will see that the name of the index is inside an <h1> tag which has a... |
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Python Code:
model = Model()
NUM_LAYERS=2
INPUT_DIM=50
HIDDEN_DIM=10
builder = LSTMBuilder(NUM_LAYERS, INPUT_DIM, HIDDEN_DIM, model)
# or:
# builder = SimpleRNNBuilder(NUM_LAYERS, INPUT_DIM, HIDDEN_DIM, model)
s0 = builder.initial_state()
x1 = vecInput(INPUT_DIM)
s1=s0.add_input(x1)
y1 = s1.output()
... | <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 that when we create the builder, it adds the internal RNN parameters to the model.
Step2: If our LSTM/RNN was one layer deep, y2 would be ... |
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Python Code:
grammar1 =
S -> NP VP
NP -> DET N
DET -> "der" | "die" | "das"
N -> "Mann" | "Frau" | "Buch"
VP -> V NP NP
V -> "gibt" | "schenkt"
test_sentences = [
"der Mann gibt der Frau das Buch"
]
import nltk
from IPython.display import display
import sys
def test_gram... | <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: Übungsblatt 4
Step2: Sammeln Sie Sätze, die als grammatisch erkannt werden sollten, am besten in einer Liste.
Step3: Die folgende Funktion kan... |
8,305 | <ASSISTANT_TASK:>
Python Code:
import keras
from keras.layers import Concatenate,Dense,Embedding
rnn_num_units = 64
embedding_size = 16
#Let's create layers for our recurrent network
#Note: we create layers but we don't "apply" them yet
embed_x = Embedding(n_tokens,embedding_size) # an embedding layer that converts cha... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: II. RNN
Step2: III. Sampling
|
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Python Code:
%matplotlib inline
import colour
from colour.plotting import *
colour.filter_warnings(True, False)
colour_plotting_defaults()
# Plotting the visible spectrum.
visible_spectrum_plot()
from pprint import pprint
import colour.colorimetry as colorimetry
pprint(colorimetry.__all__)
import co... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The spectrum is defined as the display or specification of the monochromatic components of the radiation considered. <a name="back_reference_3">... |
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Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ipsl', 'ipsl-cm6a-lr', 'seaice')
# 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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Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 2... |
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Python Code:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
%matplotlib inline
# http://scikit-learn.org/stable/auto_examples/plot_digits_pipe.html#example-plot-digits-pipe-py
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model, decomposition, d... | <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 previous weeks we have covered preprocessing our data, dimensionality reduction, and last week looked at supervised learning. This week we wi... |
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Python Code:
import random
def inicializarMatrizNueva(filas,columnas,valorMaximoNumero):
matriz = []
for i in range(filas):
fil = []
for j in range(columnas):
a = random.randrange(valorMaximoNumero)
fil.append(a)
matriz.append(fil)
matr... | <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: Matriz de prueba
Step2: Ejecutar y tomar las dos primeras filas para verificar si sí funciona el algoritmo
Step3: Se guarda el archivo en $HOM... |
8,310 | <ASSISTANT_TASK:>
Python Code:
x = np.random.RandomState(0).uniform(-5, 5, 20)
#x = np.random.uniform(-5, 5, 20)
y = x*np.sin(x)
#y += np.random.normal(0,0.5,y.size)
y += np.random.RandomState(34).normal(0,0.5,y.size)
x_star = np.linspace(-5,5,500)
#Define the basic kernels
k1 = SqExp(0.45,2)
k2 = RQ(0.5,2,3)
k3 = Ex... | <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 the Test Set
Step2: Train the Model
Step3: Regression
Step4: Optimize Hyperparameters
Step5: array([ 1.47895967, 3.99711988, 0.1629... |
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Python Code:
# import the modules
import sys
import GPy
import csv
import numpy as np
import cPickle as pickle
import scipy.stats as stats
import sklearn.metrics as metrics
from matplotlib import pyplot as plt
%matplotlib notebook
# function to compute reconstruction error
def reconstructionError(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: Plotting and Analysis Functions
Step2: Data Loading
|
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Python Code:
import tempfile
from typing import List
import equinox as eqx # https://github.com/patrick-kidger/equinox
import jax
import jax.numpy as jnp
import optax # https://github.com/deepmind/optax
import pysr # https://github.com/MilesCranmer/PySR
import sympy
# Note that PySR, which we use 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: Now for a bunch of helpers. We'll use these in a moment; skip over them for now.
Step2: Okay, let's get started.
|
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Python Code:
%pylab notebook
Sbase = 100e6 # [VA]
Vp0 = 230e3 # [V]
Vs0 = 115e3 # [V]
Rc_pu = 100.0
Xm_pu = 20.0
Req_pu = 0.015
Xeq_pu = 0.06
Sload = 80e6 # [VA]
PF = 0.8
Vls_a = Vs0
Ils_a = Sload / (sqrt(3)*Vls_a)
print('Ils_a = {:.0f} A'.format(Ils_a))
Vls_base = Vs0
Ils_base_a = 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: Description
Step2: (a)
Step3: The base apparent power is $S_\text{base} = 100\,MVA$ , and the base line voltage on the secondary side is $V_{L... |
8,314 | <ASSISTANT_TASK:>
Python Code:
import glob
import pandas as pd
samples = {
'train':{},
'test':{}
}
files = glob.glob('20news-bydate-*/talk.politics*/*')
for s in samples.keys():
for c in ['guns', 'mideast', 'misc']:
samples[s][c] = samples[s].get(c, len(filter(lambda x: s in x and c in x, files)))
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: Model
Step2: Testing
Step3: Because I don't want to re-run the training & testing everything time I come back to this project, we will save th... |
8,315 | <ASSISTANT_TASK:>
Python Code:
#importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline
#reading in an image
image = mpimg.imread('images/solidWhiteRight.jpg')
#printing out some stats and plotting
print('This image is:', type(ima... | <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 an Image
Step10: Ideas for Lane Detection Pipeline
Step11: Test Images
Step12: Build a Lane Finding Pipeline
Step13: Test on Videos
... |
8,316 | <ASSISTANT_TASK:>
Python Code:
from sklearn.datasets import fetch_lfw_people
# Importamos mediante una de las dos alternativas
# 1ª alternativa devuelve las imagenes en RGB pero con sus
# respectivos tres valores
faces = fetch_lfw_people(color = True)
positive_patches = faces.images
positive_patches.shape
%matplotlib ... | <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: Realizamos algunos imports necesarios
Step2: Antes de nada, vamos a realizar una pequeña muestra de los resultados obtenidos con una única imag... |
8,317 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from time import time, sleep
import numpy as np
import matplotlib.pyplot as plt
from IPython import display
import pandas as pd
from time import time
from tqdm import tqdm
# (re-)load layers
%run homework_modules.ipynb
# Generate some data
N = 500
X1 = np.random.randn... | <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: Framework
Step2: Toy example
Step3: Define a logistic regression for debugging.
Step4: Start with batch_size = 1000 to make sure every step l... |
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Python Code:
import pandas as pd
import statsmodels.api as sm
from sklearn.cross_validation import train_test_split
import math
import numpy as np
import matplotlib.pyplot as plt
gdf = pd.read_csv("./CSV/merged.csv")
df1 = gdf[['AIRLINE_ID','ORIGIN', 'DEST', 'DEP_TIME','ARR_TIME','DEP_DELAY','ARR_DE... | <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. Extract Data
Step2: 3. Select Data
Step3: Afterwards, we filter by the important airports (ATL, DFW, JFK, LAX and ORD)
Step4: 4. Sample Da... |
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Python Code:
import tensorflow as tf
import numpy as np
import math
import timeit
import matplotlib.pyplot as plt
%matplotlib inline
from cs231n.data_utils import load_CIFAR10
def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=10000):
Load the CIFAR-10 dataset from disk 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: What's this TensorFlow business?
Step2: Example Model
Step3: TensorFlow supports many other layer types, loss functions, and optimizers - you ... |
8,320 | <ASSISTANT_TASK:>
Python Code:
s3 = boto3.client('s3')
s3.list_buckets()
def create_s3_bucket(bucketname):
Quick method to create bucket with exception handling
s3 = boto3.resource('s3')
exists = True
bucket = s3.Bucket(bucketname)
try:
s3.meta.client.head_bucket(Bucket=bucketname)
excep... | <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: AWS (S3, Redshift, Kinesis) + Databricks Spark = Real-time Smart Meter Analytics
Step2: Copy Postgres to S3 via Postgres dump to CSV and s3cmd ... |
8,321 | <ASSISTANT_TASK:>
Python Code:
m.group(2)
m.group('first_name')
import re
foo_pattern = re.compile('''
^
([A-Za-z]+)
,[ ]
([A-Za-z]+)
$
''', re.VERBOSE)
s = 'James, Mackenzie'
m = re.match(foo_pattern, s)
m
m.groups
m.group(0)
m.group(1)
m.group(2)
foo_pattern = re.compile('''
^
(?P<last_n... | <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: Mackenzie (first name)
Step2: Regular expressions can be used to indicate if a string matches a pattern or not.
|
8,322 | <ASSISTANT_TASK:>
Python Code:
# from __future__ import exam_success
from __future__ import absolute_import
from __future__ import print_function
%matplotlib inline
import sklearn
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import random
import pandas as pd
import scipy.stats as stats
# Sk ... | <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: 13.765.202 lines in train.csv
Step2: Per wikipedia, a value of more than 421 mm/h is considered "Extreme/large hail"
Step3: We regroup the d... |
8,323 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
n = 21
n_phases = 3
from pymks.tools import draw_microstructures
from pymks.datasets import make_delta_microstructures
X_delta = make_delta_microstructures(n_phases=n_phases, size=(n, ... | <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 take a look at a few of the delta microstructures by importing draw_microstructures from pymks.tools.
Step2: Using delta microstructures ... |
8,324 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
data = {'spike-2': [1,2,3], 'hey spke': [4,5,6], 'spiked-in': [7,8,9], 'no': [10,11,12]}
df = pd.DataFrame(data)
s = 'spike'
def g(df, s):
spike_cols = [s for col in df.columns if s in col and s != col]
for i in range(len(spike_cols)):
spike_cols[i] = 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:
|
8,325 | <ASSISTANT_TASK:>
Python Code:
x_train,y_train,x_valid,y_valid = get_data()
train_ds,valid_ds = Dataset(x_train, y_train),Dataset(x_valid, y_valid)
nh,bs = 50,512
c = y_train.max().item()+1
loss_func = F.cross_entropy
data = DataBunch(*get_dls(train_ds, valid_ds, bs), c)
#export
def create_learner(model_func, loss_func... | <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: Annealing
Step2: Let's start with a simple linear schedule going from start to end. It returns a function that takes a pos argument (going from... |
8,326 | <ASSISTANT_TASK:>
Python Code:
!ssh thauser@thauser@comet.sdsc.edu 'cd .ipython/profile_nbserver; ls -al'
from IPython.lib import passwd
passwd('test password')
!ssh thauser@gordon.sdsc.xsede.org 'head -n 12 .ipython/profile_nbserver/ipython_notebook_config.py'
import sys
import time
import saga
# Adapted from the 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: 2. Create a self-signed certificate
Step2: Not recommended
Step3: Running the secured remote notebook
|
8,327 | <ASSISTANT_TASK:>
Python Code:
from bookworm import *
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
plt.rcParams['figure.figsize'] = (12,9)
import pandas as pd
import numpy as np
book = load_book('data/raw/hp_philosophers_stone.txt')
characters = extract_character_n... | <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: Visualisation with NetworkX
Step2: get_interaction_df() is defined in bookworm/build_network.py, and works by searching through the provided co... |
8,328 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
import requests
from io import BytesIO
# NBER recessions
from pandas_datareader.data import DataReader
from datetime import datetime
usrec = DataReader('USREC', 'fred', 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: Hamilton (1989) switching model of GNP
Step2: We plot the filtered and smoothed probabilities of a recession. Filtered refers to an estimate of... |
8,329 | <ASSISTANT_TASK:>
Python Code:
BATCH_SIZE = 128
EPOCHS = 10
training_images_file = 'gs://mnist-public/train-images-idx3-ubyte'
training_labels_file = 'gs://mnist-public/train-labels-idx1-ubyte'
validation_images_file = 'gs://mnist-public/t10k-images-idx3-ubyte'
validation_labels_file = 'gs://mnist-public/t10k-label... | <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: Imports
Step3: tf.data.Dataset
Step4: Let's have a look at the data
Step5: Keras model
Step6: Train and validate the model
Step7: Visualize... |
8,330 | <ASSISTANT_TASK:>
Python Code:
# Step 1: right-click the "download" link on the left
# Step 2: select "copy link address"
# Step 3: paste the link into the following bash command, after "wget"
!wget https://repo.continuum.io/archive/Anaconda3-4.4.0-Linux-x86_64.sh
!ls # This will show us the files in our current direc... | <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 download takes awhile; it's a big distribution!
Step2: This part will take awhile, depending largely on your internet connection. Go grab s... |
8,331 | <ASSISTANT_TASK:>
Python Code:
import time
# We will use some np and pandas for dealing with input data.
import numpy as np
import pandas as pd
# And of course, we need tensorflow.
import tensorflow as tf
from matplotlib import pyplot as plt
from IPython.display import clear_output
tf.__version__
tf.logging.set_verbos... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Below we demonstrate both local and global model interpretability for gradient boosted trees.
Step2: Interpret model
Step4: Local interpretab... |
8,332 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'pcmdi', 'sandbox-1', 'land')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "emai... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
8,333 | <ASSISTANT_TASK:>
Python Code:
!pip install keras-tuner -q
from tensorflow import keras
from tensorflow.keras import layers
def build_model(hp):
model = keras.Sequential()
model.add(layers.Flatten())
model.add(
layers.Dense(
# Define the hyperparameter.
units=hp.Int("units",... | <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: Introduction
Step2: You can quickly test if the model builds successfully.
Step3: There are many other types of hyperparameters as well. We ca... |
8,334 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'dwd', 'sandbox-3', 'toplevel')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "em... | <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: 2... |
8,335 | <ASSISTANT_TASK:>
Python Code:
from keras.datasets import mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
train_images.shape
len(train_labels)
train_labels
test_images.shape
len(test_labels)
test_labels
from keras import models
from keras import layers
network = models.Sequential()
... | <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: images 是用來訓練與測試的資料，label 則為每一筆影像資料對應的正確答案，每一張手寫圖片都是 28 x 28 的灰階 Bit Map
Step2: 建立準備訓練的神經網路
Step3: 上面這裡是神經網路的核心組成方式，我們在全連接層建立了兩層，由一個有 512 個神經元的... |
8,336 | <ASSISTANT_TASK:>
Python Code:
a = None
if a is None:
print('nulo')
a = True
b = False
c = True
print(a == b)
print(a == c)
print(type(1))
print(type(1.2))
print("Divisao: ",1 / 2)
print("Divisao inteira: ",1 // 2)
print(2 ** 3)
print(25 ** (1/2))
import math
print(math.sqrt(2))
print(math.log(2))
print(math.c... | <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: Boolean
Step2: Números
Step3: Ao dividir dois números (inteiros ou flutuantes), podemos fazer a divisão comum (/) ou divisão inteira (//), ond... |
8,337 | <ASSISTANT_TASK:>
Python Code:
# Import modules that contain functions we need
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
# Our data is a table and is defined as the word 'data'.
# 'data' is set equal to the .csv file that is read by the pandas function.
# The .csv file mu... | <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: This table shows the top 10 water consuming counties, the population, the amount of the population that is connected to public wa... |
8,338 | <ASSISTANT_TASK:>
Python Code:
def search(start, goal, next_states):
limit = 36
while True:
Path = depth_limited_search(start, goal, next_states, [start], { start }, limit)
if Path is not None:
return Path
limit += 1
print(f'limit = {limit}')
def depth_limited_search... | <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 function depth_limited_search tries to find a solution to the search problem
Step2: Solving the Sliding Puzzle
|
8,339 | <ASSISTANT_TASK:>
Python Code:
#!pip install -I "phoebe>=2.4,<2.5"
import phoebe
from phoebe import u # units
import numpy as np
import matplotlib.pyplot as plt
logger = phoebe.logger()
b = phoebe.default_binary()
b.add_constraint('semidetached', 'primary')
b['requiv@constraint@primary']
b['requiv_max@constraint@pr... | <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 always, let's do imports and initialize a logger and a new Bundle.
Step2: Semi-Detached Systems
Step3: We can view the constraint on requiv... |
8,340 | <ASSISTANT_TASK:>
Python Code:
import os
from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
directory = '/home/tpin3694/Documents/'
python_files = [os.path.join(root, name)
for root, dirs, files in os.walk(directory)
for name in files ... | <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: With the counts stored in a Counter object, lets now quickly print out the top ten libraries and their respective counts.
Step2: Nothing there ... |
8,341 | <ASSISTANT_TASK:>
Python Code:
#API Key: 0c3ba2a8848c44eea6a3443a17e57448
import requests
bestseller_response = requests.get('http://api.nytimes.com/svc/books/v2/lists/2009-05-10/hardcover-fiction?api-key=0c3ba2a8848c44eea6a3443a17e57448')
bestseller_data = bestseller_response.json()
print("The type of bestseller_data... | <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: Graded = 8/8
Step2: After writing a code that returns a result, now automating that for the various dates using a function
Step3: 2) What are ... |
8,342 | <ASSISTANT_TASK:>
Python Code:
def batchnormalization(X, eps=1e-8, W=None, b=None):
if X.get_shape().ndims == 4:
mean = tf.reduce_mean(X, [0,1,2])
standar_desviation = tf.reduce_mean(tf.square(X-mean), [0,1,2])
X = (X - mean) / tf.sqrt(standar_desviation + eps)
if W is ... | <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: Leaky Relu
Step2: BCE
Step3: GENERATOR AND DISCRIMINATOR FUNCTIONS
Step4: Model
Step5: Optimizer
Step6: Sample Generator
Step7: Aux functi... |
8,343 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
% matplotlib inline
from matplotlib import pyplot as plt
data = pd.read_csv('../data/data_with_problems.csv', index_col=0)
print('Our dataset has %d columns (features) and %d rows (people).' % (data.shape[1], data.shape[0]))
data.head(15)
data = d... | <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 dataset that will be used
Step2: Let us drop the missing and duplicated values since they don't matter for now (already covered in oth... |
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Python Code:
!pip install git+https://github.com/google/starthinker
from starthinker.util.configuration import Configuration
CONFIG = Configuration(
project="",
client={},
service={},
user="/content/user.json",
verbose=True
)
FIELDS = {
'auth_dv':'user', # Credentials used for dv.
'au... | <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. Set Configuration
Step2: 3. Enter DV360 Bulk Editor Recipe Parameters
Step3: 4. Execute DV360 Bulk Editor
|
8,345 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
from IPython.html import widgets
def print_sum(a, b):
Print the sum of the arguments a and b.
return a+b
i... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step2: Interact basics
Step3: Use the interact function to interact with the print_sum function.
Step5: Write a function named print_string that prin... |
8,346 | <ASSISTANT_TASK:>
Python Code:
for i in range(9):
print county_name[i]
zipcode=[93210,93263,93202,93638,93620,95641,95242,95326,93201]
ZipcodeList=[{ "County_N":county_name[i], "zipcode":zipcode[i] } for i in range(len(zipcode))]
COUNTYZIP=pd.DataFrame(ZipcodeList, columns=["County_N", "zipcode"])
COUNTYZIP
start=... | <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:
Step3: Lets extract the zipcode and precipetation data from California Department of Water Resources
Step4: Crop value extract from
|
8,347 | <ASSISTANT_TASK:>
Python Code:
try:
import numpy as np
except ImportError:
raise RuntimError('This notebook requires numpy')
# the baseline diet data as Python lists of tuples.
FOODS = [
("Roasted Chicken", 0.84, 0, 10),
("Spaghetti W/ Sauce", 0.78, 0, 10),
("Tomato,Red,Ripe,Raw", 0.27, 0, 10),
... | <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 next section we illustrate the range transformer with the Diet Problem, from DOcplex distributed examples.
Step2: Creating a Spark sessi... |
8,348 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import xarray as xr
import cartopy.crs as ccrs
import datetime as dt
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.gridspec as gridspec
import matplotlib.dates as mdates
mpl.rcParams['figure.figsize'] = 8.0, 4.0
mpl.rcPar... | <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. Load data
Step2: 2.2 SST
Step3: 2.3 Preprocess
Step4: 3. Carry out Maximum Covariance Analysis
Step5: 3.2 Postprocess
Step6: 3.2.2 Extra... |
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Python Code:
## Assume that this code exists in a file named example.py
def main():
print(1 + 1)
if __name__ == "__main__":
main()
def main():
DEBUG = False #True
if DEBUG:
random.seed(243)
print("+--------------------------------+")
print("| WELCOME TO MINSWEEPER 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: The above bit of boiler-plate code is useful in a number of situations. Indeed, this is a pattern I regularly find myself using when writing scr... |
8,350 | <ASSISTANT_TASK:>
Python Code:
def NthCharacter(n ) :
s = ""
c = 1
while(True ) :
if(c < 10 ) :
s += chr(48 + c )
else :
s1 = ""
dup = c
while(dup > 0 ) :
s1 += chr(( dup % 10 ) + 48 )
dup //= 10
s1 = "". join(reversed(s1 ) )
s += s1
c += 1
if(len(s ) >= n ) :
return s[n - 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:
|
8,351 | <ASSISTANT_TASK:>
Python Code:
data_in_shape = (5, 5, 2)
conv = Conv2D(4, (3,3), strides=(1,1), padding='valid',
data_format='channels_last', dilation_rate=(1,1),
activation='linear', use_bias=True)
layer_0 = Input(shape=data_in_shape)
layer_1 = conv(layer_0)
model = Model(inputs=layer_0, ou... | <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: [convolutional.Conv2D.1] 4 3x3 filters on 5x5x2 input, strides=(1,1), padding='valid', data_format='channels_last', dilation_rate=(1,1), activat... |
8,352 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
# Import a Kalman filter and other useful libraries
from pykalman import KalmanFilter
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import poly1d
tau = 0.1
# Set up the filter
kf = KalmanFilter(n_dim_obs=1, n_dim_state=2, # position is 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: Toy example
Step2: At each point in time we plot the state estimate <i>after</i> accounting for the most recent measurement, which is why we ar... |
8,353 | <ASSISTANT_TASK:>
Python Code:
a = np.array([3,4,5])
b = np.ones(3)
a - b
a = np.array([[1,2],[3,4]])
b = np.array([[1,2],[3,4]])
a
b
a * b
np.dot(a,b)
a = np.zeros((2,2),dtype='float')
a += 5
a
a *= 5
a
a + a
a = np.array([1,2,3])
b = np.array([4,5,6])
c = np.array([7,8,9])
np.hstack([a,b,c])
np.vstack([a,b,c])
x ... | <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: Zaskakujące może być działanie operatora *, który nie oblicza iloczynu macierzy. Odpowiada za to funkcja dot.
Step2: Inne operacje dodawania i ... |
8,354 | <ASSISTANT_TASK:>
Python Code:
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
from gensim.summarization import summarize
text = "Thomas A. Anderson is a man living two lives. By day he is an " + \
"average computer programmer and by night a hacker known 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: We will try summarizing a small toy example; later we will use a larger piece of text. In reality, the text is too small, but it suffices as an ... |
8,355 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
df = pd.io.parsers.read_csv(
'Data/NewBalanced.csv',
)
print(df.shape)
print('\n')
print(df.head(5))
print('\n')
print(df.tail(1))
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
df = pd.io.parsers.read_cs... | <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 file has 23 features and 10,341 data points. Clearly not all of these features are useful for training a model. For example we have date an... |
8,356 | <ASSISTANT_TASK:>
Python Code:
import os
home_dir = os.environ.get('HOME')
# Please enter the filename of the ztf_sim output file you would like to use. The example first determines
# your home directory and then uses a relative path (useful if working on several machines with different usernames)
survey_file = os.path... | <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: TransientGenerator
Step2: SimulSurvey
Step3: Analysing the output
Step4: You can inspect the lightcurves manually. This example should return... |
8,357 | <ASSISTANT_TASK:>
Python Code:
import time
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import StratifiedShuffleSplit
from sklearn.cross_validation import c... | <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: <h2> Import the dataframe and remove any features that are all zero </h2>
Step2: <h2> Get mappings between sample names, file names, and sample... |
8,358 | <ASSISTANT_TASK:>
Python Code:
import warnings
warnings.filterwarnings("ignore")
%matplotlib inline
import sys
sys.path.append("..")
#Import standard pydata libs
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
filename = '../facies_vectors.csv'
training_data = pd.read_csv(fi... | <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: Feature Engineering
Step2: Building the model and parameter tuning
|
8,359 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'hammoz-consortium', 'sandbox-2', 'toplevel')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contribut... | <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: 2... |
8,360 | <ASSISTANT_TASK:>
Python Code:
# 引用数据分析需要使用的一些包
# 数据规整化
import pandas as pd
from pandas import Series,DataFrame
from collections import Counter
import numpy as np
import re
# 数据可视化
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
%matplotlib inline
# 机器学习
from sklearn.linear_model import... | <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: 数据中包含的信息：
Step2: 从整体信息与摘要信息中我们获得了什么？
Step3: 这每一条冰冷的数据背后，都是一个真实存在的人。我们先聚焦这些数据背后鲜活的生命，来看看这些数据背后的故事。
Step4: 我们找到的第一个人，她的全名（'Name字段'）叫做Astor, Mrs... |
8,361 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: The Functional API
Step2: Introduction
Step3: The shape of the data is set as a 784-dimensional vector.
Step4: The inputs that is returned co... |
8,362 | <ASSISTANT_TASK:>
Python Code:
ymin = -4.0
ymax = 8.0
xmin = -4.0
xmax = 6.0
x0 = np.array([2.0, 1.0])
def rosenbrock(xvec):
x = xvec[0]
y = xvec[1]
f = (1.0 - x)**2 + 100.0*(y - x**2)**2
g = np.zeros(2)
g[0] = -2*(1 - x) + 200*(y - x**2)*-2*x
g[1] = 200*(y - x**2)
H = np.zeros((2, 2))
H... | <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 plot below show the Rosenbrock function in faded black and a blue quadratic approximation to the function about the blue dot.
Step2: We now... |
8,363 | <ASSISTANT_TASK:>
Python Code:
import pickle
import os
if not os.path.exists('secret_twitter_credentials.pkl'):
Twitter={}
Twitter['Consumer Key'] = ''
Twitter['Consumer Secret'] = ''
Twitter['Access Token'] = ''
Twitter['Access Token Secret'] = ''
with open('secret_twitter_credentials.pkl','wb'... | <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 twitter package to interface with the Twitter API
Step2: Example 1. Authorizing an application to access Twitter account data
Step3... |
8,364 | <ASSISTANT_TASK:>
Python Code:
# http://neo4j.com/docs/developer-manual/current/cypher/#query-load-csv
command =
LOAD CSV WITH HEADERS FROM "https://gist.githubusercontent.com/jexp/d788e117129c3730a042/raw/1bd8c19bf8b49d9eb7149918cc11a34faf996dd8/people.tsv"
AS line
FIELDTERMINATOR '\t'
CREATE (:Artis... | <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:
Step9: http
Step13: TRY ON OUR DATA
Step17: http
Step24: Go through and define all the nodes first, then add edges by iterating over each line?
Step... |
8,365 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mpi-m', 'icon-esm-lr', 'aerosol')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
8,366 | <ASSISTANT_TASK:>
Python Code:
#Drop quantitative features for which most samples take 0 or 1
for cols in quan:
if train_c[cols].mean() < 0.01 or train_c[cols].mean() > 0.99:
train_c.drop(cols, inplace=True, axis=1)
test_c.drop(cols, inplace=True, axis=1)
#For now we only use the quantitative featur... | <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: From now we try a range of estimators and use GridSearch to iteratively tune their hyperparameters
|
8,367 | <ASSISTANT_TASK:>
Python Code:
# Lowercase the hashtags and tweet body
df['hashtags'] = df['hashtags'].str.lower()
df['text'] = df['text'].str.lower()
print("Total number of tweets containing hashtag 'wall' = {}".format(len(df[df['hashtags'].str.contains('wall')])))
print("Total number of tweets whose body contains 'wa... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: What is the average twitter tenure of people who tweeted about the wall?
Step2: There are a couple of users tweeting multiple times, but most t... |
8,368 | <ASSISTANT_TASK:>
Python Code:
# Librerías
import pandas as pd
import numpy as np
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression, RidgeCV, LassoCV, ElasticNetCV
from sklearn.metrics import mean_squa... | <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: Contenido y estructura de los datos
Step2: Procesamiento de datos
Step3: Nota
Step4: Transformación logarítmica de la variable objetivo
Step... |
8,369 | <ASSISTANT_TASK:>
Python Code:
from konlpy.corpus import kolaw
kolaw.fileids()
c = kolaw.open('constitution.txt').read()
print(c[:100])
from konlpy.corpus import kobill
kobill.fileids()
d = kobill.open('1809890.txt').read()
print(d[:100])
x = [u"한글", {u"한글 키": [u"한글 밸류1", u"한글 밸류2"]}]
print(x)
from konlpy.utils import... | <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: 한국어 처리 유틸리티
Step2: 형태소 분석
Step3: 명사 추출
Step4: 형태소 추출
Step5: 품사 태깅
|
8,370 | <ASSISTANT_TASK:>
Python Code:
report_file = '/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing23_bow_200_512_04drb/encdec_noing23_bow_200_512_04drb.json'
log_file = '/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing23_bow_200_512_04drb/encdec_noing23_bow_200_512_04drb_... | <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: Perplexity on Each Dataset
Step2: Loss vs. Epoch
Step3: Perplexity vs. Epoch
Step4: Generations
Step5: BLEU Analysis
Step6: N-pairs BLEU An... |
8,371 | <ASSISTANT_TASK:>
Python Code:
x = 1
print(x)
y = 2 * x
print(y)
!python --version
def quicksort(arr):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quic... | <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: Global variables are shared between cells. Try executing the cell below
Step2: Keyboard Shortcuts
Step3: Basics of Python
Step4: Basic data t... |
8,372 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pymc3 as pm
import seaborn as sns
import arviz as ar
sns.set(font_scale=1.5)
%matplotlib inline
with pm.Model() as model:
H = pm.Normal('H', 2.00, sigma=0.03)
h = pm.Normal('h', 0.88, sigma=0.04)
Q = pm.Deterministic('Q', H-h)
trace = pm.sample(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: This version uses prior distributions to do all the work. H and h are both informative priors that then drive the solution to the right answer.
... |
8,373 | <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: Install TensorFlow for C
Step2: Linker
Step3: If you extract the TensorFlow C library to a non-system directory, such as
Step4: Compile
Step5... |
8,374 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from jyquickhelper import add_notebook_menu
add_notebook_menu()
def rendement(x, n, r):
return x*(1+r)**n
rendement(1, 2, 0.02)
rendement(1, 3, 0.02)
def decompose_mensualite(K,M,p):
i = K * ((1+p)**(1.0/12)-1)
return M-i, i
decompose_mensualite(180000, 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: Après chaque question, on vérifie sur un petit exemple que cela fonctionne comme attendu.
Step2: Q2
Step3: Q3
Step4: Parfois ce calcul entre ... |
8,375 | <ASSISTANT_TASK:>
Python Code:
"Elapsed decorator."
import datetime
def elapsed(func):
"Elapsed decorator"
def _wrapper(*args, **kwargs):
"Decoration function"
start = datetime.datetime.now()
ret = func(*args, **kwargs)
print("Elapsed time", datetime.datetime.now() - start)
... | <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 former was both a Closure and a High Order Function. Did you heard about Functional Programming?
Step2: The previous was the built-in "Deco... |
8,376 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'ec-earth3-lr', 'seaice')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contri... | <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... |
8,377 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', validation_size=0)
img = mnist.train.images[2]
plt.imshow(img.reshape((28, 28)), cmap='... | <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: Below I'm plotting an example image from the MNIST dataset. These are 28x28 grayscale images of handwritten digits.
Step2: We'll train an autoe... |
8,378 | <ASSISTANT_TASK:>
Python Code:
# Install dependencies with pip. Only run this once.
! pip install -q tf-nightly git+https://github.com/google-research/neural-structural-optimization.git
# Copyright 2019 Google LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in... | <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: MBB Beam (Figure 2 from paper)
Step2: If desired, designs can also be converted into PIL.Image objects with the pipeline_utils.image_from_desig... |
8,379 | <ASSISTANT_TASK:>
Python Code:
import great_expectations as ge
from ruamel import yaml
from great_expectations.core.batch import BatchRequest
from great_expectations.rule_based_profiler.rule.rule import Rule
from great_expectations.rule_based_profiler.rule_based_profiler import RuleBasedProfiler, RuleBasedProfilerResul... | <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-up
Step2: BatchRequests
Step3: Example 1
Step4: To continue our example, we will continue building a RuleBasedProfiler using our ColumnDo... |
8,380 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randint(0, 10, (3, 4)), columns=['A', 'B', 'C', 'D'])
df
np.cos(df * np.pi/2 ) - 1
A = pd.DataFrame(np.random.randint(0, 20, (2, 2)), columns=list('AB'))
A
B = pd.DataFrame(np.random.randint(0, 10, (3, 3)), columns=list(... | <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: Numpy operations
Step2: Arithmetic operations
Step3: The pandas arithmetic functions also have an option to fill missing values by replacing t... |
8,381 | <ASSISTANT_TASK:>
Python Code:
# Based on
# https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/6.2-understanding-recurrent-neural-networks.ipynb
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
%pylab inline
import matplotlib.pyplot as plt
import pandas as pd
import tensorfl... | <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: GRU RNNs
Step2: How does this work on anything that is not a real movie review?
|
8,382 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
from scipy import ndimage
from skimage import filters, data
import skimage as ski
myData = uint16(ski.exposure.rescale_intensity(ski.color.rgb2gray(data.lena()),out_range='uint16'))
matshow(myData, cmap = 'gray')
#float64 images
myData = ski.color.rgb2gray(data.lena())
ski_... | <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: Median filtering
Step2: It looks like skimage's median filter is only good for uint8 data types. If its a float it will be downgraded to uint8 ... |
8,383 | <ASSISTANT_TASK:>
Python Code:
products = pd.read_csv('amazon_baby_subset.csv')
products['name'][:10]
print (products['sentiment'] == 1).sum()
print (products['sentiment'] == -1).sum()
print (products['sentiment']).count()
import json
with open('important_words.json') as important_words_file:
important_words... | <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. listing the name of the first 10 products in the dataset.
Step2: 2. counting the number of positive and negative reviews.
Step3: Apply text... |
8,384 | <ASSISTANT_TASK:>
Python Code:
Functions for downloading and reading MNIST data.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import gzip
import os
import tempfile
import numpy
from six.moves import urllib
from six.moves import xrange # pylint: disable=re... | <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: T81-558
Step2: Define CNN
Step3: Training/Fitting CNN
Step4: Evaluate Accuracy
|
8,385 | <ASSISTANT_TASK:>
Python Code:
### General imports
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors as mcolors
import GPy
import time
### Emukit imports
from emukit.test_functions import forrester_function
from emukit.core.loop.user_function import UserFunctionWrapper
... | <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 up our toy problem (1D optimisation of the forrester function) and collect 3 initial points.
Step2: Fit our GP model to the observed data.
... |
8,386 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import os
from numpy import zeros_like
print('Esperamos trabalhar no diretório')
print(os.getcwd())
base = pd.read_csv('DOM2013.csv',sep=',')
base9 = pd.read_csv('DOM2009.csv',sep=',')
base.V0101=ba... | <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: MUDANÇA DA VARIÁVEL INICIAL QUE MOSTRA O ANO DE PESQUISA.
Step2: DEFINIÇÃO DAS REGIÕES E TRANSFORMAÇÃO EM UMA CATEGORIA;
Step3: DIVISÃO EM ZON... |
8,387 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
#%matplotlib notebook
import matplotlib
matplotlib.rcParams['figure.figsize'] = (9, 9)
import pandas as pd
url = "https://www.data.gouv.fr/fr/datasets/r/1fee314d-c278-424f-a029-a74d877eb185"
df2016 = pd.read_csv(url,
encoding='iso-8859-1',
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Informations utiles sur les données
Step2: Données par agglomération
Step3: Paris intra-muros
Step4: Remarque
Step5: Région Parisienne
|
8,388 | <ASSISTANT_TASK:>
Python Code:
class MulLayer:
def __init__(self):
self.x = None
self.y = None # 순전파시의 입력 값을 유지하기 위해 사용
def forward(self, x, y):
self.x = x
self.y = y
out = x * y
return out
def backward(self, dout):
dx = dout * s... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 5.5 활성화 함수 계층 구현하기
Step2: Sigmoid
Step3: 5.6 Affine / Softmax 계층 구현하기
Step4: 5.6.3 softmax-with-loss 계층
Step5: 5.7 오차역전파
Step6: 학습 구현
Step7... |
8,389 | <ASSISTANT_TASK:>
Python Code:
# set the midpoint
midpoint = 5
# make two empty lists
lower = []; upper = []
# split the numbers into lower and upper
for i in range(10):
if (i < midpoint):
lower.append(i)
else:
upper.append(i)
print("lower:", lower)
print("upper:", upper)
print(2*(3+4)... | <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: Este pequeño script muestra algunos aspectos importantes de la sintaxis de Python.
Step2: Los parentesis también se usan para pasar parámetros ... |
8,390 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pylab as plt
import numpy as np
import sys
sys.path.append('../')
from pyphot import sandbox as pyphot
from pyphot.svo import get_pyphot_filter as get_filter_from_svo
lst = ["2MASS/2MASS.J", "2MASS/2MASS.H", "2MASS/2MASS.Ks",
"HST/ACS_WFC.F475W", "HST/ACS... | <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: Quick Start
Step2: Suppose one has a calibrated spectrum and wants to compute the vega magnitude throug the HST WFC3 F110W passband,
|
8,391 | <ASSISTANT_TASK:>
Python Code:
import toytree
import toyplot
import numpy as np
# a tree to use for examples
url = "https://eaton-lab.org/data/Cyathophora.tre"
rtre = toytree.tree(url).root(wildcard='prz')
# hide tip labels
rtre.draw(tip_labels=False);
# get tip labels from tree
tipnames = rtre.get_tip_labels()
# modi... | <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: Tip label styling
Step2: tip_labels_align
Step3: tip_labels_colors
Step4: tip_labels_style
Step5: Node labels styling
Step6: node_labels_st... |
8,392 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'hadgem3-gc31-hm', 'seaice')
# 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... |
8,393 | <ASSISTANT_TASK:>
Python Code:
import os
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_filt-0-40_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file, verbose=False)
events_f... | <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: Annotating bad spans of data
Step2: .. sidebar
Step3: Now we can confirm that the annotations are centered on the EOG events. Since
Step4: Se... |
8,394 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pycomlink as pycml
import matplotlib.pyplot as plt
from tqdm import tqdm
cml_list = pycml.io.examples.get_75_cmls()
fig, ax = plt.subplots()
for cml in cml_list:
cml.plot_line(ax=ax, color='k')
for cml in tqdm(cml_list):
window_length = 60
threshold... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Load CML example data
Step2: Do a simple standard processing to get rain rates for each CML
Step3: Do IDW interpolation of CML rain rates
Step... |
8,395 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv("data-readonly/IL_Building_Inventory.csv")
df.columns
df.head()
df.tail()
df.describe()
df.dtypes
df.groupby(["Agency Name"])["Square Footage"].sum()
df["Agency Name"].value_counts()
df.describe()
df... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Pandas provides a number of read_* options, including read_csv, which we will use here.
Step2: One of the first things we can do is examine the... |
8,396 | <ASSISTANT_TASK:>
Python Code:
# 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, 'black'), (2, 'salmon')]
sampleDataRDD = sc.paralleli... | <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: (1b) Vetores Esparsos
Step2: (1c) Atributos OHE como vetores esparsos
Step4: (1d) Função de codificação OHE
Step5: (1e) Aplicar OHE em uma... |
8,397 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%reload_ext autoreload
%autoreload 2
from fastai.conv_learner import *
PATH = 'data/cifar10/'
os.makedirs(PATH, exist_ok=True)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
stats = (np.array([ 0.4914 , 0.48216, 0.44653]), 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: Something changed, or I forgot something, so I have to move the data into class folders.
Step2: 1. View Data
Step3: I am so happy that worked.... |
8,398 | <ASSISTANT_TASK:>
Python Code:
import pypot.dynamixel
ports = pypot.dynamixel.get_available_ports()
if not ports:
raise IOError('no port found!')
print 'ports found', ports
using_XL320 = False
my_baudrate = 1000000
for port in ports:
print port
try:
if using_XL320:
dxl_io = pypot.dynam... | <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: Protocol is not the same for XL320 servomotors, set the using_XL320 flag to True if you use them.
Step2: If the code below gives you an excepti... |
8,399 | <ASSISTANT_TASK:>
Python Code:
# Authors: Olaf Hauk <olaf.hauk@mrc-cbu.cam.ac.uk>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Nicolas P. Rougier (graph code borrowed from his matplotlib gallery)
#
# License: BSD-3-Clause
import numpy as n... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Load forward solution and inverse operator
Step2: Read and organise labels for cortical parcellation
Step3: Compute point-spread function summ... |
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