Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
|---|---|---|
9,800 | <ASSISTANT_TASK:>
Python Code:
import os
%%bash
export PROJECT=$(gcloud config list project --format "value(core.project)")
echo "Your current GCP Project Name is: "${PROJECT}
# TODO: Change these to try this notebook out
PROJECT = "cloud-training-demos" # Replace with your PROJECT
BUCKET = PROJECT # defaults to PRO... | <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: Lab Task #1
Step2: Check data exists
Step3: Now that we have the Keras wide-and-deep code working on a subset of the data, we can package the ... |
9,801 | <ASSISTANT_TASK:>
Python Code:
def p(n, m):
output = 0
for i in range(n):
output += i % m
return output
%timeit p(1000000, 42)
%%cython
def f(n, m):
output = 0
for i in range(n):
output += i % m
return output
%timeit f(1000000, 42)
%%cython
def c(int n):
cdef int i, output
... | <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: Still Python, but inside a Cython cell
Step2: Cython
Step3: Example of a while loop
Step6: Application
Step7: Quick demo
Step8: Speed test ... |
9,802 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import time
import gzip
import shutil
import seaborn as sns
from collections import Counter
from sklearn.mixture import GaussianMixture
from sklearn.cluster import KMeans, ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step 1 (clustering)
Step1: Do some preprocessing to group the data by 'Anon Stud Id' and extract features for further analysis
Step2: Note to reviewer... |
9,803 | <ASSISTANT_TASK:>
Python Code:
# A comma-delimited list of the words you want to train for.
# The options are: yes,no,up,down,left,right,on,off,stop,go
# All the other words will be used to train an "unknown" label and silent
# audio data with no spoken words will be used to train a "silence" label.
WANTED_WORDS = "yes... | <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: DO NOT MODIFY the following constants as they include filepaths used in this notebook and data that is shared during training and inference.
Ste... |
9,804 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nasa-giss', 'giss-e2-1h', 'toplevel')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("nam... | <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... |
9,805 | <ASSISTANT_TASK:>
Python Code:
import datetime
import logging
import os
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow import feature_column as fc
from tensorflow.keras import layers
from tensorflow.keras import models
# set TF error log verbosity
logging.getLogger("tensorflo... | <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 taxifare dataset
Step2: Let's check that the files were copied correctly and look like we expect them to.
Step3: Create an input pipeline... |
9,806 | <ASSISTANT_TASK:>
Python Code:
def total (m):
i=0
result=1
while i<m:
i+=1
result*=i
return result
m=int(input('please enter an integer. '))
n=int(input('please enter an integer. '))
k=int(input('please enter an integer. '))
print('The result of ',m,'!+',n,'!+',k,'! is :',total(m)+total(... | <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: 练习 2：写函数可返回1 - 1/3 + 1/5 - 1/7...的前n项的和。在主程序中，分别令n=1000及100000，打印4倍该函数的和。
Step2: 练习 3：将task3中的练习1及练习4改写为函数，并进行调用。
Step3: TASK3练习 4：英文单词单数转复数，要... |
9,807 | <ASSISTANT_TASK:>
Python Code:
from awips.dataaccess import DataAccessLayer
from awips.tables import vtec
from datetime import datetime
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATT... | <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: Next, we create a request for the "warning" data type
Step2: Now loop through each record and plot it as either Polygon or MultiPolygon, with a... |
9,808 | <ASSISTANT_TASK:>
Python Code:
project_dir = "/home/ubuntu/github/AstroWeekStudy/python_code/"
import sys
sys.path.append("/home/ubuntu/github/AstroWeekStudy/python_code/")
try:
reload(loadData)
reload(astroWeekLib)
except:
import loadData
import astroWeekLib
from loadData import *
from astroWeekLi... | <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. Study of Repository Creations
Step2: 2. Timeline Of Activity (active repos,actors, and number of events)
Step3: 3. Productive Bursts
Step4:... |
9,809 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from numpy import pi
from matplotlib import pyplot as plt
%matplotlib inline
import pyqg
from pyqg import diagnostic_tools as tools
L = 1000.e3 # length scale of box [m]
Ld = 15.e3 # deformation scale [m]
kd = 1./Ld # deformation wavenumber [m^... | <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
Step2: Initial condition
Step3: Run the model
Step4: Snapshots
Step5: pyqg has a built-in method that computes the vertical modes.
St... |
9,810 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-3', 'sandbox-2', 'aerosol')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor... | <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... |
9,811 | <ASSISTANT_TASK:>
Python Code:
%load_ext sql
# %sql postgresql://gpdbchina@10.194.10.68:55000/madlib
%sql postgresql://fmcquillan@localhost:5432/madlib
%sql select madlib.version();
%%sql
DROP TABLE IF EXISTS test_set;
CREATE TABLE test_set(
pred FLOAT8, -- predicted values
obs FL... | <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: Continuous variables
Step2: Binary classification
Step3: Run the Binary Classifier metrics function and View the True Positive Rate and the Fa... |
9,812 | <ASSISTANT_TASK:>
Python Code:
import glob
all_filenames = glob.glob('../data/names/*.txt')
print(all_filenames)
import unicodedata
import string
all_letters = string.ascii_letters + " .,;'"
n_letters = len(all_letters)
# Turn a Unicode string to plain ASCII, thanks to http://stackoverflow.com/a/518232/2809427
def unic... | <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 we have category_lines, a dictionary mapping each category (language) to a list of lines (names). We also kept track of all_categories (just... |
9,813 | <ASSISTANT_TASK:>
Python Code:
%%sh
pip install pandas
pip install scikit-learn
pip install tpot
from __future__ import print_function
import numpy as np
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold ,... | <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 Preprocessing
Step2: We procceed to run Paolo Bestagini's routine to include a small window of values to acount for the spatial component ... |
9,814 | <ASSISTANT_TASK:>
Python Code:
## Read in the Training Data and Instantiating the Photo-z Algorithm
%matplotlib inline
from astropy.table import Table
import numpy as np
import matplotlib.pyplot as plt
#data = Table.read('GTR-ADM-QSO-ir-testhighz_findbw_lup_2016_starclean.fits')
#JT PATH ON TRITON to training set after... | <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: Since we are running on separate test data, we don't need to do a train_test_split here. But we will scale the data. Need to remember to scale... |
9,815 | <ASSISTANT_TASK:>
Python Code:
#@title Python imports
import collections
import datetime
from functools import partial
import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from scipy import stats
import seaborn as sns
from sklearn.datasets import make_reg... | <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: Jax
Step2: Section 1
Step3: So we see that JAX gets the same answer by computing the derivative of the composite or by multiplying together th... |
9,816 | <ASSISTANT_TASK:>
Python Code:
import torch
from torchvision import datasets, transforms
# Define a transform to normalize the data
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5,... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Here I'll create a model like normal, using the same one from my solution for part 4.
Step2: The goal of validation is to measure the model's p... |
9,817 | <ASSISTANT_TASK:>
Python Code:
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
import os
import shutil
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard
from tensorflow... | <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: Exploring the data
Step2: Each image is 28 x 28 pixels and represents a digit from 0 to 9. These images are black and white, so each pixel is a... |
9,818 | <ASSISTANT_TASK:>
Python Code:
N = 10
theta_0 = 0.5
np.random.seed(0)
x = sp.stats.bernoulli(theta_0).rvs(N)
n = np.count_nonzero(x)
n
sp.stats.binom_test(n, N)
N = 100
theta_0 = 0.5
np.random.seed(0)
x = sp.stats.bernoulli(theta_0).rvs(N)
n = np.count_nonzero(x)
n
sp.stats.binom_test(n, N)
N = 100
theta_0 = 0.35
np.... | <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: 유의 확률(p-value)이 34%로 높으므로 귀무 가설을 기각할 수 없다. 따라서 $\theta=0.5$이다.
Step2: 유의 확률(p-value)이 92%로 높으므로 귀무 가설을 기각할 수 없다. 따라서 $\theta=0.5$이다.
Step3: 유의... |
9,819 | <ASSISTANT_TASK:>
Python Code:
import logging
from conf import LisaLogging
LisaLogging.setup()
# Generate plots inline
%matplotlib inline
import json
import os
# Support to access the remote target
import devlib
from env import TestEnv
from executor import Executor
# RTApp configurator for generation of PERIODIC tasks... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Import required modules
Step2: Target Configuration
Step3: Workload Execution and Functions Profiling Data Collection
Step4: Parse Trace and ... |
9,820 | <ASSISTANT_TASK:>
Python Code:
#--- Libraries
import pandas as pd # statistics packages
import numpy as np # linear algebra packages
import matplotlib.pyplot as plt # plotting routines
import seaborn as sns ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Specify region
Step2: Set data
Step3: Reanalysis
Step4: Once have full data set can then subdivide to create individual files for different r... |
9,821 | <ASSISTANT_TASK:>
Python Code:
import keras.backend as K
import numpy as np
# Placeholders and variables
x = K.placeholder()
target = K.placeholder()
lr = K.variable(0.1)
w = K.variable(np.random.rand())
b = K.variable(np.random.rand())
# Define model and loss
y = w * x + b
loss = K.mean(K.square(y-target))
grads = K... | <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: Model definition
Step2: Then, given the gradient of MSE wrt to w and b, we can define how we update the parameters via SGD
Step3: The whole mo... |
9,822 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
# load in data
import helper
data_dir = './data/Seinfeld_Scripts.txt'
text = helper.load_data(data_dir)
view_line_range = (0, 10)
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
import numpy as np
print('Dataset Stats')
print('Roughly the num... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: TV Script Generation
Step3: Explore the Data
Step6: Implement Pre-processing Functions
Step9: Tokenize Punctuation
Step11: Pre-process all t... |
9,823 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from __future__ import print_function
import mxnet as mx
import numpy as np
import matplotlib.pyplot as plt
record = mx.recordio.MXRecordIO('tmp.rec', 'w')
for i in range(5):
record.write('record_%d'%i)
record.close()
record = mx.recordio.MXRecordIO('tmp.rec', 'r'... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: The relevent code is under mx.recordio. There are two classes
Step2: Then we can read it back by opening the same file with 'r'
Step3: MXIndex... |
9,824 | <ASSISTANT_TASK:>
Python Code:
Setup the outcome map.
Rows correspond to vote types. Columns correspond to disposition types.
Element values correspond to:
* -1: no precedential issued opinion or uncodable, i.e., DIGs
* 0: affirm, i.e., no change in precedent
* 1: reverse, i.e., change in precent
outcome_map = pand... | <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: Disposition outcoming coding
Step8: Running a simulation
Step9: Predicting case outcomes with court reversal rate
Step10: Predicting case out... |
9,825 | <ASSISTANT_TASK:>
Python Code:
from learning import *
from notebook import psource, pseudocode
psource(NeuralNetLearner)
pseudocode('Back-Prop-Learning')
psource(BackPropagationLearner)
iris = DataSet(name="iris")
iris.classes_to_numbers()
nNL = NeuralNetLearner(iris)
print(nNL([5, 3, 1, 0.1]))
<END_TASK> | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: NEURAL NETWORK ALGORITHM
Step2: BACKPROPAGATION
Step3: Implementation
|
9,826 | <ASSISTANT_TASK:>
Python Code:
import random as rd
import numpy as np
from numpy.random import choice
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline
matplotlib.style.use('ggplot')
matplotlib.rc_params_from_file("../styles/matplotlibrc" ).update()
list_of_categories = ["H", "T"]
def initializeExpe... | <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: Constructing the experiment
Step2: Next we need to check if the last N throws have been equal to the category we want to observe. To do this we... |
9,827 | <ASSISTANT_TASK:>
Python Code:
# If we're running on Colab, install empiricaldist
# https://pypi.org/project/empiricaldist/
import sys
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
!pip install empiricaldist
# Get utils.py
from os.path import basename, exists
def download(url):
filename = basename(url)
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: This chapter introduces two related topics
Step2: Each update uses the same likelihood, but the changes in probability are not the same. The f... |
9,828 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import seaborn
%matplotlib inline
x = np.linspace(-3, 3, 100)
plt.plot(x, x**2, label='f(x)') # optymalizowana funkcja
plt.plot(x, 2 * x, label='pochodna -- f\'(x)') # pochodna
plt.legend()
plt.show()
learning_rate = ...
nb_steps = 10
... | <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: Funkcja ta ma swoje minimum w punkcie $x = 0$. Jak widać na powyższym rysunku, gdy pochodna jest dodatnia (co oznacza, że funkcja jest rosnąca) ... |
9,829 | <ASSISTANT_TASK:>
Python Code:
!pip install -r requirements.txt --user --quiet
import sys, os
from tqdm import tqdm
import subprocess
import numpy as np
import pandas as pd
from scipy import stats
import matplotlib.pyplot as plt
import zipfile
import joblib
import gc
from lightgbm import LGBMRegressor
from sklearn.met... | <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: Imports
Step2: Project hyper-parameters
Step3: Set random seed for reproducibility and ignore warning messages.
Step4: Download and load the ... |
9,830 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
index = ['x', 'y']
columns = ['a','b','c']
dtype = [('a','int32'), ('b','float32'), ('c','float32')]
values = np.zeros(2, dtype=dtype)
df = pd.DataFrame(values, index=index)
<END_TASK> | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
9,831 | <ASSISTANT_TASK:>
Python Code:
import plotly
from pageviews import *
from wiki_parser import *
import plotly.tools as tls
from helpers_parser import *
from across_languages import *
plotly.tools.set_credentials_file(username='crimenghini', api_key='***')
# Define the path of the corpora
path = '/Users/cristinamenghini... | <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. Find articles <a name="parse"></a>
Step2: After having a quick peek at a snippet of the XML. The elements we are interested in are on the ch... |
9,832 | <ASSISTANT_TASK:>
Python Code:
# import statements to make numeric and plotting functions available
%matplotlib inline
from numpy import *
from matplotlib.pyplot import *
def hill_activating(X, B, K, n):
Hill function for an activator
return (B * X**n)/(K**n + X**n)
## generate a plot using the hill_activatin... | <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: Modeling Gene Networks Using Ordinary Differential Equations
Step2: Visualizing the activating Hill function
Step3: <h2> <font color='firebric... |
9,833 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import gzip
import json
import numpy as np
def read_data(fname):
with gzip.open(fname) as fpt:
d = json.loads(str(fpt.read(), encoding='utf-8'))
return d
%matplotlib inline
plt.figure(figsize=(20, 10))
mx_pos = read_da... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Efecto oscilatorio cuyo periodo corresponde a los días de la semana.
Step2: Análisis de polaridad en Estados Unidos, Argentina, México y España... |
9,834 | <ASSISTANT_TASK:>
Python Code:
# To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
try:
# %tensorflow_version only exists in Colab.
%tensorflow_version 1.x
except Exception:
pass
# to make this notebook's out... | <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: Then of course we will need TensorFlow
Step2: Basic RNNs
Step3: Using static_rnn()
Step4: Packing sequences
Step5: Using dynamic_rnn()
Step6... |
9,835 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
def f(x):
return 2-x-np.exp(-x)
x = np.linspace(-10, 10, 400)
y = f(x)
plt.figure()
plt.plot(x, y)
# melhorando a escala para visualizar as possíveis raízes
plt.figure()
plt.plot(x, y)
plt.hlines(0,x.min(),x.max(),colors='C1',linestyl... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Note que a função tem duas raízes no entanto, por este método não temos como convergir para a raíz negativa. Uma alternativa é obter maneiras di... |
9,836 | <ASSISTANT_TASK:>
Python Code:
# Install all dependencies for this example.
! pip install ray gradio transformers requests
import gradio as gr
from ray import serve
from transformers import pipeline
import requests
serve.start()
@serve.deployment
def model(request):
language_model = pipeline("text-generation", mo... | <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: Deploying a model with Ray Serve
Step2: Next, we define a Ray Serve deployment with a GPT-2 model, by using the @serve.deployment decorator on ... |
9,837 | <ASSISTANT_TASK:>
Python Code:
from keras.datasets import mnist
(X_raw, y_raw), (X_raw_test, y_raw_test) = mnist.load_data()
n_train, n_test = X_raw.shape[0], X_raw_test.shape[0]
import matplotlib.pyplot as plt
import random
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
for i in range(15):
plt.... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 可视化 mnist
Step2: 练习：合成数据
Step3: 问题 1
Step4: 问题 2
Step5: 问题 3
Step6: 问题 4
Step7: 保存模型
|
9,838 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
def np_fact(n):
Compute n! = n*(n-1)*...*1 using Numpy.
if n==0:
c=1 # this makes it so if 0 is imputed a 1 is reurned
return c
else:
a=np.ar... | <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: Factorial
Step3: Write a function that computes the factorial of small numbers using a Python loop.
Step4: Use the %timeit magic to time both ... |
9,839 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import mpld3
mpld3.enable_notebook()
# Get the dataset:
from clustering import create_cluster_dataset, NewspaperArchive
DBFILE = "1749_1750_no_drift.db"
n = NewspaperArchive()
ds = create_cluster_dataset(n, daterange = [1749, 1750], dbfile = DBFILE)
data, transform, id... | <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 do these 'vectors' look like? What do the columns refer to?
Step2: Going from a vector back to the metadata reference
Step3: Initial data... |
9,840 | <ASSISTANT_TASK:>
Python Code:
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
import os
import gensim
# Set file names for train and test data
test_data_dir = os.path.join(gensim.__path__[0], 'test', 'test_data')
lee_train_file = os.path.join(test_data_dir, '... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Doc2Vec is a core_concepts_model that represents each
Step2: Define a Function to Read and Preprocess Text
Step3: Let's take a look at the tra... |
9,841 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
X_train, y_train = mnist.train.images, mnist.train.labels
X_validation, y_validation = mnist.validation.images, mnist.validation.labe... | <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: Section 1
Step2: <b> Part 2 </b>
Step3: <b> Question 2.1.2. </b> Calculate the number of parameters of this model
Step4: Your answer goes ... |
9,842 | <ASSISTANT_TASK:>
Python Code:
def countTrailingZero(x ) :
count = 0
while(( x & 1 ) == 0 ) :
x = x >> 1
count += 1
return count
if __name__== ' __main __' :
print(countTrailingZero(11 ) )
<END_TASK>
| <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
9,843 | <ASSISTANT_TASK:>
Python Code:
import sys
sys.version
import warnings
warnings.simplefilter('ignore', FutureWarning)
from pandas import *
show_versions()
df = read_csv('WHO POP TB all.csv')
df.head()
df.head(10)
df.tail(5)
df.columns
df.iloc[0] # first row, index 0
df.iloc[2] # third row, index 2
df.head() # first ... | <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: Exercise 1
Step2: Dataframe attributes
Step3: Dataframe rows
Step4: The <code>head()</code> method
Step5: The <code>tail()</code> method
Ste... |
9,844 | <ASSISTANT_TASK:>
Python Code:
import random
gameStake = 50
cards = range(10)
class Player:
# create here two local variables to store a unique ID for each player and the player's current 'pot' of money
# [FILL IN YOUR VARIABLES HERE]
# in the __init__() function, use the two input variables t... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: First we will establish some general variables for our game, including the 'stake' of the game (how much money each play is worth), as well as a... |
9,845 | <ASSISTANT_TASK:>
Python Code:
from pint import UnitRegistry
import sympy
import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
import sys
%matplotlib inline
from IPython.display import display
from Section import Section
ureg = UnitRegistry()
sympy.init_printing()
A, A0, t, t0, a, b, h, L, E, G =... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Import Section class, which contains all calculations
Step2: Initialization of sympy symbolic tool and pint for dimension analysis (not really ... |
9,846 | <ASSISTANT_TASK:>
Python Code:
## Utility functions.
def revcomp(sequence):
"returns reverse complement of a string"
sequence = sequence[::-1].strip()\
.replace("A", "t")\
.replace("T", "a")\
.replace("C", "g")\
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Handy function for manufacturing a psuedo-genome with guaranteed hits from simulated data
Step2: Insert SE and PE reads into simulated genome
S... |
9,847 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cnrm-cerfacs', 'cnrm-esm2-1-hr', 'seaice')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor... | <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... |
9,848 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import ABCPRC as prc
import seaborn as sns
import scipy.stats as stats
def createData(beta,gamma,n=100,T=100,I0=1):
S,I = np.zeros(T),np.zeros(T)
S[0] = n-I0
I[0] = I0
eps=0.1
for i in np.arange(1,T):
if beta*S[i-1]*I[i-1]/n<0: print(beta*S[i-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: Create individual-based model
Step2: Generate some fake data
Step3: Check the shape of the output. $T=100$, so the number of pairs should be $... |
9,849 | <ASSISTANT_TASK:>
Python Code:
# Author: Marijn van Vliet <w.m.vanvliet@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
from scipy.signal import welch, coherence
from mayavi import mlab
from matplotlib import pyplot as plt
import mne
from mne.simulation import simulate_raw
from mne.datas... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Setup
Step3: Data simulation
Step4: Let's simulate two timeseries and plot some basic information about them.
Step5: Now we put the signals a... |
9,850 | <ASSISTANT_TASK:>
Python Code:
from sympy.parsing.latex import parse_latex
parse_latex(r'\frac{x^2}{\sqrt{y}}')
import numpy as np
import pandas as pd
x = np.empty([3], dtype=object)
x
x[0]=parse_latex(r'\frac{x^2}{\sqrt{y}}')
x
y = list(np.array([parse_latex(r'x+y=6'),parse_latex(r'x-y=0')]))
y
from sympy import 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: Solo nos queda pasar un string con la expresión LaTeX para que la función devuelva la expresión en código entendible por Sympy. NOTA
Step2: Eje... |
9,851 | <ASSISTANT_TASK:>
Python Code:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os
print("Required modules imported.")
# You should have checked out original Caffe
# git clone https://github.com/BVLC/caffe.git
# ... | <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 you can setup your root folder for Caffe below if you put it somewhere else. You should only be changing the path that's being set for CAFFE... |
9,852 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from karabo_data import RunDirectory
import matplotlib.pyplot as plt
import numpy as np
import re
import xarray as xr
run = RunDirectory('/gpfs/exfel/exp/SA1/201830/p900025/raw/r0150/')
df = run.get_dataframe(fields=[("*_XGM/*", "*.i[xy]Pos"), ("*_XGM/*", "*.photonFlux... | <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: Using pandas
Step2: We can now make plots to compare the parameters at the two XGM positions.
Step3: We can also export the dataframe to a CSV... |
9,853 | <ASSISTANT_TASK:>
Python Code:
import sklearn
import matplotlib.pyplot as plt
import scipy
import numpy as np
from keras.models import Sequential
from keras.layers.embeddings import Embedding
from keras.layers import Flatten, Activation, Merge
from keras.preprocessing.text import Tokenizer, base_filter
from keras.prepr... | <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: ...Depending on which environment you're running this from, you may find yourself needing to upgrade one of these libraries, which you can do by... |
9,854 | <ASSISTANT_TASK:>
Python Code:
import urllib, json, time
apiKey=""
if not apiKey:
print "Enter your API key for traffic data!"
exit(1)
origin="Empire State Building, NY"
destination="One World Trade Center, NY"
params = urllib.urlencode(
{'origin': origin,
'destination': destination,
'mode': ... | <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: Enter your Google Maps Directions API key below
Step2: Enter your origin and destination
Step3: Try to grab realtime traffic from Google
Step4... |
9,855 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from scipy.integrate import odeint
from IPython.html.widgets import interact, fixed
def solve_euler(derivs, y0, x):
Solve a 1d ODE using Euler's method.
Parameters
----------
... | <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: Euler's method
Step4: The midpoint method is another numerical method for solving the above differential equation. In general it is more accura... |
9,856 | <ASSISTANT_TASK:>
Python Code:
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
from google.cloud import bigquery
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
%%bigquery
SELECT
FORMAT_TIMESTAMP(
"%Y-%m-%d %H:%M:%S %Z", pickup_datetime) AS pi... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: <h3> Extract sample data from BigQuery </h3>
Step2: Let's increase the number of records so that we can do some neat graphs. There is no guara... |
9,857 | <ASSISTANT_TASK:>
Python Code:
from flexx import app, ui, react
app.init_notebook()
b = ui.Button(text='foo')
b
b.text('Push me!')
with ui.HBox() as hbox:
slider = ui.Slider(flex=0)
label = ui.Label(flex=1, text='xx')
hbox
@react.connect('slider.value')
def show_slider_value(v):
label.text(str(v))
clas... | <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: Displaying widgets
Step2: Widgets have many input signals to modify their appearance and behavior
Step3: Layout
Step4: React
Step5: Compound... |
9,858 | <ASSISTANT_TASK:>
Python Code:
from netCDF4 import Dataset
url = ('http://geoport.whoi.edu/thredds/dodsC/usgs/data2/rsignell/gdrive/'
'nsf-alpha/Data/MIT_MSEAS/MSEAS_Tides_20160317/mseas_tides_2015071612_2015081612_01h.nc')
nc = Dataset(url)
vtime = nc['time']
coords = nc['vgrid2']
vbaro = nc['vbaro']
itime = ... | <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: Extract lon, lat variables from vgrid2 and u, v variables from vbaro.
Step2: Using iris to create the CF object.
Step3: Now the phenomena.
Ste... |
9,859 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import seaborn as sns
sns.set_context("notebook", font_scale=1.5)
#import warnings
#warnings.filterwarnings("ignore")
import pandas as pd
tbl2 = pd.read_clipboard(#"http://iopscience.iop.org/0004-637X/785/2/159/suppdata/apj492858t2_ascii.txt",
sep='\t', 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: Table 2 - Photometry and Spectral Types for the Objects of Spectral Type M, or Slightly Earlier, Identified Toward Lupus 3
Step2: Table 4-
|
9,860 | <ASSISTANT_TASK:>
Python Code:
import sklearn
sklearn.__version__
import pandas as pd
import numpy as np
import time
import datetime as dt
import matplotlib.pyplot as plt
from sklearn import tree
from sklearn.model_selection import train_test_split
%matplotlib inline
column_names = pd.read_excel('langevincodebook.xlsx... | <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: Importing the data and the column names from the codebook
Step2: The time in the data is measured with MATLAB's absolute time, converting to a ... |
9,861 | <ASSISTANT_TASK:>
Python Code:
%load_ext sql
%sql mysql://steinam:steinam@localhost/sommer_2015
%%sql
select k.kd_id, k.kd_plz,
(select count(a.Au_ID) from auftrag a where a.au_kd_id = k.kd_id ) as AnzahlAuftr,
(select count(f.`f_id`) from fahrten f, auftrag a
where f.f_au_id = a.au_id and a.`au_kd... | <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: Sommer 2015
Step2: Warum geht kein Join ??
Step3: Der Ansatz mit Join funktioniert in dieser Form nicht, da spätestens beim 2. Join die Firma ... |
9,862 | <ASSISTANT_TASK:>
Python Code:
%cd ~/Documents/W261/hw10/
import os
import sys
spark_home = os.environ['SPARK_HOME'] = \
'/Users/davidadams/packages/spark-1.5.1-bin-hadoop2.6/'
if not spark_home:
raise ValueError('SPARK_HOME enviroment variable is not set')
sys.path.insert(0,os.path.join(spark_home,'python'))
sy... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: HW 10.0
Step2: HW 10.1
Step3: HW 10.1.1
Step4: HW 10.2
Step5: HW 10.3
Step6: The WSSE decreases with the number of iterations from 1 to 20 ... |
9,863 | <ASSISTANT_TASK:>
Python Code:
from astropy import time
from astropy import units as u
from poliastro.bodies import Sun, Earth, Jupiter
from poliastro.ephem import Ephem
from poliastro.frames import Planes
from poliastro.twobody import Orbit
from poliastro.plotting import StaticOrbitPlotter
from poliastro import iod
fr... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Parking orbit
Step2: Hyperbolic exit
Step3: Quoting "New Horizons Mission Design"
Step4: So it stays within the same order of magnitude. Whic... |
9,864 | <ASSISTANT_TASK:>
Python Code:
from os import environ
slack_hook = environ.get('IRE_CFJ_2017_SLACK_HOOK', None)
import json
import requests
# build a dictionary of payload data
payload = {
'channel': '#general',
'username': 'IRE Python Bot',
'icon_emoji': ':ire:',
'text': 'helllllllo!'
}
# turn it 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: Hello JSON
Step2: Using requests to post data
Step3: Formatting the data correctly
Step4: Send it off to Slack
|
9,865 | <ASSISTANT_TASK:>
Python Code:
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
from six.moves import range
pickle_file = 'notMNIST.pickle... | <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: First reload the data we generated in 1_notmnist.ipynb.
Step2: Reformat into a shape that's more adapted to the models we're going to train
Ste... |
9,866 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import bokeh.plotting as bkp
from mpl_toolkits.axes_grid1 import make_axes_locatable
%matplotlib inline
# read in readmissions data provided
hospital_read_df = pd.read_csv('data/cms_hospital_readmissions.csv')
# deal ... | <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: Preliminary analysis
|
9,867 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import keras
from keras.datasets import mnist
# fix random seed for reproducibility
np.random.seed(7)
# load data
(x_train,y_train), (x_test,y_test) = mnist.load_data()
# show the training size, test size, number of class
print("train size : ",x_train.shape)
print("test... | <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) Display some image samples using matplotlib.pyplot
Step2: 3) (If necessary) Reduce the number of training images (for quick training and sma... |
9,868 | <ASSISTANT_TASK:>
Python Code:
from theano.sandbox import cuda
%matplotlib inline
import utils; reload(utils)
from utils import *
from __future__ import division, print_function
#path = "data/dogscats/sample/"
path = "data/dogscats/"
model_path = path + 'models/'
if not os.path.exists(model_path): os.mkdir(model_path)
... | <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: Are we underfitting?
Step2: ...and load our fine-tuned weights.
Step3: We're going to be training a number of iterations without dropout, so i... |
9,869 | <ASSISTANT_TASK:>
Python Code:
def calculate_minimum_split(a , k ) :
p =[]
n = len(a )
for i in range(1 , n ) :
p . append(a[i ] - a[i - 1 ] )
p . sort(reverse = True )
min_sum = sum(p[: k - 1 ] )
res = a[n - 1 ] - a[0 ] - min_sum
return res
/ * Driver code * /
if __name__== "__main __":
ar... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
9,870 | <ASSISTANT_TASK:>
Python Code:
import os
import zipfile
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
%matplotlib inline
# Put files in current direction into a list
files... | <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: Unzipping files with house sales data
Step2: Loading Sales data, Sales Training data, and Sales Test data
Step3: Learning a multiple regressio... |
9,871 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
import string
import operator
from functools import reduce
s = '''
73167176531330624919225119674426574742355349194934
96983520312774506326239578318016984801869478851843
85861560789112949495459501737958331952853208805511
125406987471585238630507156... | <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: It seems that having s as a big string and repeatedly converting digits to ints is wasteful, so I convert s to be a list of ints and repeat the ... |
9,872 | <ASSISTANT_TASK:>
Python Code:
import pyspark
sc = pyspark.SparkContext(appName="my_spark_app")
sc
## just check that sc variables is not
print("is SpartContext loaded?", sc != '')
rdd = sc.parallelize([x for x in range(1000)],20)
rdd.getNumPartitions()
rdd.take(5)
rdd.setName("my_rdd").persist(pyspark.StorageLeve... | <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: Interactive programming
Step2: Answer 2
Step3: Answer 3
Step4: Answer 4
Step5: Answer 5
|
9,873 | <ASSISTANT_TASK:>
Python Code:
import os
# The Vertex AI Workbench Notebook product has specific requirements
IS_WORKBENCH_NOTEBOOK = os.getenv("DL_ANACONDA_HOME")
IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(
"/opt/deeplearning/metadata/env_version"
)
# Vertex AI Notebook requires dependencies to be install... | <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: Restart the kernel
Step2: Before you begin
Step3: Get your project number
Step4: Region
Step5: Timestamp
Step6: Authenticate your Google Cl... |
9,874 | <ASSISTANT_TASK:>
Python Code:
def kthgroupsum(k ) :
cur = int(( k *(k - 1 ) ) + 1 )
sum = 0
while k :
sum += cur
cur += 2
k = k - 1
return sum
k = 3
print(kthgroupsum(k ) )
<END_TASK>
| <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
9,875 | <ASSISTANT_TASK:>
Python Code:
url = None
key = None
import sys
from docplex.cp.model import *
mdl0 = CpoModel()
masonry = mdl0.interval_var(size=35)
carpentry = mdl0.interval_var(size=15)
plumbing = mdl0.interval_var(size=40)
ceiling = mdl0.interval_var(size=15)
roofing = mdl0.interval_var(size=5)
painting = mdl0.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: Scheduling building blocks
Step2: This code creates a CP model container that allows the use of constraints that are specific to constraint pro... |
9,876 | <ASSISTANT_TASK:>
Python Code:
# As usual, a bit of setup
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from cs231n.solver impo... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Fully-Connected Neural Nets
Step4: Affine layer
Step5: Affine layer
Step6: ReLU layer
Step7: ReLU layer
Step8: "Sandwich" layers
Step9: Lo... |
9,877 | <ASSISTANT_TASK:>
Python Code:
#@title License
# 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 ... | <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: Single image to MPI example Colab
Step2: Set up the model
Step3: Generate an MPI from an input image, show layers and disparity
Step4: Genera... |
9,878 | <ASSISTANT_TASK:>
Python Code:
import deepchem as dc
import tensorflow as tf
import numpy as np
tasks, datasets, transformers = dc.molnet.load_tox21()
train_dataset, valid_dataset, test_dataset = datasets
n_tasks = len(tasks)
n_features = train_dataset.X.shape[1]
model = dc.models.MultitaskClassifier(n_tasks, n_feature... | <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 want to train the model using the training set, then evaluate it on the test set. As our evaluation metric we will use the ROC AUC, averaged... |
9,879 | <ASSISTANT_TASK:>
Python Code:
npz_file = "../model-mlp_n100-e100.txt.npz"
# Load network params
with np.load(npz_file) as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
print_statement = "Number of params: %s" % len(param_values)
print(print_statement)
print("="*len(print_statement))
for i in p... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Inspect params
Step2: weights for input to hidden layer
Step3: Quick visual of weights
Step5: Weight of some regions are more variable than o... |
9,880 | <ASSISTANT_TASK:>
Python Code:
!pip install -I "phoebe>=2.0,<2.1"
%matplotlib inline
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_compute()
b.set_value_all('mesh_method', 'wd')
# TODO: for now only the 'graham'... | <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: As always, let's do imports and initialize a logger and a new bundle. See Building a System for more details.
Step2: Changing Meshing Options
... |
9,881 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin
class MyClassifier(BaseEstimator, ClassifierMixin):
An example classifier
def __init__(self, param1=1, param2=2):
Called when initializing the classifier
The constructor is used to ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step4: <!--BOOK_INFORMATION-->
Step5: The classifier can be instantiated as follows
Step6: You can then fit the model to some arbitrary data
Step7: ... |
9,882 | <ASSISTANT_TASK:>
Python Code:
#import packages
import warnings
warnings.simplefilter("ignore")
import sys
import nibabel as nib
import numpy as np
import os
from PIL import Image, ImageDraw,ImageFont
import matplotlib.pyplot as plt
from m2g.stats.qa_skullstrip import gen_overlay_pngs
def gen_overlay_pngs(
brain, ... | <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: gen_overlay_pngs
Step4: plot_overlays_skullstrip
Step5: Inputs
Step6: Run AFNI 3dSkullStrip to do skull strip
Step7: Run qa_skullstrip.py
|
9,883 | <ASSISTANT_TASK:>
Python Code:
from dist_time import *
import pandas as pd
from collections.abc import Iterator, Iterable, Generator
from collections import defaultdict
import re
from sqlalchemy import create_engine
from sqlalchemy.types import Integer, Text, DateTime, Float, VARCHAR
from sqlalchemy import Table, MetaD... | <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: NC件
Step2: R类
Step3: D类
Step4: s类
Step5: I类
Step6: U-U类
Step7: concat
|
9,884 | <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-hr', '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... |
9,885 | <ASSISTANT_TASK:>
Python Code:
from transitions import Machine
import json
class Model:
def say_hello(self, name):
print(f"Hello {name}!")
# import json
json_config =
{
"name": "MyMachine",
"states": [
"A",
"B",
{ "name": "C", "on_enter": "say_hello" }
],
"transitions": [
["go", "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: Frequently asked questions
Step3: Loading a YAML configuration
Step4: Exporting YAML or JSON
Step5: How to use transitions with django models... |
9,886 | <ASSISTANT_TASK:>
Python Code:
# This useful nonsense just goes at the top
from datascience import *
import numpy as np
import matplotlib.pyplot as plots
plots.style.use('fivethirtyeight')
%matplotlib inline
# datascience version number of last run of this notebook
version.__version__
raw_berkeley_sal_2011 = Table.rea... | <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: Reading raw data into a Table
Step2: Accessing data in a Table
Step3: Some prefer the selectors - column and row
Step4: Rows in the table can... |
9,887 | <ASSISTANT_TASK:>
Python Code:
# http://api.census.gov/data/2010/surname
import requests
import json
import pandas as pd
import matplotlib.pyplot as plt
# First, get the basic info about the dataset.
# References: Dataset API (https://api.census.gov/data/2010/surname.html)
# Requests API (http://docs.pyth... | <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. Basic interactions with the Census dataset API
Step2: Get surname data
Step3: Laying out the API response like a table helps illustrate wha... |
9,888 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'noresm2-lmec', 'atmos')
# 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
<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... |
9,889 | <ASSISTANT_TASK:>
Python Code:
import json
import os
import boto3
s3 = boto3.client("s3")
# create instance
response = !aws ec2 run-instances --image-id ami-a9d276c9 \
--count 1 \
--instance-type t2.micro \
--key-name ec2_rob \
--security-groups ssh_only \
--block-device-mappings file://examples/bl... | <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: <hr>
Step2: You can specify a script to run on startup with the <code>--user-data</code> option. This enables you to do things like automatical... |
9,890 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import scipy.sparse as sp
import scipy.sparse.linalg as sla
# autoreload the lattice module so that we can make changes to it
# without restarting the ipython notebook server
%load_ext autoreload
%autoreload 1
%aimpor... | <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: Next import the dvr_1d module. We import dvr_1d using a series of ipython notebook magic commands so that we can make changes to the module file... |
9,891 | <ASSISTANT_TASK:>
Python Code:
#!pip install google-cloud-bigquery
%load_ext google.cloud.bigquery
PROJECT='cloud-training-demos' # CHANGE THIS
%%bigquery --project $PROJECT
SELECT
start_station_name
, AVG(duration) as duration
, COUNT(duration) as num_trips
FROM `bigquery-public-data`.london_bicycles.cycle_h... | <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: Run a query
Step2: Run a parameterized query
Step3: Into a dataframe
|
9,892 | <ASSISTANT_TASK:>
Python Code:
# remove display of install details
%%capture --no-display
!pip install ipyparallel
# authorize Google to access Google drive files
!pip install -U -q PyDrive
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client i... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: End of Warning
Step2: Problem 1) Light Curve Data
Step3: As we have many light curve files (in principle as many as 37 billion...), we will de... |
9,893 | <ASSISTANT_TASK:>
Python Code:
!pip install -I "phoebe>=2.2,<2.3"
import phoebe
from phoebe import u # units
import numpy as np
import matplotlib.pyplot as plt
logger = phoebe.logger()
b = phoebe.default_binary()
times = np.linspace(0,1,21)
b.add_dataset('lc', times=times, dataset='lc01')
b.add_dataset('rv', times=ti... | <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: As always, let's do imports and initialize a logger and a new bundle. See Building a System for more details.
Step2: Adding Datasets
Step3: R... |
9,894 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
# Use Floyd's cifar-10 dataset if ... | <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: Image Classification
Step2: Explore the Data
Step5: Implement Preprocess Functions
Step8: One-hot encode
Step10: Randomize Data
Step12: Che... |
9,895 | <ASSISTANT_TASK:>
Python Code:
import os
import sys
from glob import glob
import json
import numpy as np
from astropy.table import Table
from Chandra.Time import DateTime
from Ska.Matplotlib import plot_cxctime
from chandra_aca import drift
import parse_cm
TEST_DIR = '/proj/sot/ska/ops/SFE/JUL0415O/oflso'
dynam_table ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Check dynamic offset file for consistency
Step2: Run the ACA model and get new offsets
Step3: Compare values to dynamic offset table from Matl... |
9,896 | <ASSISTANT_TASK:>
Python Code:
import gachon_autograder_client as g_autograder
EMAIL = "#YOUR_EMAIL"
PASSWORD = "#YOUR_PASSWORD"
ASSIGNMENT_NAME = "nb_test"
g_autograder.get_assignment(EMAIL, PASSWORD, ASSIGNMENT_NAME)
def addition(a, b):
result = None
return result
# 실행결과
print (addition(5, 3))
print (additio... | <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: 위 소스 코드를 .py 파일 또는 jupyter notebook에 입력하여 파이썬으로 실행 시키면 "nb_arithmetic_functions.ipynb" 파일이 생성되며, jupyter notebook으로 실행하거나, 콘솔창(cmd)에서 해당 파일이 있는 ... |
9,897 | <ASSISTANT_TASK:>
Python Code::
from sklearn.metrics import mean_squared_error
rmse = mean_squared_error(y_test, y_pred, squared = False)
<END_TASK>
| <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
9,898 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import scipy.io
import scipy.optimize
import matplotlib.pyplot as plt
%matplotlib inline
# uncomment for console - useful for debugging
# %qtconsole
ex3data1 = scipy.io.loadmat("./ex4data1.mat")
X = ex3data1['X']
y = ex3data1['y'][:,0]
m, n = X.shape
m, n
input_layer_si... | <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: Part 1
Step2: Part 2
Step3: Unrolling the parameters into one vector
Step4: Part 3
Step5: The cost at the given parameters should be about 0... |
9,899 | <ASSISTANT_TASK:>
Python Code:
import py2neo
import pandas as pd
graph= py2neo.Graph()
query=
MATCH
(s1:Subdomain)<-[:BELONGS_TO]-
(type:Type)-[r:DEPENDS_ON*0..1]->
(dependency:Type)-[:BELONGS_TO]->(s2:Subdomain)
RETURN s1.name as from, s2.name as to, COUNT(r) as x_number
result = graph.run(query).data()
d... | <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: Optimierung des fachlichen Schnitts
Step2: Graph-Abfrage-Ergebnis
Step3: Visualisierungsdaten
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.