Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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100 | <ASSISTANT_TASK:>
Python Code:
After measuring the height of your family, you decide to collect some information
on the house you're living in. The areas of the different parts of your house
are stored in separate variables for now, as shown in the script.
Instructions:
+ Create a list, "areas", that contain the ar... | <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: List data structure
Step3: 2. Creating list with different types - 100xp, Status
Step4: 3. Select the valid list -- 50xp, status
Step6: 4 L... |
101 | <ASSISTANT_TASK:>
Python Code:
!pip install -U tensorflow_transform
!pip install pyarrow
import pkg_resources
import importlib
importlib.reload(pkg_resources)
import os
import tempfile
import tensorflow as tf
import tensorflow_transform as tft
import tensorflow_transform.beam as tft_beam
from tensorflow_transform.tf_me... | <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 a preprocessing function
Step2: Here, x, y and s are Tensors that represent input features. The first
Step3: The transformed_data conte... |
102 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
from dtw import dtw
x = array([0, 0, 1, 1, 2, 4, 2, 1, 2, 0]).reshape(-1, 1)
y = array([1, 1, 1, 2, 2, 2, 2, 3, 2, 0]).reshape(-1, 1)
plot(x)
plot(y)
dist, cost, acc, path = dtw(x, y, dist=lambda x, y: norm(x - y, ord=1))
print 'Minimum distance found:', dist
imshow(acc.... | <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 dtw module contains a single function named dtw as well.
Step2: Let's define two sequences
Step3: Compute DTW
Step4: You can plot the acc... |
103 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import advancedfilters as af
def buildTestVolume(size,sigma) :
vol = np.zeros([size,size,size])
margin = size // 4
vol[margin:-margin,margin:-margin,margin:-margin]=1
vol = vol + np.random.normal(0,1,size=vol.shape)*sigma... | <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: Make some test data
Step2: The cube has the dimensions 40x40x40 voxels and the StdDev of the noise is 0.5.
Step3: The filter operates inplace,... |
104 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'nicam16-9d-l78', '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
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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... |
105 | <ASSISTANT_TASK:>
Python Code:
import rebound
import reboundx
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
def makesim():
sim = rebound.Simulation()
sim.G = 4*np.pi**2
sim.add(m=1.)
sim.add(m=1.e-4, a=1.)
sim.add(m=1.e-4, a=1.5)
sim.move_to_com()
return sim
sim = ma... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: We now consider a first-order Kepler splitting (Wisdom-Holman map)
Step2: We now set sim.integrator to none, so that REBOUND doesn't do anythin... |
106 | <ASSISTANT_TASK:>
Python Code:
import os.path as op
import numpy as np
import mne
data_path = mne.datasets.sample.data_path()
fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(fname)
raw.set_eeg_reference() # set EEG average reference
order = np.arange(raw.info['nchan'])
... | <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 MNE, epochs refers to a collection of single trials or short segments
Step2: To create time locked epochs, we first need a set of events tha... |
107 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
view_sentence_range = (0, 10)
DON'T MODIFY AN... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Language Translation
Step3: Explore the Data
Step6: Implement Preprocessing Function
Step8: Preprocess all the data and save it
Step10: Chec... |
108 | <ASSISTANT_TASK:>
Python Code:
# Import networkx and also matplotlib.pyplot for visualization
import networkx as nx
import matplotlib.pyplot as plt
%matplotlib inline
# Create an empty undirected graph
G = nx.Graph()
# Add some nodes and edges. Adding edges aslo adds nodes if they don't already exist.
G.add_node('Jan... | <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: Undirected Graphs
Step2: Directed Graphs
Step3: What can nodes be?
Step4: Reading in Data
|
109 | <ASSISTANT_TASK:>
Python Code:
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
%pylab inline
import matplotlib.pylab as plt
import numpy as np
from distutils.version import StrictVersion
import sklearn
print(sklearn.__version__)
assert StrictVersion(sklearn.__version__ ) >= StrictVersion('0.18.1')
... | <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: This script goes along the blog post
Step2: Next step is to use those saved bottleneck feature activations and train our own, very simple fc la... |
110 | <ASSISTANT_TASK:>
Python Code:
import os, pdb
import fitsio
import numpy as np
import matplotlib.pyplot as plt
from glob import glob
from astropy.table import vstack, Table
from astrometry.libkd.spherematch import match_radec
import seaborn as sns
sns.set(context='talk', style='ticks', font_scale=1.4)
%matplotlib inlin... | <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 the SDSS training sample.
Step2: Next, assemble the full catalog of forced-PSF Gaia sources from DR8.
Step3: Make some plots and develop ... |
111 | <ASSISTANT_TASK:>
Python Code:
m = 50000 # timesteps
dt = 1/ 250.0 # update loop at 250Hz
t = np.arange(m) * dt
freq = 0.05 # Hz
amplitude = 5.0 # meter
alt_true = 405 + amplitude * np.cos(2 * np.pi * freq * t)
height_true = 6 + amplitude * np.cos(2 * np.pi * freq * t)
vel_true = - amplitude * (2 * np.pi *... | <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) MEASUREMENTS
Step2: Baro
Step3: GPS
Step4: GPS velocity
Step5: Acceleration
Step6: III) PROBLEM FORMULATION
Step7: Initial uncertainty... |
112 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Deep & Cross Network (DCN)
Step2: Toy Example
Step3: Let's generate the data that follows the distribution, and split the data into 90% for tr... |
113 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
view_sentence_range = (0, 10)
DON'T MODIFY ANYTHING IN THIS CELL
import num... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: TV Script Generation
Step3: Explore the Data
Step6: Implement Preprocessing Functions
Step9: Tokenize Punctuation
Step11: Preprocess all the... |
114 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
np.argmax(np.bincount([0, 0, 1],
weights=[0.2, 0.2, 0.6]))
ex = np.array([[0.9, 0.1],
[0.8, 0.2],
[0.4, 0.6]])
p = np.average(ex,
axis=0,
weights=[0.2, 0.2, 0.6])
p
np.argmax(p)
from ... | <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: Certain classifiers in scikit-learn can also return the probability of a predicted class label via the predict_proba method. Using the predicted... |
115 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
#Physical Constants (SI units)
G=6.67e-11 #Universal Gravitational constant in m^3 per kg per s^2
AU=1.5e11 #Astronomical Unit in meters = Distance between sun and earth
daysec=24.0*60*60 #seconds in a day
#####run sp... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Now we will define the physical constants of our system, which will also establish the unit system we have chosen. We'll use SI units here. Belo... |
116 | <ASSISTANT_TASK:>
Python Code:
print("Hello world!")
#wyrównanie do nawiasu otwierającego
foo = moja_dluga_funkcja(zmienna_jeden, zmienna_dwa
zmienna_trzy, zmienna_cztery)
# zwiększone wcięcia aby rozróżnić funkcję od ciała funkcji
def moja_dluga_funkcja(
zmienna_jeden, zmienna_dwa, zm... | <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: Uruchomiene Aplikacji
|
117 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import pandas as pd
import matplotlib.pylab as plt
import seaborn as sns
import statsmodels.api as sm
# read the data and inspect
titanic = pd.read_csv('titanic-data.csv')
print titanic.info()
titanic.head()
# drop those columns we are not interested in.
titanic.drop(["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: Name and Embarked are dropped from the dataset because passenger name and embarking location shouldn't have any meaningful correlation with thei... |
118 | <ASSISTANT_TASK:>
Python Code:
# TODO 1: Install TF.Text TensorFlow library
!pip install -q "tensorflow-text==2.8.*"
import tensorflow as tf
import tensorflow_text as text
hypotheses = tf.ragged.constant([['captain', 'of', 'the', 'delta', 'flight'],
['the', '1990', 'transcript']])
ref... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Please ignore any incompatibility warnings and errors.
Step2: ROUGE-L
Step3: The hypotheses and references are expected to be tf.RaggedTensors... |
119 | <ASSISTANT_TASK:>
Python Code:
from learntools.core import binder
binder.bind(globals())
from learntools.data_cleaning.ex5 import *
print("Setup Complete")
# modules we'll use
import pandas as pd
import numpy as np
# helpful modules
import fuzzywuzzy
from fuzzywuzzy import process
import chardet
# read in all our data... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Get our environment set up
Step2: Next, we'll redo all of the work that we did in the tutorial.
Step3: 1) Examine another column
Step4: Do yo... |
120 | <ASSISTANT_TASK:>
Python Code:
import torch
import numpy as np
torch.__version__
arr = np.array([1,2,3,4,5])
print(arr)
print(arr.dtype)
print(type(arr))
x = torch.from_numpy(arr)
# Equivalent to x = torch.as_tensor(arr)
print(x)
# Print the type of data held by the tensor
print(x.dtype)
# Print the tensor object typ... | <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: Confirm you're using PyTorch version 1.1.0
Step2: Converting NumPy arrays to PyTorch tensors
Step3: Here <tt>torch.DoubleTensor</tt> refers to... |
121 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import string
from pyspark import SparkContext
sc = SparkContext('local[*]')
ulysses = sc.textFile('data/Ulysses.txt')
ulysses.take(10)
num_lines = sc.accumulator(0)
def tokenize(line):
table = dict.fromkeys(map(ord, string.punctuation))
return line.translate(... | <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: Resources
Step2: Event counting
Step3: Broadcast Variables
Step4: The dictionary table is sent out twice to worker nodes, one for each call
S... |
122 | <ASSISTANT_TASK:>
Python Code:
pd.read_
"../class2/"
"data/Fatality.csv"
##Some code to run at the beginning of the file, to be able to show images in the notebook
##Don't worry about this cell
#Print the plots in this screen
%matplotlib inline
#Be able to plot images saved in the hard drive
from IPython.display impor... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 1. About python
Step2: 1.1 Jupyter notebook
Step3: NEW NOTEBOOK
Step4: BUTTONS TO REMOVE AND RENAME
Step5: CELLS IN JUPYTER NOTEBOOKS
Step6:... |
123 | <ASSISTANT_TASK:>
Python Code:
%matplotlib notebook
import xarray as xr
import datetime
import numpy as np
from dask.distributed import LocalCluster, Client
import s3fs
import cartopy.crs as ccrs
import boto3
import matplotlib.pyplot as plt
bucket = 'era5-pds'
#Make sure you provide / in the end
prefix = 'zarr/2008/01... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: First we look into the era5-pds bucket zarr folder to find out what variables are available. Assuming that all the variables are available for a... |
124 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
sns.set_style('white')
from scipy.interpolate import griddata
# YOUR CODE HERE
x = np.hstack((np.arange(-5, 6), np.full(10, 5), np.arange(-5, 5), np.full(9, -5), [0]))
y = np.hstack((np.full(11, 5... | <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: Sparse 2d interpolation
Step2: The following plot should show the points on the boundary and the single point in the interior
Step3: Use meshg... |
125 | <ASSISTANT_TASK:>
Python Code:
%%bigquery
SELECT
bqutil.fn.median(ARRAY_AGG(TIMESTAMP_DIFF(a.creation_date, q.creation_date, SECOND))) AS time_to_answer
FROM `bigquery-public-data.stackoverflow.posts_questions` q
JOIN `bigquery-public-data.stackoverflow.posts_answers` a
ON q.accepted_answer_id = a.id
%%bigquery
WIT... | <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: Find the error metric of always predicting that it will take 2120 seconds to get an answer. This the baseline metric against which to report mod... |
126 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
from tqdm import tqdm_notebook
#import tqdm
import magpy as mp
%matplotlib inline
def e_anisotropy(moments, anisotropy_axes, V, K, particle_id):
cos_t = np.sum(moments[particle_id, :]*anisotrop... | <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: Metropolis MCMC
Step2: Dipolar interaction energy
Step3: Total energy
Step4: The Monte-Carlo algorithm
Step5: Parameter set up
Step6: Run t... |
127 | <ASSISTANT_TASK:>
Python Code:
f1 = (1/2)*r_c*c**2+(1/4)*u_c*c**4+(1/6)*v_c*c**6-E*p+(1/2)*r_p*p**2-gamma*c*p
pmin = solve(f1.diff(c),p)[0]
pmin
E_cp = solve(f1.diff(p),E)[0]
E_cp
expand(E_cp.subs(p,pmin))
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: $\dfrac{\partial f_{1}(c,p)}{\partial p} = 0 = $
|
128 | <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'
class DLProgress(tqdm):
last_b... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Image Classification
Step2: Explore the Data
Step5: Implement Preprocess Functions
Step8: One-hot encode
Step10: Randomize Data
Step12: Che... |
129 | <ASSISTANT_TASK:>
Python Code:
from text import *
from utils import open_data
from notebook import psource
psource(UnigramWordModel, NgramWordModel, UnigramCharModel, NgramCharModel)
flatland = open_data("EN-text/flatland.txt").read()
wordseq = words(flatland)
P1 = UnigramWordModel(wordseq)
P2 = NgramWordModel(2, wor... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: CONTENTS
Step2: Next we build our models. The text file we will use to build them is Flatland, by Edwin A. Abbott. We will load it from here. I... |
130 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
x = np.random.randn(100)
y = np.random.randn(100)
plt.scatter(x, y, marker='*', color='red');
x = np.random.randn(100)
plt.hist(x, bins=5);
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Scatter plots
Step2: Histogram
|
131 | <ASSISTANT_TASK:>
Python Code:
for i in range(3):
a = i * 7 #0, 7, 14
b = i + 2 #2, 3, 4
c = a * b # 0, 21, 56
#만약 이 range값이 3017, 5033일 경우에는 무슨 값인지 알 수 없다. 이 때 쉽게 a,b,c값이 무엇인지 찾는 방법을 소개
name = "KiPyo Kim"
age = 29
from IPython import embed
embed()
for i in range(3):
a = i * 7
b = i + 2
c = a... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 디버깅? de+bugg+ing => 버그를 잡는다
Step2: 만약 안에 있는 것을 exit()로 종료하지 않고 dd로 밖에서 강제 종료할 경우
Step3: print를 쓰면 직관적으로 볼 수 있지만 안 좋은 이유는 결과에 영향을 미치기 때문
Step4:... |
132 | <ASSISTANT_TASK:>
Python Code:
import xarray as xr
from matplotlib import pyplot as plt
%matplotlib inline
from oocgcm.oceanmodels.nemo import grids
#- Parameter
coordfile = '/Users/lesommer/data/NATL60/NATL60-I/NATL60_coordinates_v4.nc'
maskfile = '/Users/lesommer/data/NATL60/NATL60-I/NATL60_v4.1_cdf_byte_mask.nc'
fi... | <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: with a 2D input xarray dataarray at a given time
Step2: with a 2D + time input xarray dataarray
Step3: Compute a laplacian
Step4: plotting th... |
133 | <ASSISTANT_TASK:>
Python Code:
%reload_ext autoreload
%autoreload 2
%matplotlib inline
from fastai.imports import *
from fastai.torch_imports import *
from fastai.transforms import *
from fastai.model import *
from fastai.dataset import *
from fastai.sgdr import *
from fastai.plots import *
from fastai.conv_learner imp... | <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. Initial Exploration
Step2: 3. Initial Model
Step3: 3.1 Precompute
Step4: 3.2 Augment
Step5: 3.3 Increase Size
Step6: 6. Individual Predi... |
134 | <ASSISTANT_TASK:>
Python Code:
# using Tensorflow 2
%tensorflow_version 2.x
import numpy as np
from matplotlib import pyplot as plt
import tensorflow as tf
print("Tensorflow version: " + tf.__version__)
#@title Display utilities [RUN ME]
from enum import IntEnum
import numpy as np
class Waveforms(IntEnum):
SINE1 = ... | <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: Generate fake dataset
Step2: Hyperparameters
Step3: Visualize training sequences
Step4: Prepare datasets
Step5: Peek at the data
Step6: Ben... |
135 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pickle as pkl
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')
def model_inputs(real_dim, z_dim):
inputs_real = tf.placeholde... | <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 Inputs
Step2: Generator network
Step3: Discriminator
Step4: Hyperparameters
Step5: Build network
Step6: Discriminator and Generator L... |
136 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import h5py
import scipy
from PIL import Image
from scipy import ndimage
from lr_utils import load_dataset
%matplotlib inline
# Loading the data (cat/non-cat)
train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dat... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 2 - Overview of the Problem set
Step2: We added "_orig" at the end of image datasets (train and test) because we are going to preprocess them. ... |
137 | <ASSISTANT_TASK:>
Python Code:
# We start with the usual import statements
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import climlab
# create a new model with all default parameters (except the grid size)
mymodel = climlab.EBM_annual(num_lat = 30)
# What did we just do?
print mymodel
mymo... | <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 is a climlab Process?
Step2: We have an array of temperatures in degrees Celsius. Let's see how big this array is
Step3: Every state var... |
138 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
USE_COLAB = False
if not USE_COLAB:
plt.rc("text", usetex=True)
import numpy as np
C = 10
alpha = -0.5
q = 0.9
num_iter = 10
sublinear = np.array([C * k**alpha for k in range(1, num_iter + 1)])
linear = np.array([C * q**k for k in ran... | <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: Значение теорем сходимости (Б.Т. Поляк Введение в оптимизацию, гл. 1, $\S$ 6)
Step2: $f(x) = x\log x$
Step3: Backtracking
Step4: Выбор шага
S... |
139 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import io
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas
import seaborn as sns
import skimage
import skimage.color
import skimage.data
import skimage.feature
import skimage.filters
import skimage.future
import skimage.io
import skimage.morpho... | <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: Color threshold
Step2: Based on the histogram of the hue, threshold the hue such that only the yellowish colors remain.
Step3: Add the cities ... |
140 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
df = pd.DataFrame({'product': [1179160, 1066490, 1148126, 1069104, 1069105, 1160330, 1069098, 1077784, 1193369, 1179741],
'score': [0.424654, 0.424509, 0.422207, 0.420455, 0.414603, 0.168784, 0.168749, 0.168738, 0.168703, 0.168684]})
products = [1066... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
|
141 | <ASSISTANT_TASK:>
Python Code:
import kfp
import kfp.gcp as gcp
import kfp.dsl as dsl
import kfp.compiler as compiler
import kfp.components as comp
import datetime
import kubernetes as k8s
# Required Parameters
PROJECT_ID='<ADD GCP PROJECT HERE>'
GCS_BUCKET='gs://<ADD STORAGE LOCATION HERE>'
# Optional Parameters, but... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Create client
Step2: Wrap an existing Docker container image using ContainerOp
Step3: Creating a Dockerfile
Step4: Build docker image
Step5: ... |
142 | <ASSISTANT_TASK:>
Python Code:
from IPython import parallel
c=parallel.Client()
dview=c.direct_view()
dview.block=True
c.ids
import numpy as np
x=np.arange(100)
dview.scatter('x',x)
print c[0]['x']
print c[1]['x']
print c[-1]['x']
dview.execute('import numpy as np; y=np.sum(x)')
ys=dview.gather('y')
total=np.sum(ys)... | <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: Check a number of cores
Step2: Simple parallel summation
Step3: Parallel sum
|
143 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
# check tf version
print(tf.__version__)
a = tf.constant(2)
b = tf.constant(5)
operation = tf.add(a, b, name='cons_add')
with tf.Session() as ses:
print ses.run(operation)
sub_operation = tf.subtract(a, b, name='cons_subtraction')
x = tf.constant([[-1.37 ... | <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: Config Contants
Step2: in the variable "b" we are going to assign a constant with the initial value of "5"
Step3: In the following variable "o... |
144 | <ASSISTANT_TASK:>
Python Code:
import json
series_types = ["Don't Know", "Other nonmetal", "Alkali metal",
"Alkaline earth metal", "Nobel gas", "Metalloid",
"Halogen", "Transition metal", "Post-transition metal",
"Lanthanoid", "Actinoid"]
class Element... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Python for STEM Teachers<br/>Oregon Curriculum Network
Step3: <div align="center">graphic by Kenneth Snelson</div>
|
145 | <ASSISTANT_TASK:>
Python Code:
%%capture
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/Data.zip
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/images.zip
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/Extra_Material.zip
!unzip Data.zip -d ../
!unz... | <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: Chapter 7 - Sets
Step2: Curly brackets surround sets, and commas separate the elements in the set
Step3: Please note that sets are unordered. ... |
146 | <ASSISTANT_TASK:>
Python Code:
print("Exemplo 4.11")
#Superposicao
#Analise Fonte de Tensao
#Req1 = 4 + 8 + 8 = 20
#i1 = 12/20 = 3/5 A
#Analise Fonte de Corrente
#i2 = 2*4/(4 + 8 + 8) = 8/20 = 2/5 A
#in = i1 + i2 = 1A
In = 1
#Req2 = paralelo entre Req 1 e 5
#20*5/(20 + 5) = 100/25 = 4
Rn = 4
print("Corrente In:",In,"A"... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Problema Prático 4.11
Step2: Exemplo 4.12
Step3: Problema Prático 4.12
Step4: Máxima Transferência de Potência
Step5: Problema Prático 4.13
|
147 | <ASSISTANT_TASK:>
Python Code:
# load json twitter data
twitter_json = r'data/twitter_01_20_17_to_3-2-18.json'
# Convert to pandas dataframe
tweet_data = pd.read_json(twitter_json)
# read the json data into a pandas dataframe
tweet_data = pd.read_json(twitter_json)
# set column 'created_at' to the index
tweet_data.set... | <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: Using Pandas I will read the twitter json file, convert it to a dataframe, set the index to 'created at' as datetime objects, then write it to a... |
148 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import seaborn as sns
data = pd.read_csv('https://s3-ap-southeast-1.amazonaws.com/intro-to-ml-minhdh/EcommercePurchases.csv')
data.head()
data.shape
data["Purchase Price"].mean()
data["Purchase Price"].max()
data["Purchase Price"].min()
data[dat... | <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: Check the head of the DataFrame.
Step2: How many rows and columns are there?
Step3: What is the average Purchase Price?
Step4: What were the ... |
149 | <ASSISTANT_TASK:>
Python Code:
import toytree
import itertools
import numpy as np
t0 = toytree.rtree.unittree(10, seed=0)
t1 = toytree.rtree.unittree(10, seed=1)
toytree.mtree([t0, t1]).draw(ts='p', height=200);
t0.draw(
ts='p',
node_colors="lightgrey",
edge_widths=3,
edge_colors=t0.get_edge_values_ma... | <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: get two random trees
Step2: Plan for counting quartets (Illustrated below)
Step3: Example to sample tips from each quartet edge
Step4: Exampl... |
150 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
pd?
pd.Categorical
cdr = pd.read_csv('data/CDR_data.csv')
cdr.head()
cdr.info()
len(cdr)
cdr.CallTimestamp = pd.to_datetime(cdr.CallTimestamp)
cdr.Duration = pd.to_timedelta(cdr.Duration)
cdr.info()
cdr.Duration.mean()
phone_owners = pd.read_excel("data/phoneow... | <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: Interaktive Hilfe
Step2: Die weitere Funktionalität der Pandas-Bibliothek können wir erkunden, indem wir die Methoden von Pandas ansehen. Dazu ... |
151 | <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
<END_TASK>
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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... |
152 | <ASSISTANT_TASK:>
Python Code:
import datashader as ds
import datashader.transfer_functions as tf
import dask.dataframe as dd
import numpy as np
%%time
#df = dd.from_castra('data/census.castra')
df = dd.read_hdf('data/census.hdf', key='census')
#df = df.cache(cache=dict)
import warnings
warnings.filterwarnings('ignore... | <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
Step2: The output of .tail() shows that there are more than 300 million datapoints (one per person), each with a location in Web Mercator ... |
153 | <ASSISTANT_TASK:>
Python Code:
#!pip install -I "phoebe>=2.3,<2.4"
import phoebe
from phoebe import u # units
import numpy as np
logger = phoebe.logger('error')
b = phoebe.default_binary()
b.set_value('ecc', 0.2)
b.set_value('per0', 25)
b.set_value('teff@primary', 7000)
b.set_value('teff@secondary', 6000)
b.set_value(... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Create fake "observations"
Step2: We'll set the initial model to be close to the correct values, but not exact. In practice, we would instead ... |
154 | <ASSISTANT_TASK:>
Python Code:
%%bash
pip freeze | grep tensor
!pip3 install tensorflow-hub==0.4.0
!pip3 install --upgrade tensorflow==1.13.1
import os
import tensorflow as tf
import numpy as np
import tensorflow_hub as hub
import shutil
PROJECT = 'cloud-training-demos' # REPLACE WITH YOUR PROJECT ID
BUCKET = 'cloud-t... | <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: If 'tensorflow-hub' isn't one of the outputs above, then you'll need to install it. Uncomment the cell below and execute the commands. After doi... |
155 | <ASSISTANT_TASK:>
Python Code:
# Import dependencies
from __future__ import division, print_function
%matplotlib inline
import scipy
import sympy
from sympy import Symbol, symbols, Matrix, sin, cos, latex
from sympy.interactive import printing
printing.init_printing()
sympy.init_printing(use_latex="mathjax", fontsize='... | <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: Simulation of kinematic motion model
Step2: Implementation of EKF for $\sigma$-model
Step3: Define state transition function $F$
Step4: Compu... |
156 | <ASSISTANT_TASK:>
Python Code:
#Importamos el modulo numpy con el alias np
import numpy as np
#Creo un array
a = np.array([1,0,0])
a
type(a)
#Ejemplo creo una lista de Python de 0 a 1000 y calculo el cuadrado de cada elemento
L = range(1000)
%%timeit
[i**2 for i in L]
#Ahora hago lo mismo con Numpy
a = np.arange(1000... | <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: Python posee por defecto un tipo de datos que se asemeja(listas), pero es numéricamente ineficiente
Step2: Caracteristicas y utilidades princip... |
157 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'inpe', 'sandbox-1', 'ocean')
# 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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<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
158 | <ASSISTANT_TASK:>
Python Code:
from scipy import stats
import numpy as np
# making kde
values = np.arange(10)
kde = stats.gaussian_kde(values)
np.median(kde.resample(100000))
def KDE_make_means(kde, size=10):
func = lambda x : np.random.randint(0, x.n, size=x.d)
kde.means = [kde.dataset[:, func(kde)] for i in x... | <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: Notes
|
159 | <ASSISTANT_TASK:>
Python Code:
#importando bibliotecas que iremos usar
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
import warnings
import os
from numpy import arange
from scipy.stats import skew
from sklearn.utils import shuffle
from scipy.s... | <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: Importa a planilha que contem os dados utilizados. Esta planilha foi importada de www.kaggle.com
Step2: Normaliza a coluna 'Value' (valor do jo... |
160 | <ASSISTANT_TASK:>
Python Code:
x = 5
y = 7
x2 = -3 # oops maybe the choice of variable names is not optimal
y2 = 17
x3 = x + x2
y3 = y + y2
print(x3, y3)
from math import sqrt
length_3 = sqrt(x3 * x3 + y3 * y3)
print length_3
length_1 = sqrt(x * x + y * y)
print length_1
def length(x, y):
return sqrt(x* 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: And here is another one
Step2: In our example we want to apply some vector arithmetics.
Step3: Now, what is the length of this new vector?
Ste... |
161 | <ASSISTANT_TASK:>
Python Code:
# meld is a great visual difference program
# http://meldmerge.org/
# the following command relies on the directory structure on my computer
# tdd-demo comes from https://github.com/james-prior/tdd-demo/
!cd ~/projects/tdd-demo;git difftool -t meld -y 389df2a^ 389df2a
False or False
0 or... | <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: Python's or
Step2: Python's concept of truthiness
Step3: Slowly scroll through the output of the following cell,
Step4: There was some confus... |
162 | <ASSISTANT_TASK:>
Python Code:
# Get the parameters for Rprop of climin:
climin.Rprop?
class RProp(Optimizer):
# We want the optimizer to know some things in the Optimizer implementation:
def __init__(self, step_shrink=0.5, step_grow=1.2, min_step=1e-06, max_step=1, changes_max=0.1, *args, **kwargs):
su... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: This is all we need, GPy/Paramz will handle the rest for you
Step2: This is the model plot before optimization
Step3: And then the optimized ... |
163 | <ASSISTANT_TASK:>
Python Code:
import hashlib
import os
import pickle
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils import resample
from tqdm import tqdm
from zipfil... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step3: The notMNIST dataset is too large for many computers to handle. It contains 500,000 images for just training. You'll be using a subset of this... |
164 | <ASSISTANT_TASK:>
Python Code:
x = y = 7
print(x,y)
x = y = 7
print(id(x))
print(id(y))
from lolviz import *
callviz(varnames=['x','y'])
name = 'parrt'
userid = name # userid now points at the same memory as name
print(id(name))
print(id(userid))
you = [1,3,5]
me = [1,3,5]
print(id(you))
print(id(me))
callviz(varn... | <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: But, did you know that they are both referring to the same 7 object? In other words, variables in Python are always references or pointers to da... |
165 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
plt.rc('xtick', labelsize=14)
plt.rc('ytick', labelsize=14)
# for auto-reloading external modules
# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipy... | <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: Helper functions
Step2: Load raw trip data
Step3: Processing Time and Date
Step4: Plotting weekly rentals
Step5: The rentals show that over ... |
166 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
! cargo run --release --example clv > clv.csv
df = np.arccos(pd.read_csv("clv.csv"))
for col in df.columns:
plt.figure()
plt.title(col)
df[col].hist(bins=100)
plt.xlim(0, np.pi)
... | <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: A sample script for calculate CLV of Lorenz 63 model is placed at examples/clv.rs
Step2: Tangency of CLVs
|
167 | <ASSISTANT_TASK:>
Python Code:
# Author: Alexandre Barachant <alexandre.barachant@gmail.com>
# Jean-Remi King <jeanremi.king@gmail.com>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne import Epochs
from mne.decoding import SPoC
from mne.datasets.fieldtrip_cmc import data_path
fro... | <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: Plot the contributions to the detected components (i.e., the forward model)
|
168 | <ASSISTANT_TASK:>
Python Code:
!head -n12 $LISA_HOME/logging.conf
!head -n30 $LISA_HOME/logging.conf | tail -n5
import logging
from conf import LisaLogging
LisaLogging.setup(level=logging.INFO)
from env import TestEnv
te = TestEnv({
'platform' : 'linux',
'board' : 'juno',
'host' ... | <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: Each module has a unique name which can be used to assign a priority level for messages generated by that module.
Step2: The default logging le... |
169 | <ASSISTANT_TASK:>
Python Code:
# import and check version
import tensorflow as tf
# tf can be really verbose
tf.logging.set_verbosity(tf.logging.ERROR)
print(tf.__version__)
# a small sanity check, does tf seem to work ok?
hello = tf.constant('Hello TF!')
sess = tf.Session()
print(sess.run(hello))
sess.close()
a = tf... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: First define a computational graph composed of operations and tensors
Step2: Then use a session to execute the graph
Step3: Graphs can be exec... |
170 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
from pymc3 import Model, Normal, Lognormal, Uniform, trace_to_dataframe, df_summary
data = pd.read_csv('/5studies.csv')
data.head()
plt.figure(figsize =(10,10))
for study in 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: Load in data and view
Step2: Manipulate data
Step3: Now build the model
Step4: Initiate the Bayesian sampling
Step5: Plot the traces and tak... |
171 | <ASSISTANT_TASK:>
Python Code:
# Copyright 2022 The Google Research Authors.
#
# 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 require... | <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: Human evaluation of visual metrics
Step2: First download the dataset containing all evaluations.
Step3: Then decorate it with whether the crop... |
172 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
from __future__ import division
import nltk
g1 =
S -> NP VP
NP -> Det N | Det N PP | 'I'
VP -> V NP | VP PP
PP -> P NP
Det -> 'an' | 'my'
N -> 'elephant' | 'pajamas'
V -> 'shot'
P -> 'in'
grammar1 = nltk.CFG.fromstring(g1)
analyzer = nltk.ChartPar... | <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: Gramáticas Independientes del Contexto (CFG)
Step3: Fíjate cómo hemos definido nuestra gramática
Step4: Con el objeto grammar1 ya creado, crea... |
173 | <ASSISTANT_TASK:>
Python Code:
from pred import Predictor
from pred import sequence_vector
from pred import chemical_vector
par = ["pass", "ADASYN", "SMOTEENN", "random_under_sample", "ncl", "near_miss"]
for i in par:
print("y", i)
y = Predictor()
y.load_data(file="Data/Training/clean_s_filtered.csv")
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Controlling for Random Negatve vs Sans Random in Imbalanced Techniques using S, T, and Y Phosphorylation.
Step2: Y Phosphorylation
Step3: T Ph... |
174 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from pathlib import Path
from scipy.stats import linregress
dir_ = r'C:\Data\Antonio\data\8-spot 5samples data\2013-05-15/'
filenames = [str(f) for f in Path(dir_).glob('*.hdf5'... | <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: Measurement 0
Step2: Measurement 1
Step3: Measurement 2
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175 | <ASSISTANT_TASK:>
Python Code:
science_image_path_g = 'data/seo_m66_g-band_180s_apagul_1.fits' #Type the path to your image
sci_g = fits.open(science_image_path_g)
sci_im_g = fits.open(science_image_path_g)[0].data
plt.imshow(sci_im_g,cmap='gray', vmax=1800, norm=matplotlib.colors.LogNorm())
plt.colorbar()
dark_image_... | <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: This image is not science-ready yet...
Step2: Why is this?
Step3: Let's create a better image!
Step4: Compare to the original!
Step5: Reduce... |
176 | <ASSISTANT_TASK:>
Python Code:
# Version for this notebook
!pip list | grep mecabwrap
from mecabwrap import tokenize, print_token
for token in tokenize('すもももももももものうち'):
print_token(token)
token
from mecabwrap import do_mecab
out = do_mecab('人生楽ありゃ苦もあるさ', '-Owakati')
print(out)
from mecabwrap import do_mecab_vec... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Usage
Step2: Token is defined as a namedtuple (v0.3.2+) with the following fields
Step3: Using MeCab Options
Step4: The exapmle below uses do... |
177 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: BERT Preprocessing with TF Text
Step2: Our data contains two text features and we can create a example tf.data.Dataset. Our goal is to create a... |
178 | <ASSISTANT_TASK:>
Python Code:
# baseline confirmation, implying that model has to perform at least as good as it
from sklearn.dummy import DummyClassifier
clf_Dummy = DummyClassifier(strategy='most_frequent')
clf_Dummy = clf_Dummy.fit(X_train, y_train)
print('baseline score =>', round(clf_Dummy.score(X_test, y_test),... | <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: ===================================================================================================================
Step2: (2) max features
Ste... |
179 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
'''
count_times = the time since the start of data-taking when the data was
taken (in seconds)
count_rates = the number of counts since the last time data was taken, at
the time in count_... | <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: Question
Step2: Question 1
Step3: Question 2
Step4: Question 3
Step5: Section 3
Step6: Part 2
Step8: Section 4
|
180 | <ASSISTANT_TASK:>
Python Code:
%reload_ext autoreload
%autoreload 2
import sys
sys.path.append('..')
from helper import nn
from helper import logistic_regression as lr
import numpy as np
X_raw, y_raw = nn.load_data('ex4data1.mat', transpose=False)
X = np.insert(X_raw, 0, np.ones(X_raw.shape[0]), axis=1)
X.shape
y_raw... | <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: prepare data
Step2:
Step3: load weight
Step4: feed forward
Step5: cost function
Step6: regularized cost function
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181 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
np.zeros(10)
np.ones(10)
np.ones(10) * 5
np.arange(10,51)
np.arange(10,51,2)
np.arange(9).reshape(3,3)
np.eye(3)
np.random.rand(1)
np.random.randn(25)
np.arange(1,101).reshape(10,10) / 100
np.linspace(0,1,20)
mat = np.arange(1,26).reshape(5,5)
mat
# WRITE 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: Create an array of 10 zeros
Step2: Create an array of 10 ones
Step3: Create an array of 10 fives
Step4: Create an array of the integers from ... |
182 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from scipy import integrate
from matplotlib.pylab import *
import numpy as np
from scipy import integrate
import matplotlib.pyplot as plt
def vdp1(t, y):
return np.array([y[1], (1 - y[0]**2)*y[1] - y[0]])
t0, t1 = 0, 20 # start and end
t = np.linspac... | <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: Ejemplo 2 funciona
Step2: Fonction
Step6: Trabajo futuro
|
183 | <ASSISTANT_TASK:>
Python Code:
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import xarray as xr
# Any import of metpy will activate the accessors
import metpy.calc as mpcalc
from metpy.testing import get_test_data
# Open the netCDF file as a xarray Dataset
data = xr.ope... | <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: Getting Data
Step2: Preparing Data
Step3: Units
Step4: Coordinates
Step5: Projections
Step6: The cartopy Globe can similarly be accessed vi... |
184 | <ASSISTANT_TASK:>
Python Code:
# a simple function that looks like a mathematical function
# define a function called add_two_numbers that take 2 arguments: num1 and num2
def add_two_numbers(num1, num2):
# Under the def must be indented
return num1 + num2 # use the return statment to tell the function what to r... | <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: Use return ... to give a value back to the caller. A function that doesn’t explicitly return a value automatically returns None.
Step2: Questio... |
185 | <ASSISTANT_TASK:>
Python Code:
x = np.array([ 1.00201077, 1.58251956, 0.94515919, 6.48778002, 1.47764604,
5.18847071, 4.21988095, 2.85971522, 3.40044437, 3.74907745,
1.18065796, 3.74748775, 3.27328568, 3.19374927, 8.0726155 ,
0.90326139, 2.34460034, 2.14199217, 3.27446744, 3.5887... | <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: Fitting data to probability distributions
Step2: The first step is recognizing what sort of distribution to fit our data to. A couple of observ... |
186 | <ASSISTANT_TASK:>
Python Code:
!conda install -qy poliastro --channel poliastro # Instala las dependencias con conda
!pip uninstall poliastro -y
#!pip install -e /home/juanlu/Development/Python/poliastro.org/poliastro
!pip install https://github.com/poliastro/poliastro/archive/planet9-fixes.zip # Instala la versión 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: La órbita del Planeta Nueve
Step2: Vamos a crear un objeto State para representar Planeta Nueve, añadiendo a los parámetros estimados del artíc... |
187 | <ASSISTANT_TASK:>
Python Code:
!pip install -I "phoebe>=2.2,<2.3"
%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_dataset('lc', times=np.linspace(0,1,201), dataset='mylc')
b.run_compute(irrad_m... | <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: 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... |
188 | <ASSISTANT_TASK:>
Python Code:
%reload_ext autoreload
%autoreload 2
%matplotlib inline
from fastai.imports import *
from fastai.transforms import *
from fastai.conv_learner import *
from fastai.model import *
from fastai.dataset import *
from fastai.sgdr import *
from fastai.plots import *
PATH = "data/dogscats/"
sz=22... | <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: Testing
Step2: Experimenting with saving as Pandas.DataFrame.to_feather(.)
Step3: Another way to create submission file
Step4: Creating FileL... |
189 | <ASSISTANT_TASK:>
Python Code:
import igraph as ig
import json
data = []
with open('miserables.json') as f:
for line in f:
data.append(json.loads(line))
data=data[0]
data
print data.keys()
N=len(data['nodes'])
N
L=len(data['links'])
Edges=[(data['links'][k]['source'], data['links'][k]['target'])... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Read graph data from a json file
Step2: Get the number of nodes
Step3: Define the list of edges
Step4: Define the Graph object from Edges
Ste... |
190 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from plotly.offline import init_notebook_mode,iplot
import plotly.graph_objs as go
%matplotlib inline
init_notebook_mode(connected=True)
import quandl
sp500=quandl.get("YAHOO/INDEX_GSPC",start_date="2000-01-03",end_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: Download data
Step2: Clean data
Step3: Plot the closing quotes over time to get a fisrt impression about the historical market trend by using ... |
191 | <ASSISTANT_TASK:>
Python Code:
import scipy.io as sio
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import random
from keras.utils import np_utils
from keras.models import Sequential, Model
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolut... | <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: a)
Step2: Se trabajará con SVHN o Street View House Numbers, se puede observar que las imágenes con las que se trabajará, efectivamente pertene... |
192 | <ASSISTANT_TASK:>
Python Code:
reviews = gl.SFrame.read_csv('../data/yelp/yelp_training_set_review.json', header=False)
reviews
reviews[0]
reviews=reviews.unpack('X1','')
reviews
reviews = reviews.unpack('votes', '')
reviews
reviews.show()
reviews['date'] = reviews['date'].str_to_datetime(str_format='%Y-%m-%d')
re... | <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: Unpack to extract structure
Step2: Votes are still crammed in a dictionary. Let's unpack it.
Step3: Quick data visualization
Step4: Represent... |
193 | <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: Fine-tuning a BERT model
Step2: Imports
Step3: Resources
Step4: You can get a pre-trained BERT encoder from TensorFlow Hub
Step5: The data
S... |
194 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import json
import codecs
import warnings
import matplotlib.pyplot as plt
%matplotlib inline
race_metadata = pd.read_csv('~/election-twitter/elections-twitter/data/race-metadata.csv')
race_metadata_2016 = pd.read_csv('~/election-twitter/elections-twi... | <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 below is the same code as last post with a couple modifications
Step2: Now, I will do the same model building procedure as last time. There... |
195 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
from matplotlib.ticker import MultipleLocator
%matplotlib notebook
def pixel_plot(pix, counts, fig=None, ax=None):
'''Make a pixelated 1D plot'''
if fig is None and ax is None:
fig, ax = plt.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:
Step2: Background Subtraction and Source Detection
Step3: Problem 1) Simple 1-D Background Estimation
Step4: Problem 1.2) Estimate a the background a... |
196 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.ndimage as ndimage
img = ndimage.imread("noisy.png", flatten = True)
### BEGIN SOLUTION
### END SOLUTION
import scipy.ndimage as ndimage
img = ndimage.imread("noisy.png", flatten = True)
t1 = 30
s1 = 5
a1 ... | <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 B
|
197 | <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: 基本分类：对服装图像进行分类
Step2: 导入 Fashion MNIST 数据集
Step3: 加载数据集会返回四个 NumPy 数组：
Step4: 浏览数据
Step5: 同样，训练集中有 60,000 个标签：
Step6: 每个标签都是一个 0 到 9 之间的整数：... |
198 | <ASSISTANT_TASK:>
Python Code:
import os
import sys
# Modify the path
sys.path.append("..")
import pandas as pd
import yellowbrick as yb
import matplotlib.pyplot as plt
data = pd.read_csv("data/No-show-Issue-Comma-300k.csv")
data.head()
data.columns = ['Age','Gender','Appointment Registration','Appointment Date',
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Load Medical Appointment Data
Step2: Feature Analysis
Step3: Rank2D
Step4: Diagnostic Interpretation from Rank2D(Covariance)
Step5: Diagnost... |
199 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.datasets import load_linnerud
linnerud = load_linnerud()
chinups = linnerud.data[:,0]
plt.hist(chinups, histtype = "step", lw = 3)
plt.hist(chinups, bins = 5, histtype="step", lw = 3)
plt.hist(chinups, 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: Problem 1) Density Estimation
Step2: Problem 1a
Step3: Already with this simple plot we see a problem - the choice of bin centers and number ... |
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