Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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9,000 | <ASSISTANT_TASK:>
Python Code:
import re
phoneRegex = re.compile(r'/d/d/d-/d/d/d-/d/d/d/d')
#phoneRegex.search() # finds first match
#phoneRegex.findall() # finds all matches
import re
phoneRegex = re.compile(r'(/d/d/d)-(/d/d/d-/d/d/d/d)') # Two groups, so returns tuples
#phoneRegex.findall() # finds all matches in pa... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: find.all() returns a list of strings.
Step2: To get the total string, just wrap the total regex in its own group, so you get [(totalstring, gr... |
9,001 | <ASSISTANT_TASK:>
Python Code:
%%html
<style>
.example-container { background: #999999; padding: 2px; min-height: 100px; }
.example-container.sm { min-height: 50px; }
.example-box { background: #9999FF; width: 50px; height: 50px; text-align: center; vertical-align: middle; color: white; font-weight: bold; margin: 2px;}... | <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: Widget Styling
Step2: Parent/child relationships
Step3: After the parent is displayed
Step4: Fancy boxes
Step5: TabWidget
Step6: Alignment
... |
9,002 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import Image
Image(filename="ImagAnillosNewton.jpg")
from IPython.display import Image
Image(filename="PaperAnillosNewton.JPG")
from IPython.display import Image
Image(filename="esquemaAnillosNewton1.jpg")
from IPython.display import Image
Image(filename="esquemaAn... | <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: El patrón de interferencias que aparece al reflejarse la luz entre dos superficies transparentes, una curva y otra plana, es conocido como Anill... |
9,003 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
df = pd.read_csv('example')
df
df.to_csv('example',index=False)
pd.read_excel('Excel_Sample.xlsx',sheetname='Sheet1')
df.to_excel('Excel_Sample.xlsx',sheet_name='Sheet1')
df = pd.read_html('http://www.fdic.gov/bank/individual/failed/banklist.htm... | <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: CSV
Step2: CSV Output
Step3: Excel
Step4: Excel Output
Step5: HTML
Step6: SQL (Optional)
|
9,004 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
X = np.array([[1, 2, 3], [4, 5, 6]])
X
X + 2*X
np.matmul(X.transpose(), X) #X^t * X
X[1,1]
X[1, :] #1. Fila entera
X[:, 1] #2. Columna entera
X[0:2, 0:2] #3. Slice de n:m, n,n+1,...,m-1
X.shape #Dimensión de arrays
vec = np.array([1, 2, 3])
print(vec)
class Array:... | <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: Operaciones con arrays
Step2: Multiplicación de matrices con numpy.matmul()
Step3: Obteniendo datos específicos
Step4: Selección y multi sele... |
9,005 | <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
pickle_file = 'notMNIST.pickle'
with open(pickle_file, 'rb... | <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 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... |
9,006 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
# if using a jupyter notebook: include %matplotlib inline. If constructing a .py-file: comment out
%matplotlib inline
# if high-resolution images are desired: include %config InlineBackend.figure_format = 'svg'
%config InlineBackend.figure_format = 'svg'
im... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Now we'll build the circuit diagram by creating a SchemDraw Drawing object and adding elements to it.
Step2: Find R<sub>t</sub>
Step3: Find R<... |
9,007 | <ASSISTANT_TASK:>
Python Code:
!pip install nnabla-ext-cuda100
!git clone https://github.com/sony/nnabla-examples.git
%cd nnabla-examples
import nnabla as nn
import nnabla.functions as F
import nnabla.parametric_functions as PF
import nnabla.solver as S
from nnabla.logger import logger
import nnabla.utils.save as save... | <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 start by importing dependencies.
Step4: Let's also define data iterator for MNIST. You can disregard the details for now.
Step... |
9,008 | <ASSISTANT_TASK:>
Python Code:
from effect_demo_setup import *
from concise.models import single_layer_pos_effect as concise_model
import numpy as np
# Generate training data for the model, use a 1000bp sequence
param, X_feat, X_seq, y, id_vec = load_example_data(trim_seq_len = 1000)
# Generate the model
dc = concise_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 with any prediction that you want to make with a model it is necessary that the input sequences have to fit the input dimensions of your mode... |
9,009 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'hadgem3-gc31-lm', 'ocean')
# 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... |
9,010 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
# redefining the example DataFrame
data = {'country': ['Belgium', 'France', 'Germany', 'Netherlands', 'United Kingdom'],
'population': [11.3, 64.3, 81.3, 16.9, 64.9],
'area': [30510, 671308, 357050, 41526, 244820],
'capital': ['Brussels', 'Paris... | <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: Subsetting data
Step2: Remember that the same syntax can also be used to add a new columns
Step3: Subset observations (rows)
Step4: Boolean i... |
9,011 | <ASSISTANT_TASK:>
Python Code:
import cv2
import numpy as np
import sys
import pandas as pd
# typeData 为"train"或者"test"
# labelsInfo 包含每一个图片的ID
# 图片存储在trainResized和testResized文件夹内
def read_data(typeData, labelsInfo, imageSize):
labelsIndex = labelsInfo["ID"]
x = np.zeros((np.size(labelsIndex), imageSize))
... | <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: 我们希望所有的图片最后存储在一个numpy的矩阵当中，每一行为图片的像素值。为了得到统一的表达呢，我们将RGB三个通道的值做平均得到的灰度图像作为每个图片的表示
Step2: 预处理训练集和测试集
Step3: 预览数据：
Step4: 模型训练
Step5: 预测
Step6:... |
9,012 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'inm', 'inm-cm5-0', 'seaice')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "emai... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 2... |
9,013 | <ASSISTANT_TASK:>
Python Code:
# Import relevant modules
%matplotlib inline
%load_ext autoreload
%autoreload 2
import numpy as np
import corner
import matplotlib.pyplot as plt
from matplotlib import rcParams
from NPTFit import nptfit # module for performing scan
from NPTFit import create_mask as cm # module for creatin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Step 1
Step2: Step 2
Step3: Step 3
Step4: Step 4
Step5: Step 5
Step6: We also show a plot of the source count function, although a careful ... |
9,014 | <ASSISTANT_TASK:>
Python Code::
def skipIndices(N , T , arr):
sum = 0
count = { }
for i in range(N):
d = sum + arr[i]- T
k = 0
if(d > 0):
for u in list(count . keys())[: : - 1]:
j = u
x = j * count[j]
if(d <= x):
k +=(d + j - 1)// j
break
k += count[j]
d -= x
sum += arr[i]
count[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:
|
9,015 | <ASSISTANT_TASK:>
Python Code:
list_with_for_loop = [x for x in range(10)]
print list_with_for_loop
list_with_for_loop_conditional = [x for x in range(10) if x%2 == 1]
print list_with_for_loop_conditional
list_with_nested_loops = [ [x, y] for x in range(3) for y in range(3) ]
print list_with_nested_loops
list_with_n... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Even with conditions in the for loop
Step2: Nested loops in a list
Step3: Another example of nested loops
Step4: The article gives an example... |
9,016 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import os
import gdal, osr
import matplotlib.pyplot as plt
import sys
import matplotlib.pyplot as plt
from scipy import ndimage as ndi
%matplotlib inline
#Import biomass specific libraries
from skimage.morphology import watershed
from skimage.feature import peak_local... | <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: Next we will add libraries from skilearn which will help with the watershed delination, determination of predictor variables and random forest a... |
9,017 | <ASSISTANT_TASK:>
Python Code:
# variable assignment
# https://www.digitalocean.com/community/tutorials/how-to-use-variables-in-python-3
# strings -- enclose in single or double quotes, just make sure they match
my_name = 'Cody'
# numbers
int_num = 6
float_num = 6.4
# the print function
print(8)
print('Hello!')
print(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: Basic math
Step2: Lists
Step3: Dictionaries
Step5: Commenting your code
Step7: Comparison operators
Step8: String functions
Step9: upper()... |
9,018 | <ASSISTANT_TASK:>
Python Code:
df = pd.read_csv('../data/wine.data')
X_train = df[df.columns[1:]]
y_train = df[df.columns[0]]
kf = KFold(n_splits=5, random_state=42, shuffle=True)
def test_accuracy(kf, X, y):
means = list()
means_range = range(1, 51)
for r in means_range:
knn = KNeighborsClassifi... | <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. Извлеките из данных признаки и классы. Класс записан в первом столбце (три варианта), признаки — в столбцах со второго по последний. Более по... |
9,019 | <ASSISTANT_TASK:>
Python Code:
# Load Module
import numpy as np
from sklearn import datasets
from sklearn import metrics
from sklearn import model_selection
import tensorflow as tf
# Load dataset.
iris = datasets.load_iris() # 총 150개의 붓꽃 사진과 class load
x_train, x_test, y_train, y_test = model_selection.train_test_split... | <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. TF-slim(tf.contrib.slim)
Step2: with TF-Slim
Step3: Data Flow Graph
Step4: How to get the value of a?
Step5: More graphs
Step6: Why grap... |
9,020 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
# Required imports
from wikitools import wiki
from wikitools import category
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import gensim
import numpy as np
import lda
import lda.datasets
fro... | <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. Corpus acquisition.
Step2: You can try with any other categories. Take into account that the behavior of topic modelling algorithms may depe... |
9,021 | <ASSISTANT_TASK:>
Python Code:
# code for loading the format for the notebook
import os
# path : store the current path to convert back to it later
path = os.getcwd()
os.chdir(os.path.join('..', 'notebook_format'))
from formats import load_style
load_style(plot_style=False)
os.chdir(path)
# 1. magic to print version
# ... | <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: Working with Python Classes
Step2: When the Python compiler sees a private attribute, it actually transforms the actual name to _[Class name]__... |
9,022 | <ASSISTANT_TASK:>
Python Code:
%%capture
!pip install git+https://github.com/jamesvuc/jax-bayes
!pip install SGMCMCJax
!pip install distrax
import jax.numpy as jnp
from jax.experimental import optimizers
import jax
import jax_bayes
import sys, os, math, time
import numpy as np
from functools import partial
from matplot... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Data
Step2: The Bayesian NN is taken from SGMCMCJAX. However, there are couple of changes made. These can be listed as follows
Step3: Model
St... |
9,023 | <ASSISTANT_TASK:>
Python Code:
import packages.initialization
import pioneer3dx as p3dx
p3dx.init()
def forward():
# copy and paste your code here
...
def turn():
# copy and paste your code here
...
print('Pose of the robot at the start')
p3dx.pose()
for _ in range(4):
forward()
turn()
print('P... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 3. Program
Step2: The trajectory can also be displayed
|
9,024 | <ASSISTANT_TASK:>
Python Code:
import IPython.display as IPdisplay
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pynamical
from pynamical import simulate, bifurcation_plot, save_fig
%matplotlib inline
title_font = pynamical.get_title_font()
label_font = pynamic... | <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, let's see the population values the logistic map produces for a range of growth rate parameters
Step2: Now let's visualize the system at... |
9,025 | <ASSISTANT_TASK:>
Python Code:
%matplotlib notebook
import os
import numpy as np
import tempfile
import matplotlib.pyplot as pyplot
import logging
logging.basicConfig(level=logging.INFO)
import minimask.mask as mask
import minimask.healpix_projection as hp
import minimask.io.mosaic as mosaic
filename = "masks/mosaic.t... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Specify the location of the mask file to write
Step2: Construct a mask using a tile pattern with centers specified by the healpix grid.
Step3: ... |
9,026 | <ASSISTANT_TASK:>
Python Code:
%pylab notebook
imax = 1 # Normalize imax to 1
freq = 50 # [Hz]
w = 2*pi*freq # [rad/s] angluar velocity
t = linspace(0, 1./50, 100) # 100 values for one period
wt = w*t # we are going to use this quite often
# amplit... | <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: Set up the basic conditions
Step2: First, generate the three component magnetic fields
Step3: Calculate the combined current vector
Step4: Ca... |
9,027 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
df = pd.DataFrame([[1,2,3,1],[0,0,0,0],[1,0,0,1],[0,1,2,0],[1,1,0,1]],columns=['A','B','C','D'])
def g(df):
return df.loc[(df.max(axis=1) != 2), (df.max(axis=0) != 2)]
result = g(df.copy())
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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:
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9,028 | <ASSISTANT_TASK:>
Python Code:
import jax.numpy as jnp
import jax
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 18})
import ott
def create_points(rng, n, m, d):
rngs = jax.random.split(rng, 4)
x = jax.random.normal(rngs[0], (n,d)) + 1
y = jax.random.uniform(rngs[1], (m,d))
a = jax.random.uni... | <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 OT problem comparing two point clouds
Step2: Solve it with Sinkhorn and plot plan/map
Step3: Experimentations with the Low-Rank appr... |
9,029 | <ASSISTANT_TASK:>
Python Code:
# Here's a string representing a three-line SAM file. I'm temporarily
# ignoring the fact that SAM files usually have several header lines at
# the beginning.
samStr = '''\
r1 0 gi|9626243|ref|NC_001416.1| 18401 42 122M * 0 0 TGAATGCGAACTCCGGGACGCTCAGTAATGTGACGATAGCTGAAAACTGTACGATAAACNGT... | <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: SAM fields
Step2: Next we construct a function to parse the MD
Step3: Now we can write a fucntion that takes a read sequennce, a parsed CIGAR ... |
9,030 | <ASSISTANT_TASK:>
Python Code:
import argparse
import logging
import joblib
import sys
import pandas as pd
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.impute import SimpleImputer
from xgboost import XGBClassifier
logging.basicConfig(format='%(message)s')
l... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Define the model logic
Step6: Define functions to train, evaluate, and save the trained model.
Step8: Define a class for your model, with meth... |
9,031 | <ASSISTANT_TASK:>
Python Code:
import torch
import numpy as np
from torchvision import datasets
import torchvision.transforms as transforms
# convert data to torch.FloatTensor
transform = transforms.ToTensor()
# load the training and test datasets
train_data = datasets.MNIST(root='data', train=True,
... | <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: Visualize the Data
Step2: Linear Autoencoder
Step3: Training
Step4: Checking out the results
|
9,032 | <ASSISTANT_TASK:>
Python Code:
#Check that you are using the correct version of Python (should be 3.4+, otherwise gdal won't work)
import sys
sys.version
import numpy as np
import h5py
import gdal, osr
import matplotlib.pyplot as plt
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
f = h5py.File('... | <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 let's import the required packages and set our display preferences so that plots are inline and plot warnings are off
Step2: Read hdf5 fi... |
9,033 | <ASSISTANT_TASK:>
Python Code:
# general imports
import pandas as pd
import numpy as np
from datetime import datetime
from collections import defaultdict
import pickle
# imports for webscraping and text manipulation
import requests
import re
import io
import urllib
# imports to convert pdf to text
from pdfminer.pdfinte... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Part 1
Step2: Clean-up data types and identify hashtags and @mentions
Step3: Save clean twitter data as pickle
Step4: Scrape and format Data ... |
9,034 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
objective = np.poly1d([1.3, 4.0, 0.6])
print objective
import scipy.optimize as opt
x_ = opt.fmin(objective, [3])
print "solved: x={}".format(x_)
%matplotlib inline
x = np.linspace(-4,1,101.)
import matplotlib.pylab as mpl
mpl.plot(x, objective(x))
mpl.plot(x_, objecti... | <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 "optimizer"
Step2: Additional components
Step3: The gradient and/or hessian
Step4: The penalty functions
Step5: Optimizer classification... |
9,035 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd # data handeling
import numpy as np # numeriacal computing
import matplotlib.pyplot as plt # plotting core
import seaborn as sns # higher level plotting tools
%matplotlib inline
sns.set()
def h(X,a) : # model h(X) = Xa
h = np.dot(X,a)
return h
def a_opt(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: Here's the example data and a plot,
Step2: To start lets just try fitting a straight line $h(x) = a_0 + a_1x$. We'll construct the augmented ma... |
9,036 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mri', 'mri-esm2-0', 'ocnbgchem')
# 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... |
9,037 | <ASSISTANT_TASK:>
Python Code:
#from imp import *
#s=load_source('sygma','/home/nugrid/nugrid/SYGMA/SYGMA_online/SYGMA_dev/sygma.py')
#%pylab nbagg
import sys
import sygma as s
print (s.__file__)
s.__file__
#import matplotlib
#matplotlib.use('nbagg')
import matplotlib.pyplot as plt
#matplotlib.use('nbagg')
import numpy... | <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: IMF notes
Step2: The total number of stars $N_{tot}$ is then
Step3: With a yield ejected of $0.1 Msun$, the total amount ejected is
Step4: co... |
9,038 | <ASSISTANT_TASK:>
Python Code:
import sys,os,glob
from collections import OrderedDict
import numpy as np
from utils.misc import readPickle, createIfAbsent
sys.path.append('../')
from optvaedatasets.load import loadDataset as loadDataset_OVAE
from sklearn.feature_extraction.text import TfidfTransformer
default_params =... | <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 Parameters
Step2: For the moment, we will leave everything as is. Some worthwhile parameters to note
Step3: Load dataset
Step4: Setup
S... |
9,039 | <ASSISTANT_TASK:>
Python Code:
x = np.arange(-5.0, 5.0, 0.1)
y = np.array(x > 0, dtype=np.int)
plt.plot(x, y)
plt.show()
x = np.arange(-5.0, 5.0, 0.1)
y = 1 / (1 + np.exp(-x))
plt.plot(x, y)
plt.show()
x = np.arange(-5.0, 5.0, 0.1)
y = np.maximum(0, x)
plt.plot(x, y)
plt.show()
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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: シグモイド関数
Step2: ReLU関数
|
9,040 | <ASSISTANT_TASK:>
Python Code:
query_url = 'https://data.sfgov.org/resource/wbb6-uh78.json?$order=close_dttm%20DESC&$offset={}&$limit={}'
# query_url = "https://data.sfgov.org/resource/wbb6-uh78.json?$where=alarm_dttm>='2013-02-12 04:52:17'&$order=close_dttm%20DESC"
# query_url = "https://data.sfgov.org/resource/wbb6-u... | <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: According to wikipeda, the mission district falls into two zipcodes, 94103, 94110
Step2: Initial Conclusions
Step3: Disclaimers from the Fire ... |
9,041 | <ASSISTANT_TASK:>
Python Code:
import os
os.mkdir("/tmp/park-python")
try:
os.rmdir("/tmp/park-python")
except IOError as err:
print(err)
path = "/tmp/park-python/lectures/04"
if not os.path.exists(path):
os.makedirs(path)
os.rmdir("/tmp/park-python")
import shutil
shutil.rmtree("/tmp/park-python")
import p... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Работа с файлами
Step2: "r" – открытие на чтение (является значением по умолчанию).
Step3: stdin, stdout, stderr
Step4: Так как дескрипторы s... |
9,042 | <ASSISTANT_TASK:>
Python Code:
# Syntax error
x = 1; y = 2
b = x == y # Boolean variable that is true when x & y have the same value
b = 1 == 2 # Syntax error
b
# Exception - invalid operation
a = 0
5/a # Division by zero
# Exception - invalid operation
input = '40'
float(input)/11 # Incompatiable types for the oper... | <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: What's the bug here and how do we resolve?
Step3: We should have documented the inputs to the function!
Step4: Now it works f... |
9,043 | <ASSISTANT_TASK:>
Python Code:
# 加载必要的程序包
# PyTorch的程序包
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
# 数值运算和绘图的程序包
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
# 加载机器学习的软件包，主要为了词向量的二维可视化
from sklearn.decomposit... | <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: 第一步：加载词向量
Step2: 第二步：可视化同一组意思词在两种不同语言的词向量中的相互位置关系
Step3: 结论：可以看出，中文的一、二、等数字彼此之间的关系与英文的数字彼此之间的关系很类似
|
9,044 | <ASSISTANT_TASK:>
Python Code:
%load_ext sql
%sql mysql://steinam:steinam@localhost/celko
%%sql
select * from Register;
%%sql
SELECT R1.course_nbr, R1.student_name,
MIN(R1.teacher_name) as Teacher_1, NULL
FROM Register AS R1
GROUP BY R1.course_nbr, R1.student_name
HAVING COUNT(*) = 1
UNION
SELECT R1.course_nbr, R1.stu... | <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: Lösung 1
Step2: Lösung 2
Step3: Lösung 3
|
9,045 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import cv2
import sys
import os
sys.path.insert(0, os.path.abspath('..'))
import salientregions as sr
%pylab inline
#Load the image
path_to_image = 'images/graffiti.jpg'
img = cv2.imread(path_to_image)
sr.show_image(img)
det = sr.SalientDetector(SE_size_factor=0.20,
... | <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 load the image and show it.
Step2: Now we create a SalientDetector object, with some parameters.
Step3: We ask the SalientDetector t... |
9,046 | <ASSISTANT_TASK:>
Python Code:
print hash_obj([0, 1, 2])
bits = 64*2
n_elements = 200
np.log10(2*2**bits/(n_elements*(n_elements-1)))
l = list(['zero', 'one', 'two'])
l.__getitem__(0)
[x for x in l.__iter__()]
l.__setitem__(1, 1)
print l
l.__getslice__(1,3)
l.__setslice__(1, 3, ('b', 'c'))
print l
from functools 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: Testing out a container class but that
|
9,047 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from math import log
from sklearn import linear_model
#comment below if not using ipython notebook
%matplotlib inline
#read csv
anscombe_i = pd.read_csv('../datasets/anscombe_i.csv')
plt.scatter(anscombe_i.x, anscombe... | <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 lets read the first set of data, and make a simple scatter plot.
Step2: Luckly for us, we do not need to implement linear regression, since... |
9,048 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import ipywidgets as widgets
import bqplot.pyplot as plt
y = np.random.randn(100).cumsum() # simple random walk
# create a button
update_btn = widgets.Button(description='Update', button_style='success')
# create a figure widget
fig1 = plt.figure(animation_duration=750... | <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: Update the plot on a button click
Step2: Let's look at an example where we link a plot to a dropdown menu
Step3: Let's now create a scatter pl... |
9,049 | <ASSISTANT_TASK:>
Python Code:
from nipype import DataGrabber, Node
# Create DataGrabber node
dg = Node(DataGrabber(infields=['subject_id', 'task_id'],
outfields=['anat', 'func']),
name='datagrabber')
# Location of the dataset folder
dg.inputs.base_directory = '/data/ds102'
# Necessary 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: Second, we know that the two files we desire are the the following location
Step2: Now, comes the most important part of DataGrabber. We need t... |
9,050 | <ASSISTANT_TASK:>
Python Code:
def example1(x_1, x_2):
z = x_1**0.5*x_2*0.5
return z
fig = pl.figure()
ax = Axes3D(fig)
X = np.arange(0, 1, 0.1)
Y = np.arange(0, 1, 0.1)
X, Y = np.meshgrid(X, Y)
Z = example1(X, Y)
ax.plot_surface(X, Y, Z, rstride=1, cstride=1)
pl.show()
nn = NN()
x_1 = Symbol('x_1')
x_2 = Sym... | <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: <P>課題の例で使われた関数は以下の通りである。</P>
Step2: 以下に使い方を説明する。
Step3: 入力層、中間層、出力層を作る関数を実行する。引数には層の数を用いる。
Step4: <p>nn.set_hidden_layer()は同時にシグモイド関数で変換する前の中... |
9,051 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from random import random, randint, choice
from itertools import cycle, ifilter, imap, islice, izip, starmap, tee
from collections import defaultdict
from operator import add, mul
from pymonad.Maybe import *
from pymona... | <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 ubiquitous zip
Step2: In fact, it is mentioned in the documentation in the section on built-in functions. I guess, it is one of those bits ... |
9,052 | <ASSISTANT_TASK:>
Python Code:
# Install the SDK
!pip3 install 'kfp>=0.1.31.2' --quiet
import kfp.deprecated as kfp
import kfp.deprecated.components as components
#Define a Python function
def add(a: float, b: float) -> float:
'''Calculates sum of two arguments'''
return a + b
add_op = components.create_compone... | <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: Simple function that just add two numbers
Step2: Convert the function to a pipeline operation
Step3: A bit more advanced function which demons... |
9,053 | <ASSISTANT_TASK:>
Python Code:
# Authors: Jean-Remi King <jeanremi.king@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
from sklearn.pipeline import make_pipeline
from sklearn.preproce... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: We will train the classifier on all left visual vs auditory trials
Step2: Score on the epochs where the stimulus was presented to the right.
St... |
9,054 | <ASSISTANT_TASK:>
Python Code:
!hybridizer-cuda ./01-vector-add/01-vector-add.cs -o ./01-vector-add/vectoradd.exe -run
!hybridizer-cuda ./01-vector-add/01-vector-add.cs -o ./01-vector-add/parallel-vectoradd.exe -run
!hybridizer-cuda ./02-gpu-vector-add/02-gpu-vector-add.cs -o ./02-gpu-vector-add/gpu-vectoradd.exe -ru... | <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: Introduce Parallelism
Step2: Run Code on the GPU
|
9,055 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
from modshogun import *
#Needed lists for the final plot
classifiers_linear = []*10
classifiers_non_linear = []*10
classifiers_names = []*10
fading... | <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 id = "section1">Data Generation and Visualization</a>
Step5: Data visualization methods.
Step6: <a id="section2" href="http
Step7: SVM - K... |
9,056 | <ASSISTANT_TASK:>
Python Code:
import pickle
dataset = pickle.load(open('data/cafe.pkl','r')) # or 'pofa.pkl'
# This is the neural network class, for your information.
from sklearn.decomposition import PCA
from sklearn.learning_curve import learning_curve
from sklearn.cross_validation import train_test_split
from nump... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step6: Backpropagation Demo
Step7: Then train the net using the controls here
Step8: After the network is trained, use it to classify test images.
St... |
9,057 | <ASSISTANT_TASK:>
Python Code:
# Load a useful Python libraries for handling data
import pandas as pd
from IPython.display import Markdown, display
# Read the data
data_filename = r'gapminder.csv'
data = pd.read_csv(data_filename, low_memory=False)
data = data.set_index('country')
display(Markdown("General information ... | <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 will now have a look at the frequencies of the variables.
Step2: This is useless as the variable does not take discrete values. So before re... |
9,058 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
%pylab inline
pylab.style.use('ggplot')
import tensorflow as tf
X_val = numpy.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y_val = np.atleast_2d(np.array([0, 0, 0, 1])).T
X_val
y_val
tf.reset_default_graph()
n_iter = 500
threshold = 0.5
with tf.variable... | <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 idea of variable scoping in TensorFlow is to be able to organize the names and initializations of variables that play the same role in a mul... |
9,059 | <ASSISTANT_TASK:>
Python Code:
from fretbursts import *
sns = init_notebook(apionly=True)
print('seaborn version: ', sns.__version__)
# Tweak here matplotlib style
import matplotlib as mpl
mpl.rcParams['font.sans-serif'].insert(0, 'Arial')
mpl.rcParams['font.size'] = 12
%config InlineBackend.figure_format = 'retina'
u... | <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 and process data
Step2: ALEX joint plot
Step3: The inner plot in an hexbin plot, basically a 2D histogram with hexagonal bins.
Step4: Or ... |
9,060 | <ASSISTANT_TASK:>
Python Code:
%load_ext watermark
%watermark -a 'Sebastian Raschka' -v -p tensorflow
import tensorflow as tf
##########################
### WRAPPER FUNCTIONS
##########################
def fc_layer(input_tensor, n_output_units, name,
activation_fn=None, seed=None,
weight_para... | <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 Zoo -- Saving and Loading Trained Models
Step2: Train and Save Multilayer Perceptron
Step3: Reload Model from Meta and Checkpoint Files
... |
9,061 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division
import graphlab
products = graphlab.SFrame('amazon_baby_subset.gl/')
import json
with open('important_words.json', 'r') as f:
important_words = json.load(f)
important_words = [str(s) for s in important_words]
# Remote punctuation
def remove_punctuati... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load and process review dataset
Step2: Just like we did previously, we will work with a hand-curated list of important words extracted from the... |
9,062 | <ASSISTANT_TASK:>
Python Code:
# setup
import numpy as np
import sympy as sp
import pandas as pd
import scipy
from pprint import pprint
sp.init_printing(use_latex='mathjax')
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (12, 8) # (width, height)
plt.rcParams['font.size'] = 14
plt.rcParams['legend.f... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Reading raw test data example 1
Step2: Reading test data - example 2
Step3: another example of plotting data
Step5: Finding the "first" peak ... |
9,063 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
from ipywidgets import interact
from ipywidgets import widgets, FloatSlider, Checkbox, RadioButtons, fixed
from exact_solvers import shallow_water
from exact_solvers import shallow_demos
from IPython.display import IFrame
g =... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: If you wish to examine the Python code for this chapter, see
Step2: The Riemann problem
Step3: The plot above shows the Hugoniot loci in the $... |
9,064 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib.ticker import MultipleLocator
import scipy
from scipy import interpolate
from scipy.interpolate import interp1d
import scipy.io.wavfile as wf
import matplotlib.pyplot as plt
import numpy as np
def wav2file(fname, data, sr):
Write wave data to ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Import the required modules
Step3: Functions for working with signals
Step9: Plot signals
Step14: Signal processing functions
Step15: Analyz... |
9,065 | <ASSISTANT_TASK:>
Python Code:
import npfl103
import os
dpath = os.path.join('.', 'tutorial-assignment')
dlist = os.path.join(dpath, 'documents.list')
qlist = os.path.join(dpath, 'topics.list')
from npfl103.io import Collection
coll_docs = Collection(dlist)
from npfl103.io import Topic
coll_queries = Collection(qlis... | <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: Tutorial data
Step2: Loading documents and queries
Step3: Notice that creating the Collection was fast. This is because the whole pipeline in ... |
9,066 | <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
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<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... |
9,067 | <ASSISTANT_TASK:>
Python Code:
x = [1,3,5]
x.append(7)
x.insert(0,2)
x.pop(-2)
print(x)
x = [5,3,1]
y = [2,3]
z = x + y
z.sort()
print(z)
x = { 'a' : 'b', 'b' : 2, '2' : 6}
x['b']
x = { 'a' : 'b', 'b' : 2, '2' : 6}
x[2]
students = [
{ 'Name':'bob','GPA':3.4 },
{ 'Name':'sue','GPA':2.8 },
{ 'Na... | <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: A. [1,3,5,7]
Step2: A. [1,2,3,3,5]
Step3: A. 2
Step4: A. 2
Step5: Watch Me Code 3
Step6: A. 3.4
|
9,068 | <ASSISTANT_TASK:>
Python Code:
import os
import discoursegraphs as dg
ddg = dg.corpora.pcc.get_document('maz-6728')
dg.DATA_ROOT_DIR
# dg.corpora.pcc.get_files_by_layer('syntax')
tdg = dg.read_tiger(os.path.join(
dg.DATA_ROOT_DIR,
'potsdam-commentary-corpus-2.0.0/syntax/maz-11766.xml'))
# dg.info(tdg)
#... | <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: There are only very few RST spans that match to non-S/CS nodes
Step3: Are there any 'S'/'CS' that are not sentence root nodes?
|
9,069 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')
from libs import utils
# utils.<tab>
files = utils.get_celeb_files()
img = plt.imread(files[50])
# img.<tab>
print(img)
# If nothing is drawn and you are using notebook, try uncommenting the n... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: I'll be using a popular image dataset for faces called the CelebFaces dataset. I've provided some helper functions which you can find on the re... |
9,070 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
incomes = np.random.normal(27000, 15000, 10000) # (center around, stdev, population )
np.mean(incomes) # calculate mean
%matplotlib inline
import matplotlib.pyplot as plt
# segment the income data into 50 buckets and plot as a histogram
plt.hist(incomes, 50)
plt.show()... | <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 can segment the income data into 50 buckets, and plot it as a histogram
Step2: Now compute the median - since we have a nice, even distribut... |
9,071 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
import os
import sys
import pandas as pd
import numpy as np
%matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns
import datetime
#set current working directory
os.chdir('D:/Practical Time Series')
#Read the dataset into a pand... | <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 this notebook, we will use a multi-layer perceptron to develop time series forecasting models.
Step2: To make sure that the rows are in the ... |
9,072 | <ASSISTANT_TASK:>
Python Code:
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
vgg_dir = 'tensorflow_vgg/'
# Make sure vgg exists
if not isdir(vgg_dir):
raise Exception("VGG directory doesn't exist!")
class DLProgress(tqdm):
last_block = 0
def hook(self, block_... | <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: Flower power
Step2: ConvNet Codes
Step3: Below I'm running images through the VGG network in batches.
Step4: Building the Classifier
Step5: ... |
9,073 | <ASSISTANT_TASK:>
Python Code:
import graphlab
sales = graphlab.SFrame.read_csv('Philadelphia_Crime_Rate_noNA.csv/')
sales
graphlab.canvas.set_target('ipynb')
sales.show(view="Scatter Plot", x="CrimeRate", y="HousePrice")
crime_model = graphlab.linear_regression.create(sales, target='HousePrice', features=['CrimeRat... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load some house value vs. crime rate data
Step2: Exploring the data
Step3: Fit the regression model using crime as the feature
Step4: Let's s... |
9,074 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import math
def velocity(radius, model='galaxy'):
describe the streaming velocity as function of radius in or around an object
such as a star or a galaxy. We usually define the velocity to be 1 at a radius of ... | <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: Initialize the data
Step3: Plotting the Rotation Curve
Step4: This curve of velocity as function of radius is called a Rotation Curve, and ext... |
9,075 | <ASSISTANT_TASK:>
Python Code:
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
import tensorflow as tf
# Creates a graph.
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c = tf.matmul(... | <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 02 -- Manually specifying devices for running Tensorflow code
Step2: Setting up Tensorflow to run on CPU
Step3: Setting up Tensorflow to ... |
9,076 | <ASSISTANT_TASK:>
Python Code:
database_path = os.path.join('..', 'data', 'Ana', 'database', 'Garmin-Ana-180226-1.csv')
print(os.path.abspath(database_path))
data = pd.read_csv(database_path)
data.columns = [s.replace('_', ' ') for s in data.columns]
data = data.set_index('file name')
data.head()
data.shape
data.groupb... | <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: Filter the data
Step2: Scale and encode the data
Step3: Split the data
Step4: Train the model
Step5: Test the model
|
9,077 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
invocations = pd.read_csv("datasets/test_code_invocations.csv", sep=";")
invocations.head()
invocation_matrix = invocations.pivot_table(
index=['test_type', 'test_method'],
columns=['prod_type', 'prod_method'],
values='invocations',
fill_value=0
)
# s... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: What we've got here are all names of our test types (test_type) and production types (prod_type) as well as the signatures of the test methods (... |
9,078 | <ASSISTANT_TASK:>
Python Code:
from sklearn.tree import DecisionTreeClassifier as dtc
X = [[0, 0], [1, 1]]
Y = [0, 1]
clf = dtc()
clf = clf.fit(X, Y)
clf.predict([[2., 2.]])
clf.predict_proba([[2., 2.]])
from sklearn.datasets import load_iris
from sklearn import tree
iris = load_iris()
print(iris.DESCR)
print(iris.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: After being fitted, the model can then be used to predict the class of samples
Step2: Alternatively, the probability of each class can be predi... |
9,079 | <ASSISTANT_TASK:>
Python Code:
!pip install git+https://github.com/google/starthinker
from starthinker.util.configuration import Configuration
CONFIG = Configuration(
project="",
client={},
service={},
user="/content/user.json",
verbose=True
)
FIELDS = {
'auth_read':'user', # Credentials used for reading... | <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. Set Configuration
Step2: 3. Enter BigQuery Query To View Recipe Parameters
Step3: 4. Execute BigQuery Query To View
|
9,080 | <ASSISTANT_TASK:>
Python Code:
strat_train_set_copy = strat_train_set.copy()
housing.plot(kind="scatter", x='longitude', y='latitude')
housing.plot(kind="scatter", x='longitude', y='latitude', alpha=0.1)
strat_train_set_copy.plot(kind='scatter', x='longitude', y='latitude', alpha=0.4,
s=strat_... | <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: Experimenting with Attribute Combinations
Step2: 2.5 Prepare the Data for Machine Learning Algorithms
Step3: Handling Text and Categorical Att... |
9,081 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import datetime
from matplotlib import pyplot as plt
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
df = pd.read_csv('data/pm25.csv')
print(df.shape)
df.head()
df.isnull().sum()*100/df.shape[0]
df.dropna(subset=['pm2.5'], axis=0... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Note
Step3: Note
|
9,082 | <ASSISTANT_TASK:>
Python Code:
import scipy
import numpy as np
a = np.array([[26, 3, 0], [3, 195, 1], [0, 1, 17]])
a = 1-np.sign(a)
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Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
|
9,083 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function, division
%matplotlib inline
import matplotlib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# use matplotlib style sheet
plt.style.use('ggplot')
# import the t-distribution from scipy.stats
from scipy.stats import t
y = np.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: CI for continuous data, Pg 18
Step2: Numpy uses a denominator of N in the standard deviation calculation by
Step3: CI for proportions, Pg 18
S... |
9,084 | <ASSISTANT_TASK:>
Python Code:
import cv2
import matplotlib.pyplot as plt
%matplotlib inline
# TODO: Feel free to try out your own images here by changing img_path
# to a file path to another image on your computer!
img_path = 'data/udacity_sdc.png'
# load color image
bgr_img = cv2.imread(img_path)
# convert to graysc... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Define and visualize the filters
Step2: Define a convolutional layer
Step3: Visualize the output of each filter
Step4: Let's look at the outp... |
9,085 | <ASSISTANT_TASK:>
Python Code:
labVersion = 'cs190.1x-lab3-1.0.4'
print labVersion
# load testing library
from test_helper import Test
import os.path
baseDir = os.path.join('mnt', 'spark-mooc')
inputPath = os.path.join('cs190', 'millionsong.txt')
fileName = os.path.join(baseDir, inputPath)
numPartitions = 2
rawData = ... | <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: WARNING
Step3: (1b) Using LabeledPoint
Step5: Visualization 1
Step6: (1c) Find the range
Step7: (1d) Shift labels
Step8: Visualization 2... |
9,086 | <ASSISTANT_TASK:>
Python Code:
import sys
import math
import numpy as np
import pandas as pd
import scipy.optimize as so
import scipy.integrate as si
import matplotlib.pyplot as plt
import nest
%matplotlib inline
plt.rcParams['figure.figsize'] = (12, 3)
def Vpass(t, V0, gNaL, ENa, gKL, EK, taum, I=0):
tau_eff = ta... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Neuron Model
Step2: Agreement is excellent.
Step3: Agreement is as good as possible
Step5: ISIs are as predicted
Step6: I_h channel
Step7: ... |
9,087 | <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['ecc'] = 0.2
b['dperdt'] = 2.0 * u.deg/u.d
b.add_dataset('lc', times=np.linspace(0... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: As always, let's do imports and initialize a logger and a new Bundle. See Building a System for more details.
Step2: In order for apsidal moti... |
9,088 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
%matplotlib inline
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
# import all shogun classes
from shogun import *
import shogun as sg
#number of data points.
n=100
#generate a random 2d line(y1 = mx1 + c)
m = random.randint(1,10)
c = random.randint... | <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: Some Formal Background (Skip if you just want code examples)
Step2: Step 2
Step3: Step 3
Step4: Step 5
Step5: In the above figure, the blue ... |
9,089 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np, matplotlib.pyplot as plt, matplotlib.gridspec as gridspec
from mpl_toolkits.basemap import Basemap
from ipywidgets import interact, interactive, fixed
import ipywidgets as widgets
[ra, dec, z], [ra_isol, dec_isol, z_isol], [ra_pair, dec_pair, z_pair... | <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: Run the following code to load the catalogues of galaxies that will be represented. In this case we represent the LSS by all the galaxies in the... |
9,090 | <ASSISTANT_TASK:>
Python Code:
%%cython
cpdef noop():
pass
%load_ext Cython
%%cython
cimport numpy
cpdef cysum(numpy.ndarray[double] A):
Compute the sum of an array
cdef double a=0
for i in range(A.shape[0]):
a += A[i]
return a
def pysum(A):
Compute the sum of an array
a = 0
for ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step2: Customizing IPython - Extensions
Step3: Let's see what Cython's load_ipython_extension function looks like
Step4: Our own extension
Step5: %i... |
9,091 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
d = 5e-9 # particle radius in meters
eta = 1.0e-3 # viscosity of water in SI units (Pascal-seconds) at 293 K
kB = 1.38e-23 # Boltzmann constant
T = 293 # Temperature in degrees Kelvin
D = kB*T/(3*np.pi*eta*d) # [m^2 / s]
D
Du = D*(1e6)**2/(1e3) # [u... | <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: Or expressing $D$ in $\textrm{nm}^2 /\mu s$
Step2: We can also estimate $D$ experimentally from the knowledge of the PSF and the diffusion time... |
9,092 | <ASSISTANT_TASK:>
Python Code:
%%capture
%matplotlib inline
import numpy as np
import sympy as sp
import matplotlib.pyplot as plt
# To get equations the look like, well, equations, use the following.
from sympy.interactive import printing
printing.init_printing(use_latex=True)
from IPython.display import display
# Tool... | <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 member of the Lorentz group that reverses time is remarkably simple
Step2: Create a 4-vector.
Step3: Do the time reversal a bunch of times... |
9,093 | <ASSISTANT_TASK:>
Python Code:
# The Python Spark (pyspark) libraries include functions designed to be run on columns of data
# stored in Spark data frames. They need to be imported in order to use them. Here we
# are going to use
from pyspark.sql.functions import year
# The matplotlib package is used for graphing. Th... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Loading the data set
Step2: Examining the data
Step3: Next we can look at the first row of data. The (1) after head tells Python how many rows... |
9,094 | <ASSISTANT_TASK:>
Python Code:
# Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.
#
# 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
#
# http://www.apache.org/licenses/LICENSE... | <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: ユニバーサルセンテンスエンコーダー
Step2: Tensorflow のインストールに関する詳細は、https
Step3: セマンティックテキストの類似性タスクの例
Step4: 類似性の視覚化
Step5: 評価
Step7: 文章埋め込みの評価
|
9,095 | <ASSISTANT_TASK:>
Python Code:
import os
import tensorflow.compat.v1 as tf
import pprint
assert 'COLAB_TPU_ADDR' in os.environ, 'Did you forget to switch to TPU?'
tpu_address = 'grpc://' + os.environ['COLAB_TPU_ADDR']
with tf.Session(tpu_address) as sess:
devices = sess.list_devices()
pprint.pprint(devices)
device_is... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Authentication
Step2: Check imports
Step3: Training ESRGAN
Step4: Load Training Dataset
Step5: Visualize the dataset
Step6: Network Archite... |
9,096 | <ASSISTANT_TASK:>
Python Code:
purity_coll = client['run']['purity']
purity_coll.count()
d = purity_coll.find_one()
for d in purity_coll.find(sort=(('calculation_time', -1), )):
print(str(d['calculation_time']), parse_expr(d['function']))
import numpy as np
data = np.array([
4, 0.9948, # Julien
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: Try to add drift velocity correction function
Step2: Systematic error on drift velocity estimates is around 0.2%, see
Step3: Insert in runs d... |
9,097 | <ASSISTANT_TASK:>
Python Code:
# Add tools
# NOTE: This should only be needed if you do not store the notebook on the lxmls root
import sys
sys.path.append('../../')
from pdb import set_trace
# Location of Part-of-Speech WSJ Data
WSJ_TRAIN = "../../data/train-02-21.conll"
WSJ_TEST = "../../data/test-23.conll"
WSJ_DEV =... | <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: Model configuration
Step2: Exercise 6.2
Step3: The following example should help you understand about matrix multiplications and passing value... |
9,098 | <ASSISTANT_TASK:>
Python Code:
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
!pip install --user google-cloud-bigquery==1.25.0
!pip install --user apache-beam[interactive]==2.24.0
import apache_beam as beam
print(beam.__version__)
import tensorflow as tf
print("TensorFlow version: ",tf.version.VE... | <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: Kindly ignore the deprecation warnings and incompatibility errors related to google-cloud-storage.
Step2: NOTE
Step3: You may receive a UserWa... |
9,099 | <ASSISTANT_TASK:>
Python Code:
from EQTransformer.utils.plot import plot_data_chart
plot_data_chart('preproc/time_tracks.pkl', time_interval=10)
from EQTransformer.utils.plot import plot_detections, plot_helicorder
plot_helicorder(input_mseed='downloads_mseeds/CA06/GS.CA06.00.HHZ__20190901T000000Z__20190902T000000Z.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: Check your current directory for 'data_chart.png'
Step2: Now you can mark those events that you have detected in your helicorder plot to check ... |
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