Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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11,800 | <ASSISTANT_TASK:>
Python Code:
#subject lines that have dates, e.g. 12/01/99
[line for line in subjects if re.search("\d\d/\d\d/\d\d", line)]
[line for line in subjects if re.search("[aeiou][aeiou][aeiou][aeiou]", line)]
[line for line in subjects if re.search("F[wW]:", line)]
[line for line in subjects if res.search... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: define your own character classes
Step2: metacharacters
Step3: aside
Step4: metacharacters 3
Step5: more metacharacters
|
11,801 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import numpy, pandas
from rep.utils import train_test_split
from sklearn.metrics import roc_auc_score
data = pandas.read_csv('toy_datasets/Higgs.csv', sep='\t')
labels = data['Label'].values
labels = labels == 's'
sample_weight = data['Weight'].values
train_data, test_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: Loading data for Higgs Boson Challenge
Step2: Variables used in training
Step3: Metric definition
Step4: Compute threshold vs metric quality
... |
11,802 | <ASSISTANT_TASK:>
Python Code:
import string
print(string.ascii_uppercase)
if 'b' in string.ascii_uppercase:
print("Yes, the letter is in string.ascii_uppercase")
else:
print("No, the string is not in string.ascii_uppercase")
print(string.ascii_lowercase)
print(string.whitespace)
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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: Our initial example will use the uppercase letters that are in the string library.
Step2: Here we will check to see if a letter is in the strin... |
11,803 | <ASSISTANT_TASK:>
Python Code:
counter = 1
while counter <= 10:
print(counter)
counter = counter + 1
print("end")
counter = 1
product = 1
while counter <= 5:
product = product * counter
print("counter: ", counter)
print("product: ", product)
counter = counter + 1
print(product)
... | <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: Упражнение
Step2: Упражнение
|
11,804 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
x = np.array([0, 1, 1, 1, 3, 1, 5, 5, 5])
y = np.array([0, 2, 3, 4, 2, 4, 3, 4, 5])
a = 1
b = 4
result = ((x == a) & (y == b)).argmax()
if x[result] != a or y[result] != b:
result = -1
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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:
|
11,805 | <ASSISTANT_TASK:>
Python Code:
# Run this cell to set up the notebook, but please don't change it.
# These lines import the Numpy and Datascience modules.
import numpy as np
from datascience import *
# These lines do some fancy plotting magic.
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
plt.sty... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: 1. Preliminaries
Step2: Question 1.2
Step3: Question 1.3
Step4: Question 1.5
Step5: Question 1.6
Step6: Since we don't know what the popula... |
11,806 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function, division
%matplotlib inline
import numpy as np
import brfss
import thinkstats2
import thinkplot
df = brfss.ReadBrfss(nrows=None)
def SampleRows(df, nrows, replace=False):
indices = np.random.choice(df.index, nrows, replace=replace)
sample =... | <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: Scatter plots
Step2: The following function selects a random subset of a DataFrame.
Step3: I'll extract the height in cm and the weight in kg ... |
11,807 | <ASSISTANT_TASK:>
Python Code:
from sklearn.datasets import load_linnerud
linnerud = load_linnerud()
chinups = linnerud.data[:,0]
plt.hist( # complete
plt.hist( # complete
# complete
# complete
plt.hist(# complete
plt.hist(chinups, histtype = 'step')
# this is the code for the rug plot
plt.plot(chinups, np.zeros_li... | <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: Problem 1a
Step2: Already with this simple plot we see a problem - the choice of bin centers and number of bins suggest that there is a 0% pro... |
11,808 | <ASSISTANT_TASK:>
Python Code:
# Author: Tommy Clausner <tommy.clausner@gmail.com>
#
# License: BSD (3-clause)
import os
import os.path as op
import mne
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
sample_dir = op.join(data_path, 'MEG', 'sample')
subjects_dir = op.join(data_path, 'subj... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Setup paths
Step2: Load example data
Step3: Setting up SourceMorph for SourceEstimate
Step4: We also need to specify the set of vertices to m... |
11,809 | <ASSISTANT_TASK:>
Python Code:
#installing pandas libraries
!pip install pandas-datareader
!pip install --upgrade html5lib==1.0b8
#There is a bug in the latest version of html5lib so install an earlier version
#Restart kernel after installing html5lib
import pandas as pd #pandas library
from pandas_datareader import d... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: <h2>Imports</h2>
Step2: <h2>The structure of a dataframe</h2>
Step3: <h3>Accessing columns and rows</h3>
Step4: <h3>Getting column data</h3>
... |
11,810 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from scipy.special import expit
line = np.linspace(-3, 3, 100)
plt.figure(figsize=(10,8))
plt.plot(line, np.tanh(line), label="tanh")
plt.plot(line, np.maximum(line, 0), label="relu")
plt.plot(line, expit(line), label='... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: For a small neural network with a single hidden layer with three nodes, the full formula for computing ŷ in the case of regression would be (whe... |
11,811 | <ASSISTANT_TASK:>
Python Code:
from numpy.linalg import pinv
from Orange.classification import Learner, Model
class LinearRegression(Learner):
def fit(self, X, Y, W=None):
coef = pinv(X.T.dot(X)).dot(X.T).dot(Y)
return LinearRegressionModel(coef)
class LinearRegressionModel(Model):
def __init__(... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Note that the above simplified version of linear regression does not fit the intercept and ignores instance weights.
Step2: We see that the err... |
11,812 | <ASSISTANT_TASK:>
Python Code:
import yaml
# Set `PATH` to include the directory containing TFX CLI and skaffold.
PATH=%env PATH
%env PATH=/home/jupyter/.local/bin:{PATH}
!python -c "import tfx; print('TFX version: {}'.format(tfx.__version__))"
!python -c "import kfp; print('KFP version: {}'.format(kfp.__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: Validate lab package version installation
Step2: Note
Step3: Note
Step4: The config.py module configures the default values for the environme... |
11,813 | <ASSISTANT_TASK:>
Python Code:
tocrawl = []
def crawl(url):
html = download(url)
page = parse(html)
urls = extract_links(page)
tocrawl.append(urls)
return tocrawl
starter_url = "www.example.com"
tocrawl = crawl(starter_url)
while len(tocrawl) != 0:
for url in tocrawl:
crawl(url)
#!/usr/... | <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: Evidemment c'est un tout petit peu plus compliqué que ça...
Step2: Définissons ensuite l'encodage pour prendre en compte les accents
Step3: un... |
11,814 | <ASSISTANT_TASK:>
Python Code:
%%writefile ComplaintDistribution.py
from mrjob.job import MRJob
class ComplaintDistribution(MRJob):
def mapper(self, _, lines):
line = lines[:30]
if "Debt collection" in line:
self.increment_counter('Complaint', 'Debt collection', 1)
elif "Mortgage... | <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: HW 3.2 Analyze the performance of your Mappers, Combiners and Reducers using Counters
Step2: Please use multiple mappers and reducers for these... |
11,815 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
iowadf= pd.read_csv("Class05_iowa_data.csv")
iowadf.head()
# The sales data looks like it isn't a float like we want it to be (the presence of a $ in front is my clue that there may be something wrong.) Let's look at the data types to be sure.
iowadf.dtypes
# Sure enou... | <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 only lose 25 rows out of 13,000. I'm going to go with that- it simplifies further computations.
Step2: We now want the sum of all of the Sal... |
11,816 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams["figure.figsize"] = (20,5) # This can be the default, or else, you can also specify this every time you generate a graph
import vincent
vincent.core.initialize_note... | <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: Import the CSV file
Step2: Basic Exploration
Step3: Our dataset seems really clean, without any missing values, which is wonderful!
Step4: We... |
11,817 | <ASSISTANT_TASK:>
Python Code:
# Authors: Chris Holdgraf <choldgraf@gmail.com>
# Jona Sassenhagen <jona.sassenhagen@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)
import mne
import numpy as np
import matplotlib.pyplot as plt
# Load the data from the internet
path = mne.da... | <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 use this metadata attribute to select subsets of Epochs. This
Step2: Next we'll choose a subset of words to keep.
Step3: Note that trad... |
11,818 | <ASSISTANT_TASK:>
Python Code:
IMAGE_PATH = "datasets/CIFAR10"
import os, subprocess
from urllib.request import urlretrieve
dataFile = "test.zip"
if not os.path.isdir(IMAGE_PATH):
os.makedirs(IMAGE_PATH)
urlretrieve("https://mmlspark.azureedge.net/datasets/CIFAR10/test.zip",
IMAGE_PATH + ".zip")... | <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 images are loaded from the directory (for fast prototyping, consider loading a fraction of
Step2: When collected from the DataFrame, the im... |
11,819 | <ASSISTANT_TASK:>
Python Code:
plt.imshow(plt.imread('./res/find_connected.png'))
plt.figure(figsize=(12,8))
plt.imshow(plt.imread('./res/fig21_1.png'))
# Exercises
plt.figure(figsize=(15,8))
plt.imshow(plt.imread('./res/fig21_2.png'))
# Exercises
plt.imshow(plt.imread('./res/fig21_4.png'))
plt.imshow(plt.imread('.... | <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: 21.2 Linked-list representation of disjoint sets
Step2: MAKE-SET and FIND-SET requires $O(1)$ time.
Step3: 21.3 Disjoint-set forests
Step4... |
11,820 | <ASSISTANT_TASK:>
Python Code:
def parse(line):
Parses a line from the colors dataset.
items = tf.string_split([line], ",").values
rgb = tf.string_to_number(items[1:], out_type=tf.float32) / 255.0
color_name = items[0]
chars = tf.one_hot(tf.decode_raw(color_name, tf.uint8), depth=256)
length = tf.cast(tf.sh... | <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: Case study
Step7: To show the use of control flow, we write the RNN loop by hand, rather than using a pre-built RNN model.
Step9: We will now ... |
11,821 | <ASSISTANT_TASK:>
Python Code:
import os
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG
! pip3 install -U google-cloud-storage $USER_FLAG
! pip3 install $USER kfp --upgra... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Install the latest GA version of google-cloud-storage library as well.
Step2: Install the latest GA version of KFP SDK library as well.
Step3: ... |
11,822 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
# Module with the neural net classes
import DNN
import Solvers
N = 100
data = np.concatenate((np.random.multivariate_normal(mean=[0, 0], cov=[[0.5, 0],[0, 0.5]], size=N),
np.ra... | <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 wil demonstrate the nonlinear representation capabilities fot the multilayer feedforward network with the XOR problem. First, let's create a ... |
11,823 | <ASSISTANT_TASK:>
Python Code:
import json
from pybbn.graph.variable import Variable
from pybbn.graph.node import BbnNode
from pybbn.graph.edge import Edge, EdgeType
from pybbn.graph.dag import Bbn
a = BbnNode(Variable(0, 'a', ['t', 'f']), [0.2, 0.8])
b = BbnNode(Variable(1, 'b', ['t', 'f']), [0.1, 0.9, 0.9, 0.1])
bbn ... | <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: Deserializing
Step2: Serde a join tree
Step3: Deserializing
Step4: Updating the conditional probability tables (CPTs) of a BBN nodes in a jun... |
11,824 | <ASSISTANT_TASK:>
Python Code:
import os
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG
! pip3 install -U google-cloud-storage $USER_FLAG
if os.environ["IS_TESTING"]:
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Install the latest GA version of google-cloud-storage library as well.
Step2: Restart the kernel
Step3: Before you begin
Step4: Region
Step5:... |
11,825 | <ASSISTANT_TASK:>
Python Code:
import arviz as az
import stan
import numpy as np
import matplotlib.pyplot as plt
# enable PyStan on Jupyter IDE
import nest_asyncio
nest_asyncio.apply()
np.random.seed(26)
xdata = np.linspace(0, 50, 100)
b0, b1, sigma = -2, 1, 3
ydata = np.random.normal(loc=b1 * xdata + b0, scale=sigma)... | <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: For the example we will use a linear regression.
Step3: Now we will write the Stan code, keeping in mind that it must be able to compute the po... |
11,826 | <ASSISTANT_TASK:>
Python Code:
### import flame module
from flame import Machine
### specify lattice file location
lat_file = "LS1FS1_lattice.lat"
### read lattice file in
with open(lat_file, 'rb') as inf:
# create lattice data object M
M = Machine(inf)
### Initialize simulation parameters
# states
S = ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: - Plot energy history
Step2: - plot x, y centroid and rms of overall beam
Step3: - python object data are easy to manage
Step4: - plot beam ... |
11,827 | <ASSISTANT_TASK:>
Python Code:
a = {'x': 1, 'z': 3}
b = {'y': 2, 'z': 4}
# 需在两 dict 中执行查找操作 (先从 a 中找，若是找不到，再在 b 中找)
from collections import ChainMap
c = ChainMap(a,b)
print(c['x'])
print(c['y'])
print(c['z'])
len(c)
list(c.keys())
list(c.values())
c['z'] = 10
c['w'] = 80
del c['x']
c_old = ChainMap(a,b)
c_old
type(c... | <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: 一个 ChainMap 接受多个 dict 将他们在逻辑上变为一个 dict 然后 这些 dict 不是真的合并在一起了 ChainMap 类只是在内部创建了一个容纳这些 dict 的 list and 重新定义了一些常用的 dict 操作来遍历这些列表 大部分 dict 都是正常使用的... |
11,828 | <ASSISTANT_TASK:>
Python Code:
from crpropa import *
## settings for MHD model (must be set according to model)
filename_bfield = "clues_primordial.dat" ## filename of the magnetic field
gridOrigin = Vector3d(0,0,0) ## origin of the 3D data, preferably at boxOrigin
gridSize = 1024 #... | <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: to make use of periodicity of the provided data grid, use
Step2: to not follow particles forever, use
Step3: Uniform injection
Step4: Injecti... |
11,829 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
def random_line(m, b, sigma, size=10):
Create a line y = m*x + b + N(0,sigma**2) between x=[-1.0,1.0]
Param... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step2: Line with Gaussian noise
Step5: Write a function named plot_random_line that takes the same arguments as random_line and creates a random line ... |
11,830 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from jyquickhelper import add_notebook_menu
add_notebook_menu()
import numpy as np
l = [1, 42, 18 ]
a = np.array(l)
print(a)
print(a.dtype)
print(a.ndim)
print(a.shape)
print(a.size)
a
b = np.array(l, dtype=float)
print(b)
print(b.dtype)
l[0] = 1.0
bb = np.array(l)
p... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Numpy arrays
Step2: Creation d'un array
Step3: On peut indiquer explicitement le dtype lors de la création de l'array. Sinon, Numpy sélectionn... |
11,831 | <ASSISTANT_TASK:>
Python Code:
#load libraries
import pandas as pd
import numpy as np
#Supervised learning
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
#Load data set
from sklearn.datasets import load_breast_cancer
cancer = load_breast_cancer()
cancer =pd.DataFrame(cancer.data)
canc... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: The model overfits quite substantially, with a perfect score on the training set and only 63% accuracy on the test set.
Step2: Preprocessing d... |
11,832 | <ASSISTANT_TASK:>
Python Code:
import io, os, sys, types
from IPython import get_ipython
from IPython.nbformat import current
from IPython.core.interactiveshell import InteractiveShell
def find_notebook(fullname, path=None):
find a notebook, given its fully qualified name and an optional path
This turns "... | <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: Import hooks typically take the form of two objects
Step5: Notebook Loader
Step7: The Module Finder
Step8: Register the hook
Step9: After th... |
11,833 | <ASSISTANT_TASK:>
Python Code:
from csp import *
%psource CSP
s = UniversalDict(['R','G','B'])
s[5]
%psource different_values_constraint
%pdoc parse_neighbors
%psource MapColoringCSP
australia, usa, france
%psource queen_constraint
%psource NQueensCSP
eight_queens = NQueensCSP(8)
import copy
class InstruCSP(CS... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Review
Step2: The _ init _ method parameters specify the CSP. Variable can be passed as a list of strings or integers. Domains are passed as ... |
11,834 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from sklearn import datasets
from sklearn import linear_model
import matplotlib.pyplot as plt
import sklearn
print sklearn.__version__
# boston data
boston = datasets.load_boston()
y = boston.target
' '.join(dir(boston))
boston['feature_names']
regr = linear_model.Linea... | <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: 使用sklearn做logistic回归
Step2: 使用sklearn实现贝叶斯预测
Step3: naive_bayes.GaussianNB Gaussian Naive Bayes (GaussianNB)
Step4: cross-validation
Step5... |
11,835 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nasa-giss', 'giss-e2-1h', 'land')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
11,836 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
# Import helper module
from helpers import ex02
# Load one-way BLAST results into a data frame called data_fwd
data_fwd = ex02.read_data("pseudomonas_blastp/B728a_vs_NCIMB_11764.tab")
# Show first few lines of the loaded data
data_fwd.head()
# Show descriptive statistics 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: The first thing we do is load in the BLASTP output we generated, so that we can plot some of the key features. We do that using the ex02.read_da... |
11,837 | <ASSISTANT_TASK:>
Python Code:
from astropy.io import ascii, fits
import pylab as plt
%matplotlib inline
from astropy import wcs
import numpy as np
import xidplus
from xidplus import moc_routines
import pickle
xidplus.__path__[0]
#Folder containing maps
imfolder=xidplus.__path__[0]+'/../test_files/'
pswfits=imfolder+'... | <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 image and catalogue filenames
Step2: Load in images, noise maps, header info and WCS information
Step3: Load in catalogue you want to fit ... |
11,838 | <ASSISTANT_TASK:>
Python Code:
%%writefile ../../user_models/cylinder_Bscan_2D.in
#title: B-scan from a metal cylinder buried in a dielectric half-space
#domain: 0.240 0.210 0.002
#dx_dy_dz: 0.002 0.002 0.002
#time_window: 3e-9
#material: 6 0 1 0 half_space
#waveform: ricker 1 1.5e9 my_ricker
#hertzian_dipole: z 0.040 ... | <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 differences between this input file and the one from the A-scan are the x coordinates of the source and receiver, and the commands needed to... |
11,839 | <ASSISTANT_TASK:>
Python Code:
data = pd.read_csv('bracket-05.tsv', sep='\t')
data = data.\
query('rd1_win > 0').\
rename(columns=dict(rd1_win=1, rd2_win=2, rd3_win=3, rd4_win=4, rd5_win=5, rd6_win=6, rd7_win=7))\
[['team_name', 'team_seed', 1, 2, 3, 4, 5, 6, 7]]
data.head()
data[8] = 0
for col in range(8,... | <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 numbered columns represent the probability that a team will win in that round of the tournament. This of course means that they had to win a... |
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Python Code:
#Implements functional expansions
from functions.FE import FE
#Evaluates accuracy in a dataset for a particular classifier
from fitness import Classifier
#Implements gafe using DEAP toolbox
import ga
from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
i... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Import modules from scikit-learn, numpy and pandas to help us deal with the data
Step2: Load data using pandas. We will use the famous Iris Dat... |
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Python Code:
!find export/probs/
%%bash
LOCAL_DIR=$(find export/probs | head -2 | tail -1)
BUCKET=ai-analytics-solutions-kfpdemo
gsutil rm -rf gs://${BUCKET}/mlpatterns/batchserving
gsutil cp -r $LOCAL_DIR gs://${BUCKET}/mlpatterns/batchserving
gsutil ls gs://${BUCKET}/mlpatterns/batchserving
%%bigqu... | <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 model into BigQuery for batch serving
Step2: Now, do it at scale, on consumer complaints about financial products and services
|
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Python Code:
import os
import zipfile
from math import sqrt
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
%matplotlib inline
# Put files in current direc... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Unzipping files with house sales data
Step2: Loading Sales data, Sales training data, and Sales test data
Step3: Convert to DataFrame data to ... |
11,843 | <ASSISTANT_TASK:>
Python Code:
def number_to_words(n):
Given a number n between 1-1000 inclusive return a list of words for the number.
n=str(n)
key = {1:'one', 2:'two', 3:'three', 4:'four', 5:'five', 6:'six', 7:'seven', 8:'eight', 9:'nine', 10:'ten', 11:'eleven', 12:'twelve', 13:'thirteen', 14:'fourteen', ... | <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: Project Euler
Step2: Now write a set of assert tests for your number_to_words function that verifies that it is working as expected.
Step4: No... |
11,844 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import pylab
# Required imports
from wikitools import wiki
from wikitools import category
# import nltk
import nltk
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
from nltk.corpus import stopwords
from tes... | <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... |
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Python Code:
!pip install -q opencv-python
import os
import tensorflow.compat.v2 as tf
import tensorflow_hub as hub
import numpy as np
import cv2
from IPython import display
import math
# Load the model once from TF-Hub.
hub_handle = 'https://tfhub.dev/deepmind/mil-nce/s3d/1'
hub_model = hub.load(hub... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step3: 导入 TF-Hub 模型
Step4: 演示文本到视频检索
|
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Python Code:
a = 1
a
b = 'pew'
b
%matplotlib inline
import matplotlib.pyplot as plt
from pylab import *
x = linspace(0, 5, 10)
y = x ** 2
figure()
plot(x, y, 'r')
xlabel('x')
ylabel('y')
title('title')
show()
import numpy as np
num_points = 130
y = np.random.random(num_points)
plt.plot(y)
%%latex
\be... | <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 is some text, here comes some latex
Step2: Apos?
Step3: Javascript plots
Step4: bokeh
|
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Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
ydata = np.genfromtxt('dataForNathan.csv', delimiter=',')[:-1]
xdata = np.arange(ydata.size)+1
plt.figure(figsize=(7,7)); plt.xlim(0,64)
plt.plot(xdata, ydata); plt.scatter(xdata,ydata, c='k')
plt.show()
import 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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Description:
Step1: He is choosing to model the function as the difference of Gamma distributions
|
11,848 | <ASSISTANT_TASK:>
Python Code:
import os
from gensim import corpora, models
%load_ext memory_profiler
import scipy
scipy.show_config()
MODELS_DIR = "../Data/models/lda_standard"
num_topics = 10
dictionary = corpora.Dictionary.load(os.path.join(MODELS_DIR,'twentyNewsGroup.dict'))
corpus = corpora.MmCorpus(os.path.join(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: Default settings
Step2: Additional parameters
Step3: Testing LDA with iterations with 3 workers
Step4: Number of topics
Step5: Number of pas... |
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Python Code:
# Install jdk8
!apt-get install openjdk-8-jdk-headless -qq > /dev/null
import os
# Set environment variable JAVA_HOME.
os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-8-openjdk-amd64"
!update-alternatives --set java /usr/lib/jvm/java-8-openjdk-amd64/jre/bin/java
!java -version
# Install lat... | <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: Install Analytics Zoo
Step2: Data-Parallel Pandas with XShards for Distributed Deep Learning
Step3: Init Orca Context
Step4: Data Preprocessi... |
11,850 | <ASSISTANT_TASK:>
Python Code:
print('"{}" = "{}"'.format('A', ord('A')))
print('"{}" = "{}"'.format('a', ord('a')))
print('"{}" = "{}"'.format(88, chr(88)))
print('"{}" = "{}"'.format(112, chr(112)))
for n in range(5):
print(n)
for char in ['p', 'y', 't', 'h', 'o', 'n']:
print(char)
for char in "python":
... | <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: To implement an ASCII version of gematria in Python, we need to turn each letter into a number and add them all together. So, to start, note th... |
11,851 | <ASSISTANT_TASK:>
Python Code:
import sympy as sy
import numpy as np
from sympy import *
r = Symbol('r')
I = integrate(exp(-2*r**2)*r**2,(r,0,+oo))
C = sqrt(1/I)
print(latex(simplify(C)))
E = C**2*integrate((-2*r**4+3*r**2-r)*exp(-2*r**2),(r,0,oo))
print('Expected value is %0.4f Ha.'%E)
# Hydrogen atom energy equation... | <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: So the normalized 1s wavefunction is $\tilde{R}_{10}(r) = \frac{2}{\sqrt[4]{\pi}} 2^{\frac{3}{4}} e^{-r^2} = (\frac{128}{\pi}) ^ {\frac{1}{4}} e... |
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Python Code:
import pandas as pd
import featuretools as ft
from featuretools.selection import (
remove_highly_correlated_features,
remove_highly_null_features,
remove_single_value_features,
)
from featuretools.primitives import NaturalLanguage
from featuretools.demo.flight import load_flig... | <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: Remove Highly Null Features
Step2: We look at the above feature matrix and decide to remove the highly null features
Step3: Notice that callin... |
11,853 | <ASSISTANT_TASK:>
Python Code:
def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].uppe... | <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: Lesson
Step3: Project 1
Step4: We'll create three Counter objects, one for words from postive reviews, one for words from negativ... |
11,854 | <ASSISTANT_TASK:>
Python Code:
print("Happy birthday to you.")
print("Happy birthday to you.")
print("Happy birthday, dear Chris.")
print("Happy birthday to you.")
print("Happy birthday to you.")
print("Happy birthday to you.")
print("Happy birthday, dear Thomas.")
print("Happy birthday to you.")
def birthdaySong(nam... | <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: But what if we wanted to reuse this code to congratulate someone else, e.g. named Thomas? There's basically two (fundamentally different) approa... |
11,855 | <ASSISTANT_TASK:>
Python Code:
from enum import Enum
import itertools
import random
from collections import Counter
import numpy as np
from plotting import *
from multiprocessing import Pool
from tqdm import tqdm_notebook
%matplotlib inline
class Party(Enum):
D = 1
R = 2
color_trans = {Party.D:'blue', Par... | <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'll need a way to track which party the Senate & President are part of. For now, let's just stick with the two major parties and create... |
11,856 | <ASSISTANT_TASK:>
Python Code:
with open('input.txt', 'rt') as f:
moves = next(f).rstrip().split(',')
import re
import numpy as np
import copy
def shuffle(p, moves):
s = copy.copy(p)
for move in moves:
spin = re.search('s(\d+)', move)
swapx = re.search('x(\d+)\/(\d+)', move)
swapp = ... | <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: Test
Step2: Solution
Step3: Part 2
Step4: Test
Step5: Solution
|
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Python Code:
class PixWord2Vec:
# vocabulary indexing
index2word = None
word2indx = None
# embeddings vector
embeddings = None
# Normailized embeddings vector
final_embeddings = None
# hidden layer's weight and bias
softmax_weights = None
softma... | <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: 資料前處理
Step2: 設計 Graph
Step3: Build Category2Vec
Step4: 測試 Category Vec
Step5: 開始轉換成向量
Step6: Load TagVectors
Step7: 進行隨機抽樣驗證
|
11,858 | <ASSISTANT_TASK:>
Python Code:
import os
import numpy as np
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.optimizers import SGD
from keras.utils import np_utils
def unpickle(file):
import cPickle
fo = open(file, 'rb')
dict = cPickle... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: b) Función para escalar data entre rango (-1,1) o bien normalización.
Step2: A continuación se cargará un batch del dataset y se mostrarán imág... |
11,859 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
#from skimage.io import imread
import matplotlib.gridspec as gridspec
plt.rcParams['image.interpolation'] = 'none'
plt.rcParams['image.cmap'] = 'gray'
figsize(4,4)
size = 256
img = np.zeros((size,size), dtype=np.uint8)
t = linspace(start=0, stop=50*pi, endpoint=False, num=si... | <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 this image with clear direction of fibers.
Step3: The function we want to make better
Step4: Per block optimation
Step5: Threshold
Step6:... |
11,860 | <ASSISTANT_TASK:>
Python Code:
# Additional Libraries
%matplotlib inline
import matplotlib.pyplot as plt
# Import relevant libraries:
import time
import numpy as np
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from sklearn import preprocessing
from sklearn.preprocessing import MinMaxScaler
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: Local, individual load of updated data set (with weather data integrated) into training, development, and test subsets.
Step2: The Best RF Clas... |
11,861 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib as mpl
import pandas as pd
import json
import pandas as pd
import csv
import os
import re
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn import svm
from sklearn.linear_model i... | <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 a function that we'll use later to plot the results of a linear SVM classifier
Step2: Load in the sample JSON file and view its content... |
11,862 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
from random import normalvariate, uniform, weibullvariate
# Make several sets of data; one randomly sampled
# from a normal distribution and others that aren't.
n = 100
d_norm = [normalvariate(0,1) for x in range(n)]
d_unif = [unifo... | <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: Make probability plots
Step2: Interesting. Normal distribution follows the quantiles well and has the highest $R^2$ value, but both the uniform... |
11,863 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
# import lsst sims maf modules
import lsst.sims.maf
import lsst.sims.maf.db as db
import lsst.sims.maf.metrics as lsst_metrics
import lsst.sims.maf.slicers as slicers
import lsst.sims.maf.stackers as stackers
import lsst.sims.maf.plots as... | <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: General Input
Step2: SQL Query
Step3: Metrics
Step4: Slicer
Step5: Plot functions and customization
Step6: Bundles
Step7: Plot a light cur... |
11,864 | <ASSISTANT_TASK:>
Python Code:
import usau.reports
import usau.fantasy
from IPython.display import display, HTML
import pandas as pd
pd.options.display.width = 200
pd.options.display.max_colwidth = 200
pd.options.display.max_columns = 200
def display_url_column(df):
Helper for formatting url links
df.url = df.url.a... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Stats Quality for 2016 D-I College Nationals
Step2: Since we should already have the data downloaded as csv files in this repository, we will n... |
11,865 | <ASSISTANT_TASK:>
Python Code:
# Run some setup code for this notebook.
import random
import numpy as np
from cs231n.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
# This is a bit of magic to make matplotlib figures appear inline in the notebook
# rather than in a new window.
%matplotlib inline
plt.rcPa... | <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 would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps
Step2: Inline Qu... |
11,866 | <ASSISTANT_TASK:>
Python Code:
import gambit
gambit.__version__
g = gambit.Game.read_game("poker.efg")
g
g.players
g.players["Alice"]
g.players["Alice"].infosets
g.players.chance
g.players.chance.infosets
g.players.chance.infosets[0].actions
deal = g.players.chance.infosets[0]
deal.actions["A"].prob
deal.acti... | <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: Gambit version 16.0.0 is the current development version. You can get it from http
Step2: Inspecting a game
Step3: Gambit's .efg format is a ... |
11,867 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import sklearn
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_digits
from sklearn.pipeline import Pipeline
from sklearn.decomposition import PCA
digits = load_digits()
X_digits = digits.data
y_digits = digits.target
logistic = LogisticR... | <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: Finding the best model
|
11,868 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import time
import machine_learning_helper as machine_learning_helper
import metrics_helper as metrics_helper
import sklearn.neighbors, sklearn.linear_model, sklearn.ensemble, sklearn.naive_bayes
from sklearn.model_selection import KFold, train_test_... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Read .csv files
Step2: Construct sessions data frame
Step3: 1. From data frame to matrix
Step4: 2. From data frame to matrix
Step5: For Me... |
11,869 | <ASSISTANT_TASK:>
Python Code:
# Copyright 2019 The Google AI Language Team 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unl... | <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: Running a Tapas fine-tuned checkpoint
Step2: Fetch models fom Google Storage
Step3: Imports
Step5: Load checkpoint for prediction
Step7: Pre... |
11,870 | <ASSISTANT_TASK:>
Python Code:
# Load the network. This network, while in reality is a directed graph,
# is intentionally converted to an undirected one for simplification.
G = cf.load_physicians_network()
# Make a Circos plot of the graph
from nxviz import CircosPlot
c = CircosPlot(G)
c.draw()
# Example code.
def in_... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step2: Question
Step3: In reality, NetworkX already has a function that counts the number of triangles that any given node is involved in. This is pro... |
11,871 | <ASSISTANT_TASK:>
Python Code:
# As usual, a bit of setup
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from cs231n.solver impo... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Batch Normalization
Step2: Batch normalization
Step3: Batch Normalization
Step4: Batch Normalization
Step5: Fully Connected Nets with Batch ... |
11,872 | <ASSISTANT_TASK:>
Python Code::
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error, mean_absolute_error
# initialise & fit a ridge regression model with alpha set to 1
# if the model is overfitting, increase the alpha value
model = Ridge(alpha=1)
model.fit(X_train, y_train)
# create 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:
|
11,873 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import xgboost
import shap
N = 40000
M = 2
# randomly create binary features for (is_young, and is_female)
X = (np.random.randn(N,2) > 0) * 1
# force the first sample to be a young boy
X[0,0] = 1
X[0,1] = 0
# you survive only if you are young or female
y = ((X[:,0] + 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: Create a dataset following an OR function
Step2: Train an XGBoost model to mimic this OR function
Step3: Explain the prediction for a young bo... |
11,874 | <ASSISTANT_TASK:>
Python Code:
import sys
print('{0[0]}.{0[1]}'.format(sys.version_info))
pi = 3.1416
radio = 5
area= pi * radio**2
print(area)
color_list_1 = set(["White", "Black", "Red"])
color_list_2 = set(["Red", "Green"])
color_list_1 - color_list_2
path = 'C:/Users/Margarita/Documents/Mis_documentos/Biologia_... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: 2. Calcule el área de un circulo de radio 5
Step2: 3. Escriba código que imprima todos los colores de que están en color_list_1 y no estan pres... |
11,875 | <ASSISTANT_TASK:>
Python Code:
from itertools import accumulate, islice
def cubocta():
Classic Generator: Cuboctahedral / Icosahedral #s
https://oeis.org/A005901
yield 1 # nuclear ball
f = 1
while True:
elem = 10 * f * f + 2 # f for frequency
yield elem # <--- pause /... | <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: Oregon Curriculum Network <br />
Step3: Octet Truss
Step4: Each number in Pascal's Triangle may be understood as the number of unique pathways... |
11,876 | <ASSISTANT_TASK:>
Python Code:
y_sum = [0] * len(vol[0,:,0])
for i in range(len(vol[0,:,0])):
y_sum[i] = sum(sum(vol[:,i,:]))
ax = sns.barplot(x=range(len(y_sum)), y=y_sum, color="b")
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
from scipy.signal import argrelextrema
def local_minima(a):
return argr... | <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: Above, we see a histogram of y_sum that indicates that there is a local minimum at the 12th layer of y-sampling, which colocates with where we a... |
11,877 | <ASSISTANT_TASK:>
Python Code:
import math
import shutil
import numpy as np
import pandas as pd
import tensorflow as tf
print(tf.__version__)
tf.logging.set_verbosity(tf.logging.INFO)
pd.options.display.max_rows = 10
pd.options.display.float_format = '{:.1f}'.format
df = pd.read_csv("https://storage.googleapis.com/ml_... | <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: Next, we'll load our data set.
Step2: Examine the data
Step3: In this exercise, we'll be trying to predict median_house_value. It will be our ... |
11,878 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
import ipywidgets as widgets
from traitlets import Unicode, validate
class HelloWidget(widgets.DOMWidget):
_view_name = Unicode('HelloView').tag(sync=True)
_view_module = Unicode('hello').tag(sync=True)
%%javascript
define('hello', ["jupyter... | <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: Building a Custom Widget - Hello World
Step2: sync=True traitlets
Step3: Define the view
Step4: Render method
Step5: Test
Step6: Making the... |
11,879 | <ASSISTANT_TASK:>
Python Code:
import torch as T
import torch.autograd
import numpy as np
'''
Define a scalar variable, set requires_grad to be true to add it to backward path for computing gradients
It is actually very simple to use backward()
first define the computation graph, then call backward()
'''
x = T.randn(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: Simplicity of using backward()
Step2: The simple operations defined a forward path $z=(2x)^3$, $z$ will be the final output tensor we would lik... |
11,880 | <ASSISTANT_TASK:>
Python Code:
edges = set([(1, 2), (3, 1), (3, 2), (2, 4)])
edges = set([(1, 2), (3, 1), (3, 2), (2, 4)])
edges_list = [i[0] for i in edges] + [i[1] for i in edges]
nodes = set(edges_list)
edges_number = len(edges)
nodes_number = len(nodes)
print "Número de nodos: " + str(nodes_number)
print "Número de... | <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: Ejercicios Graphs, Paths & Components
Step6: Ejercicio - Matriz de Adyacencia
Step12: D## Ejercicio - Sparseness
Step20: En la matriz de adya... |
11,881 | <ASSISTANT_TASK:>
Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Joan Massich <mailsik@gmail.com>
#
# License: BSD Style.
import os.path as op
import mne
from mne.channels.montage import get_builtin_montages
from mne.datasets import fetch_fsaverage
from mne.viz import set_3d_title, ... | <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 all montages against a sphere
Step2: Check all montages against fsaverage
|
11,882 | <ASSISTANT_TASK:>
Python Code:
# Import everything that we are going to need... but not more
import pandas as pd
import xarray as xr
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap, cm
%matplotlib inline
DF=pd.DataFrame.from_items([('A', [1, 2, 3]), ('B', [4, 5, 6])], ... | <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 main advantages of using xarray versus plain netCDF4 are
Step2: ...or import local dataset
Step3: Extract variable from dataset
Step4: Ac... |
11,883 | <ASSISTANT_TASK:>
Python Code:
import scipy
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn.cross_validation as cv
# Extra plotting functionality
import visplots
from sklearn import preprocessing, metrics
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import 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: 2. Exploring and pre-processing data
Step2: At this point, you should try to explore the first few rows of the imported wine DataFrame using th... |
11,884 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import io
from scipy import integrate
string = '''
Time A
2017-12-18-19:54:40 -50187.0
2017-12-18-19:54:45 -60890.5
2017-12-18-19:54:50 -28258.5
2017-12-18-19:54:55 -8151.0
2017-12-18-19:55:00 -9108.5
2017-12-18-19:55:05 -12047.0
2017... | <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:
|
11,885 | <ASSISTANT_TASK:>
Python Code:
# Import necessary packages
import tensorflow as tf
import tqdm
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Import MNIST data so we have something for our experiments
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step6: Neural network classes for testing
Step9: There are quite a few comments in the code, so those should answer most of your questions. However, l... |
11,886 | <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(0,20,501))
b.run_compute(detach=True, model='my... | <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: Now we'll add datasets
St... |
11,887 | <ASSISTANT_TASK:>
Python Code:
%pylab inline --no-import-all
#plt.rc('text', usetex=True)
plt.rcParams['figure.figsize'] = (6.0, 6.0)
#plt.rcParams['savefig.dpi'] = 60
import george
from george.kernels import ExpSquaredKernel, My2ExpLEEKernel, MySignificanceKernel
from scipy.stats import chi2, norm
length_scale_of_corr... | <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: Now lets histogram the values of the random field.
Step3: Define the threshold for counting upcrossings
Step4: Check the code to count upcross... |
11,888 | <ASSISTANT_TASK:>
Python Code:
# handy graph library for python
import igraph
# science
import numpy as np
from collections import defaultdict
# plot things
import tabulate
import matplotlib.pyplot as plt
%matplotlib inline
# get some toy graph data so we can demonstrate these properties
network = igraph.Nexus.get("kap... | <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 quick graph vocabulary refresher
Step2: degree
Step3: Degree centrality
Step4: Eigenvector Centrality
Step5: One potential problem with ei... |
11,889 | <ASSISTANT_TASK:>
Python Code:
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
fig = plt.figure()
ax = Axes3D(fig)
x = [1,0,0]
y = [0,1,0]
z = [0,0,1]
verts = [zip(x, y,z)]
ax.add_collection3d(Poly3DCollection(verts, edgecolor="k", lw=5, alpha=0.4))
ax.text(1, 0, 0, "(1,0... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: 다음 함수는 생성된 점들을 2차원 삼각형 위에서 볼 수 있도록 그려주는 함수이다.
Step2: 만약 이 문제를 단순하게 생각하여 서로 독립인 0과 1사이의 유니폼 확률 변수를 3개 생성하고 이들의 합이 1이 되도록 크기를 정규화(normalize)하면 다음... |
11,890 | <ASSISTANT_TASK:>
Python Code:
# Setup feedback system
from learntools.core import binder
binder.bind(globals())
from learntools.computer_vision.ex2 import *
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
plt.rc('figure', autolayout=True)
plt.rc('axes', labelweight='bold', labelsize='large',... | <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: Apply Transformations
Step2: You can run this cell to see some standard kernels used in image processing.
Step3: 1) Define Kernel
Step4: Now ... |
11,891 | <ASSISTANT_TASK:>
Python Code:
from pyturb.gas_models import ThermoProperties
tp = ThermoProperties()
print(tp.species_list[850:875])
tp.is_available('Air')
from pyturb.gas_models import PerfectIdealGas
from pyturb.gas_models import SemiperfectIdealGas
# Air as perfect gas:
perfect_air = PerfectIdealGas('Air')
# Air 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: Import Perfect and Semiperfect Ideal Gas classes
Step2: To retrieve the thermodynamic properties you can print the thermo_prop from the gas
Ste... |
11,892 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
np.set_printoptions(suppress=True, precision=1)
fw = np.array([200,200,50,50,50,50,200,200])
f = np.array([fw,fw,fw,fw])
print(f)
F = np.fft.fft2(f)
print(F)
frestaurado = np.fft.ifft2(F)
print(frestaurado)
Faux = np.zeros_like(F)
Faux[0,0] = F[0,0]
print(Faux)
fr0 = np... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Aprendizados
Step2: Rotação
Step3: Processamento
Step4: Visualização
|
11,893 | <ASSISTANT_TASK:>
Python Code:
from sklearn.grid_search import GridSearchCV
from sklearn.svm import SVC
from sklearn.datasets import load_digits
from sklearn.cross_validation import train_test_split
digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target)
import numpy as n... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Define parameter grid
Step2: A GridSearchCV object behaves just like a normal classifier.
Step3: Exercises
|
11,894 | <ASSISTANT_TASK:>
Python Code:
import pixiedust
pixiedust.enableJobMonitor()
# @hidden_cell
# Enter your S3 access key (e.g. 'A....K')
s3_access_key = '...'
# Enter your S3 secret key (e.g. 'S....K')
s3_secret_key = '...'
# Enter your S3 bucket name (e.g. 'my-source-bucket')
s3_bucket = '...'
# Enter your csv file nam... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Configure Amazon S3 connectivity
Step2: Load CSV data
Step3: Explore the loaded data using PixieDust
|
11,895 | <ASSISTANT_TASK:>
Python Code:
import requests
from bs4 import BeautifulSoup
url = "http://www.theguardian.com/discussion/p/4fqc7"
r = requests.get(url)
html = r.text
soup = BeautifulSoup(html, "html.parser")
comments = soup.select(".d-comment__main")
comment_authors = soup.select(".d-comment__author")
print len (comme... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Extract the comments
Step2: Create comment stemmer and TFIDF vectorizer
Step3: Vectorize extracted comments
Step4: These are the vectorized c... |
11,896 | <ASSISTANT_TASK:>
Python Code:
k = 4
for n in range(2 * k):
print abs(n - k),
for n in range(2 * k):
print abs(n - (k - 1)),
for n in range(2 * k):
print abs(n - (k - 1)) + k,
def row_value(k, i):
i %= (2 * k) # wrap the index at the row boundary.
return abs(i - (k - 1)) + k
k = 5
for i in rang... | <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: Subtract $k$ from the index and take the absolute value
Step2: Not quite. Subtract $k - 1$ from the index and take the absolute value
Step3: G... |
11,897 | <ASSISTANT_TASK:>
Python Code:
# connect to PostgreSQL using psycopg2
# !pip install psycopg2-binary
import psycopg2
# Connect to an existing database and create the test table
with psycopg2.connect("dbname=yugabyte user=yugabyte host=localhost port=5433") as yb_conn:
cur = yb_conn.cursor()
# use this dro... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step2: Define the query to compute the histogram
Step3: Fetch the histogram data into a pandas dataframe
Step4: Histogram plotting
|
11,898 | <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
<END_TASK>
<USER_TASK:>
Description:
Step1: Model Inputs
Step2: Generator network
Step3: Discriminator
Step4: Hyperparameters
Step5: Build network
Step6: Discriminator and Generator L... |
11,899 | <ASSISTANT_TASK:>
Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)
import os.path as op
import numpy as np
from numpy.random import randn
from scipy import stats as stats
import mne
from mne import (io, spatia... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Set parameters
Step2: Read epochs for all channels, removing a bad one
Step3: Transform to source space
Step4: Transform to common cortical s... |
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