Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
|---|---|---|
11,000 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import pandas as pd
import seaborn as sns
raw_df = pd.read_csv("/home/brianb/Downloads/odsc_football_modeling_data_2.csv")
df_no_week_1 = raw_df[raw_df.week > 1]
ff_cols = raw_df.columns
sort(ff_cols.values)
raw_df[ff_cols].head()
df_no_week_1[ff_cols].head()
raw_df = pd... | <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. Meet the data
Step2: What columns do we have?
Step3: A quick look at our data
Step4: Where do we begin?
Step5: What did that just do?
Ste... |
11,001 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import GPyOpt
from numpy.random import seed
import numpy as np
seed(123)
func = GPyOpt.objective_examples.experimentsNd.alpine1(input_dim=9)
mixed_domain =[{'name': 'var1', 'type': 'continuous', 'domain': (-5,5),'dimensionality': 3},
{'name': 'var3', 'ty... | <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 use the Alpine1 function, that it is available in the benchmark of functions of the package. This function is defined for arbitrary dime... |
11,002 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cellpy
from cellpy import log
from cellpy import cellreader
from cellpy import prms
from cellpy import prmreader
from cellpy.utils import batch
# import holoviews as hv
%matplo... | <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: 4. Looking at the data
Step2: Cycles
Step3: Selecting specific cells and investigating them
Step4: Let's see how the smoothing (interpolation... |
11,003 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division
import numpy as np
import pandas as pd
from scipy import stats
from statsmodels.sandbox.stats.multicomp import multipletests
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
from itertools import combinations
from IPython.core.inter... | <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: Классификатор C4.5 и три его модификации
Step2: Сколько статистически значимых на уровне 0.05 различий мы обнаружили?
Step3: Сравнивая 4 класс... |
11,004 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
from sklearn import preprocessing
data = load_data()
df_out = pd.DataFrame(preprocessing.scale(data), index=data.index, columns=data.columns)
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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:
|
11,005 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
data = pd.read_csv("../data/GOOG.csv").ix[:,["Date", "Open"]]
data.sort_values(by="Date", inplace=True)
data.reset_index(inplace=True)
rate = np.zeros_like(data.Date)
n = 0
m = 0
for d in data.Date:... | <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: For the interest rate to be used, observing the charts of LIBOR rates, I will pick a linear model, starting from 2.5% at Jan-1-2005, going to 6%... |
11,006 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import networkx as nx
import community
import itertools
import matplotlib.pyplot as plt
import numpy as np
import re
%matplotlib inline
legacybib = pd.read_csv("ACMDL201612108240806.csv")
legacybib.columns
legacybib.head(3)
assert 0, sum(legacybib.id.duplicated())
... | <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 loading, sanitization and massage
Step2: The available data columns are
Step3: A peek at the topmost data items.
Step4: Does the id fiel... |
11,007 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
import numpy as np
def make_data(N, f=0.3, rseed=1):
rand = np.random.RandomState(rseed)
x = rand.randn(N)
x[int(f * N):] += 5
return x
x = make_data(1000)
hist = plt.hist(x, bins=30)
h... | <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: Motivating KDE
Step2: Let's plot this dataset.
Step3: Or, more properly
Step4: The normalization simply changes the scale on the y-axis
Step5... |
11,008 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
def soliton(x, t, c, a):
Return phi(x, t) for a soliton wave with constants c and a.
if type(x) == np.nda... | <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: Using interact for animation with data
Step3: To create an animation of a soliton propagating in time, we are going to precompute the soliton d... |
11,009 | <ASSISTANT_TASK:>
Python Code:
lc_data = pd.DataFrame.from_csv('./lc_dataframe(cleaning).csv')
lc_data = lc_data.reset_index()
lc_data.tail()
x = lc_data['grade']
sns.distplot(x, color = 'r')
plt.show()
x = lc_data['sub_grade']
sns.distplot(x, color = 'g')
plt.show()
x = lc_data['emp_title']
plt.hist(x)
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: V4 grade (범주형 데이터형)
Step2: V5 sub_grade (범주형 데이터형)
Step3: V6 emp_title (범주형 데이터형)
Step4: V7 emp_length (범주형 데이터형)
Step5: V8 home_ownership (... |
11,010 | <ASSISTANT_TASK:>
Python Code:
# Additional Resources 📚
import pickle
import faiss
def load_data():
with open('movies.pickle', 'rb') as f:
data = pickle.load(f)
return data
data = load_data()
vectors = data["vector"]
names = data["name"]
data
faiss.MatrixStats(vectors).comments.split("\n")
index = fa... | <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: Motivation 🎇
Step2: Strategies for Exact Nearest Neighbors 🧠
Step3: But It’s Not All Rainbows And Unicorns 🦄
Step4: Vector Encoding using ... |
11,011 | <ASSISTANT_TASK:>
Python Code:
%time "list(range(1000000)); print('ololo')"
def my_cool_function(a, b):
return a + b
def my_cool_function2(a: int, b: int) -> int:
return a + b
def my_cool_function(a, b):
return a + b
my_cool_function2("foo", "bar")
def main():
# here be dragons
return
if __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: Вопрос
Step2: Заготовка для типичного скрипта на Python
Step3: Модули
Step4: Обработка ошибок
Step5: Генерация списков (списковые включения)... |
11,012 | <ASSISTANT_TASK:>
Python Code:
# Imports
import math
import matplotlib.pyplot as plt
plt.style.use('seaborn-darkgrid')
%matplotlib inline
def logistic_algo(x, max_value, min_value=1.5, c=0.85, k=0.1):
Algorithm for scaling a given point's radius according to a Logistic Function.
phi = c * (10**(int(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:
Step2: While this procedure is written in JavaScript for use by D3.js during the simulation, an implementation in python is shown below for simplicity.... |
11,013 | <ASSISTANT_TASK:>
Python Code:
#|all_multicuda
#hide
from fastai.vision.all import *
from fastai.distributed import *
from fastai.vision.models.xresnet import *
from accelerate import notebook_launcher
from accelerate.utils import write_basic_config
#from accelerate.utils import write_basic_config
#write_basic_config... | <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: Overview
Step2: We need to setup Accelerate to use all of our GPUs. We can do so quickly with write_basic_config ()
Step3: Next let's download... |
11,014 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
a_true = 0.5
b_true = 2.0
c_true = -4.0
N = 30
xdata = np.linspace(-5, 5, N)
dy = 2
ydata = a_true*xdata**2 + b_true*xdata + c_true + np.random.normal(0.0, dy, size = N)
plt.figure(figsize... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Fitting a quadratic curve
Step2: First, generate a dataset using this model using these parameters and the following characteristics
Step3: No... |
11,015 | <ASSISTANT_TASK:>
Python Code:
# Import modules that contain functions we need
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
# Our data is the dichotomous key table and is defined as the word 'key'.
# key is set equal to the .csv file that is read by pandas.
# The .csv file 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:
Step 1 - Creating a Checkpoint
Step1: Pre-Questions
Step2: PART 1
Step3: Use and modify the section of code below to answer questions 3-5.
Step4: PA... |
11,016 | <ASSISTANT_TASK:>
Python Code:
import analysis3 as a3
reload(a3)
import time
def time_function(fun, *args):
start = time.time();
result = fun(*args);
run_time = time.time() - start;
minutes = run_time / 60;
print('RUN TIME: %f s (%f m)' % (run_time, minutes));
return result;
token = 's275_to_ar... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Timing individual functions
Step2: Testing pipeline as a whole
|
11,017 | <ASSISTANT_TASK:>
Python Code:
!ls ../scripts/hello-world*.py
!cat ../scripts/hello-world.py
!python scripts/hello-world.py
import math
import math
x = math.cos(2 * math.pi)
print(x)
from math import *
x = cos(2 * pi)
print(x)
from math import cos, pi
x = cos(2 * pi)
print(x)
import math
print(dir(math))
help(mat... | <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: Jupyter Notebooks
Step2: This includes the whole module and makes it available for use later in the program. For example, we can do
Step3: Alt... |
11,018 | <ASSISTANT_TASK:>
Python Code:
import os
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_filt-0-40_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file, verbose=False)
events_f... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Annotating bad spans of data
Step2: .. sidebar
Step3: Now we can confirm that the annotations are centered on the EOG events. Since
Step4: Se... |
11,019 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%load_ext metatab
%load_ext autoreload
%autoreload 2
%mt_lib_dir lib
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import metatab as mt
import seaborn as sns; sns.set(color_codes=True)
import sqlite3
import statsmodels 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: Source Data
Step2: Procedure
Step3: Then, we group the dataset by valueh_group and collect all of the income values for each group. These grou... |
11,020 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
np.__version__
author = 'kyubyong. longinglove@nate.com'
x = np.array([1, 2, 6, 4, 2, 3, 2])
out, indices = np.unique(x, return_inverse=True)
print "unique elements =", out
print "reconstruction indices =", indices
print "reconstructed =", out[indices]
x = np.array([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: Making proper sets
Step2: Boolean operations
Step3: Q3. Find the unique intersection of x and y.
Step4: Q4. Find the unique elements of x tha... |
11,021 | <ASSISTANT_TASK:>
Python Code:
import swat
conn = swat.CAS(host, port, username, password)
cls = conn.read_csv('https://raw.githubusercontent.com/sassoftware/sas-viya-programming/master/data/class.csv',
casout=dict(name='class', caslib='casuser'))
cls
out = conn.datastep.runcode('''
data bmi(ca... | <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 need to get some data into our session.
Step2: The datastep.runcode Action
Step3: We can pull the output table DataFrame out using the ... |
11,022 | <ASSISTANT_TASK:>
Python Code:
# Run some setup code
import numpy as np
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.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParam... | <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 data
Step2: Extract Features
Step3: Train SVM on features
Step4: Neural Network on image features
|
11,023 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'inpe', 'sandbox-2', 'aerosol')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "em... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
11,024 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import Image
import pandas
pandas.set_option('display.precision', 4)
pandas.read_csv('img/old-tagging-parts.csv').drop(['AUC, with untag', '$\Delta$ AUC, with untag'], axis=1)
pandas.set_option('display.precision', 4)
pandas.read_csv('img/old-tagging-parts-MC.csv').... | <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: Old tagging
Step2: MC
Step3: Taggers combination
Step4: Additional information
Step5: Check calibration of mistag
Step6: Check calibration ... |
11,025 | <ASSISTANT_TASK:>
Python Code:
Image(url="https://raw.githubusercontent.com/birdsarah/bokeh-miscellany/master/cut-off-tooltip.png", width=400, height=400)
from IPython.core.display import HTML
HTML(
<style>
div.output_subarea {
overflow-x: visible;
}
</style>
)
from bokeh.plotting import figure, ColumnDataSour... | <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: Unfortunately Bokeh can't solve this as Bokeh can't control the CSS of the parent element, which belongs to Jupyter. This can be solved in two w... |
11,026 | <ASSISTANT_TASK:>
Python Code:
urls = ['http://www.domain.com',
'https://somedomain.com',
'http://my-domain-123.net',
'https://google.com',
'http://www.foo.com',
'https://bar-baz3.com',
'ftp://domain2.com']
import re
# A complete match checking for the presence of some al... | <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. Get domains (without protocols) (including extension, e.g. .com) for URLs with both http and https protocols.
Step2: 3. Below is a list of l... |
11,027 | <ASSISTANT_TASK:>
Python Code:
import order as od
import scinum as sn
# campaign
c_2017 = od.Campaign("2017_13Tev_25ns", 1, ecm=13, bx=25)
# processes
p_data = od.Process("data", 1,
is_data=True,
label="data",
)
p_ttH = od.Process("ttH", 2,
label=r"$t\bar{t}H$",
xsecs={
13: sn.Number(0.5071, {"... | <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, Analysis-unrelated Setup
Step2: Task
Step3: Analysis Setup
Step4: <hr />
Step5: Task
Step6: <hr />
Step7: Task
Step8: <hr />
Ste... |
11,028 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
%matplotlib inline
import numpy as np
import pydotplus
from pandas.tools.plotting import scatter_matrix
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn import tree
from sklearn.externals.six import StringIO
from sklearn.cross_validation impor... | <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: Redo the model with a 75% - 25% training/test split and compare the results. Are they better or worse than before? Discuss why this may be.
Step... |
11,029 | <ASSISTANT_TASK:>
Python Code:
from pyproj import Geod
g = Geod(ellps='WGS84')
lat1,lon1 = (40.7143528, -74.0059731) # New York, NY
lat2,lon2 = (1.359, 103.989) # Delhi, India
az12,az21,dist = g.inv(lon1,lat1,lon2,lat2)
az12,az21,dist
# using geograhiclib:
# Compute path from 1 to 2
from geographiclib.geodesic 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: Set two location for which we want compute the measurments, in this example $P_1$
Step2: Note
Step3: Geodetic curve
Step4: Extract Latitude a... |
11,030 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
from PIL import Image
import numpy as np
from scipy.misc import imread, imresize
from imagenet_classes import class_names
import os
#File Path
# filepath_input = "./data/run/" #input csv file path
filepath_ckpt = "./ckpt/model_weight.ckpt" #weight saver check po... | <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: file_path
Step2: LSTM - Hyper Params
Step3: vgg16
Step4: load_vgg16
Step5: File Info
Step6: Text Reader
Step7: LSTM First Layer
Step8: ma... |
11,031 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from time import time
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.offsetbox import AnnotationBbox, OffsetImage
import os
from sklearn.datasets import fetch_mldata
# Fetch MNIST dataset and create a local copy.
if os.path.exists('mnist.npz'):
... | <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: Visualizing MNIST
Step2: Now it's your turn to plot some random representatives from each of 10 (obviously) available classes
Step3: The whole... |
11,032 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Constructing a Text Generation Model
Step2: Get the Dataset
Step3: First 10 Songs
Step4: Create Sequences and Labels
Step5: Train a Text Gen... |
11,033 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cnrm-cerfacs', 'sandbox-1', '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... |
11,034 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import scipy
from scipy import io
eeg = np.load("data/eeg_eyes_opened.npy")
num_trials, num_channels, num_samples = np.shape(eeg)
eeg_ts = np.squeeze(eeg[0, :, :])
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
from dyconnmap.fc import 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: Static connectivity
Step2: Define the frequency band we are interested to examine, in Hz
Step3: Define the sampling frequency, in Hz
Step5: W... |
11,035 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import fredpy as fp
import requests
import os
import matplotlib.pyplot as plt
plt.style.use('classic')
%matplotlib inline
url = 'https://www.philadelphiafed.org/-/media/frbp/assets/surveys-and-data/survey-of-professional-forecasters/historical-data/... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Download forecast data from SPF
Step2: Manage forecast data
Step3: Download and manage data from FRED
Step4: Prepare dataset and export
Step5... |
11,036 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as mpyplot
mpyplot.plot([1,2,3,4])
mpyplot.ylabel('some numbers')
mpyplot.show()
mpyplot.plot([1,2,3,4], [1,4,9,16])
import matplotlib.pyplot as mpyplot
mpyplot.plot([1,2,3,4], [1,4,9,16], 'ro')
mpyplot.axis([0, 6, 0, 20])
mpyplot.show()
seq = 'ATGGTGCATCTGACTC... | <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() is a versatile command, and will take an arbitrary number of arguments. For example, to plot x versus y, you can issue the command
Step2:... |
11,037 | <ASSISTANT_TASK:>
Python Code:
# print something
print(c)
# what is hello_str
# reverse indexing
# stepwise indexing (start:stop:step)
my_dict = {'one':1, 'two':2, 'three':3}
print(my_dict['one'])
# how to test if certain key is in dict
print('one' in my_dict)
print('four' in my_dict)
### example immutable
x = 10
y... | <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: Numerical Data Types
Step2: Sequence Data Types
Step3: Tuple
Step4: Boolean
Step5: <a id='flowcontrol'/>
Step9: you see
Step10: Creating y... |
11,038 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Title
Step2: Resources
Step3: Run the model on a single batch of data, and inspect the output
Step4: Compile the model for training
|
11,039 | <ASSISTANT_TASK:>
Python Code:
export CHGDISABLE=1
~/hg/hg debugpython << 'EOS'
from timeit import timeit
from bindings import tracing
def nop(): pass
@tracing.wrapfunc
def wrap(): pass
@tracing.wrapfunc
@tracing.meta(lambda: [("color", "blue")])
def wrap_meta(): pass
def bindings(tracer=tracing.singleton):
id = tr... | <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: Trace-Them-All as a Profiler
Step2: The ASCII Format
Step3: Annotated metadata (like perftrace)
Step4: Call counts (like traceprof)
Step5: T... |
11,040 | <ASSISTANT_TASK:>
Python Code:
# Load pickled data
import pickle
from keras.datasets import cifar10
from sklearn.model_selection import train_test_split
# TODO: Fill this in based on where you saved the training and testing data
#training_file = "traffic-signs-data/train.p"
#validation_file = "traffic-signs-data/valid.... | <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: Step 1
Step2: Include an exploratory visualization of the dataset
Step3: Step 2
Step4: Model Architecture
Step5: Train, Validate and Test th... |
11,041 | <ASSISTANT_TASK:>
Python Code:
import keras
# Constants
Num_Classes = Num_Classes
batch_size = 4
lr = 0.01
# Helper Functions
# get_batches(..) copied from utils.py
# gen.flow_from_directory() is an iterator that yields batches of images
# from a directory indefinitely.
from keras.preprocessing import image
def get_bat... | <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. Basic Linear Model
Step2: 2. 1-Layer Neural Network
|
11,042 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'sandbox-2', 'atmos')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributo... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
11,043 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import bayesloop as bl
import sympy.stats as stats
from tqdm import tqdm_notebook
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_color_codes() # use seaborn colors
# minute-scale pricing data
prices = np.array(
[ 221.14 , 221.09 ... | <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: Persistent random walk model
Step2: Online study
Step3: <div style="background-color
Step4: Before any data points are passed to the study in... |
11,044 | <ASSISTANT_TASK:>
Python Code:
from nltk.grammar import DependencyGrammar
from nltk.parse import (
DependencyGraph,
ProjectiveDependencyParser,
NonprojectiveDependencyParser,
)
treebank_data = Pierre NNP 2 NMOD
Vinken NNP 8 SUB
, , 2 P
61 CD 5 NMOD
ye... | <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: We can load different Dependency Grammar parsers from NLTK
Step3: Dependency Version of the Penn Treebank
Step5: "Using the output of zpar (li... |
11,045 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
class DLProgress(tqdm):
last_b... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Image Classification
Step2: Explore the Data
Step5: Implement Preprocess Functions
Step8: One-hot encode
Step10: Randomize Data
Step12: Che... |
11,046 | <ASSISTANT_TASK:>
Python Code:
import random
import numpy as np
from cs231n.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
from __future__ import print_function
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Load data
Step2: Extract Features
Step3: Train SVM on features
Step4: Inline question 1
|
11,047 | <ASSISTANT_TASK:>
Python Code:
from keras.datasets import mnist
# use Keras to import pre-shuffled MNIST database
(X_train, y_train), (X_test, y_test) = mnist.load_data()
print("The MNIST database has a training set of %d examples." % len(X_train))
print("The MNIST database has a test set of %d examples." % len(X_test)... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 2. Visualize the First Six Training Images
Step2: 3. View an Image in More Detail
Step3: 4. Rescale the Images by Dividing Every Pixel in Ever... |
11,048 | <ASSISTANT_TASK:>
Python Code:
friends = ['John', 'Bob', 'Mary']
stuff_to_pack = ['socks','shirt','toothbrush']
print(friends)
print(stuff_to_pack)
#list of integers
print([1, 24, 76])
#list of strings
print(['red', 'yellow', 'blue'])
#mixed list
print(['red', 24, 98.6])
#list with a list included
print([1, [5, 6], 7]... | <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: Square brackets surround lists, and commas separate the elements in the list
Step2: Please note that there are two ways of creating an empty li... |
11,049 | <ASSISTANT_TASK:>
Python Code:
import SimpleITK as sitk
import numpy as np
%run update_path_to_download_script
from downloaddata import fetch_data as fdata
%matplotlib inline
import matplotlib.pyplot as plt
import gui
from ipywidgets import interact, fixed
def display_with_overlay(
segmentation_number, slice_numbe... | <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: Utility method for display
Step3: Fetch the data
Step4: Derive a reference
Step5: Evaluate segmentations using the reference
Step6: Improved... |
11,050 | <ASSISTANT_TASK:>
Python Code:
import datetime
import json
import os
import pprint
import random
import string
import sys
import tensorflow as tf
assert 'COLAB_TPU_ADDR' in os.environ, 'ERROR: Not connected to a TPU runtime; please see the first cell in this notebook for instructions!'
TPU_ADDRESS = 'grpc://' + os.envi... | <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: Secondly, prepare and import BERT modules.
Step2: Thirdly, prepare for training
Step3: Now, let's play!
|
11,051 | <ASSISTANT_TASK:>
Python Code:
regimentNames = ['Night Riflemen', 'Jungle Scouts', 'The Dragoons', 'Midnight Revengence', 'Wily Warriors']
# create a variable for the for loop results
regimentNamesCapitalized_f = []
# for every item in regimentNames
for i in regimentNames:
# capitalize the item and add it to regim... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Using A For Loop
Step2: Using Map()
Step3: Map the capitalizer function to regimentNames, convert the map into a list, and view the variable
S... |
11,052 | <ASSISTANT_TASK:>
Python Code:
%%capture
!pip install --upgrade trax
import trax
from trax import layers as tl
from trax.supervised import training
# Trax offers the WideResnet architecture in it's models module
from trax.models.resnet import WideResnet
trax.fastmath.set_backend('tensorflow-numpy')
%%capture
train_st... | <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: Introduction
Step2: Downloading Dataset
Step3: Batch Generator
Step4: Model Architecture
Step5: When we have our model and the data, we use ... |
11,053 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
import numpy as np
from exact_solvers import euler
from utils import riemann_tools as rt
from ipywidgets import interact
from ipywidgets import widgets
State = euler.Primitive_State
def roe_averages(q_l, q_r, gamma=1.4):
r... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Approximate solvers for the Euler equations of gas dynamics
Step2: An implementation of this solver for use in Clawpack can be found here. Rec... |
11,054 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('ggplot')
data = pd.Series(np.random.rand(100))
actual_mean = data.mean()
print('{:.3f}'.format(actual_mean))
def calc_sample_means(data, n):
Make n bootstrap samples from data an... | <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 examples, let's say we have an array of values and their mean.
Step3: How stable is this measure? To answer this, we'll sample with replace... |
11,055 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import os
import tarfile as TAR
import sys
from datetime import datetime
from PIL import Image
import warnings
import json
import pickle
import zipfile
from math import *
import numpy as np
import pandas as pd
from sklearn.cluster import MiniBatchKMeans
import matplotli... | <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: Most likely, the ImageHash library will be missing in a typical setup. The following cell, installs the library.
Step2: Feature Extraction
Step... |
11,056 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('ggplot')
%matplotlib inline
data = pd.read_csv('american_presidents.csv', header=0, index_col=None)
data
data.describe()
data.plot(x='order',y='height_cm', color='blue')
data.plot('order', kind='hist', 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:
Step2: The standard bootstrap method
Step5: The Bayesian bootstrap (with a Dirichlet prior)
Step6: Test both the weighted statistic method and the we... |
11,057 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Introduction to tensor slicing
Step2: Extract tensor slices
Step3: Alternatively, you can use a more Pythonic syntax. Note that tensor slices ... |
11,058 | <ASSISTANT_TASK:>
Python Code:
# Generative model
mu_x = 10.0
sigma_x = 2.0
x_s = edm.Normal(mu_x, sigma_x)
# Sample data produced by model
n_samples = 100
samples = np.zeros(n_samples)
with tf.Session() as sess:
for i in range(n_samples):
samples[i] = sess.run(x_s)
# Descriptive statistics
print('Mean: {}'... | <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: Point estimate of model parameters
Step2: Posterior estimate of model parameters
|
11,059 | <ASSISTANT_TASK:>
Python Code:
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.forward import make_forward_dipole
from mne.evoked import combine_evoked
from mne.simulation import simulate_evoked
from nilearn.plotting import plot_anat
from nilearn.datasets import load_mni152_t... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Let's localize the N100m (using MEG only)
Step2: We can also plot the result using outlines of the head and brain.
Step3: Plot the result in 3... |
11,060 | <ASSISTANT_TASK:>
Python Code:
# Authors: Denis Engemann <denis.engemann@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne.preprocessing import ICA
from mne.preprocessing import create_ecg_epochs, create_eog_epochs
from ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Setup paths and prepare raw data.
Step2: 1) Fit ICA model using the FastICA algorithm.
Step3: 2) identify bad components by analyzing latent s... |
11,061 | <ASSISTANT_TASK:>
Python Code:
# FUSEDWind imports
from fusedwind.plant_flow.vt import GenericWindFarmTurbineLayout, WTPC, WeibullWindRoseVT, GenericWindRoseVT
# Topfarm lib imports
from topfarm.aep import AEP
from topfarm.layout_distribution import spiral, DistributeSpiral, DistributeXY, DistributeFilledPolygon
from t... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Loading all the input data
Step2: Plotting the inputs
Step3: Plotting the depth
Step4: The red points indicate the position of the baseline t... |
11,062 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from scipy import constants
import pygmsh
from MeshedFields import *
with pygmsh.geo.Geometry() as geom:
Lx = 0.215
Ly = 0.150
Ri = 0.002
lca = 0.005
lci = 0.001
stretch = 50.0
p1 = geom.add_point([Lx/2.0*stretch, Ly/2.0], lca)
p2 =... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Create a meshed screen with a central hole
Step2: The z-position of all mesh points is computed to lay on a toroid with 1.625 m focal length.<b... |
11,063 | <ASSISTANT_TASK:>
Python Code:
import time
import numpy as np
import tensorflow as tf
import utils
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import zipfile
dataset_folder_path = 'data'
dataset_filename = 'text8.zip'
dataset_name = 'Text8 Dataset'
class DLProgress(tq... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Load the text8 dataset, a file of cleaned up Wikipedia articles from Matt Mahoney. The next cell will download the data set to the data folder. ... |
11,064 | <ASSISTANT_TASK:>
Python Code:
x = True
print(x)
print(type(x))
def can_run_for_president(age):
Can someone of the given age run for president in the US?
# The US Constitution says you must be at least 35 years old
return age >= 35
print("Can a 19-year-old run for president?", can_run_for_president(19))
pr... | <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: Rather than putting True or False directly in our code, we usually get boolean values from boolean operators. These are operators that answer y... |
11,065 | <ASSISTANT_TASK:>
Python Code:
import numpy
import nibabel
import os
import nilearn.plotting
import matplotlib.pyplot as plt
from statsmodels.regression.linear_model import OLS
import nipype.interfaces.fsl as fsl
import scipy.stats
if not 'FSLDIR' in os.environ.keys():
raise Exception('This notebook requires that F... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Set up default parameters. We use 28 subjects, which is the median sample size of the set of fMRI studies published in 2015 that were estimated... |
11,066 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
def tokenize(s, stop_words=None, punctuation='`~!@#$%^&*()_-+={[}]|\:;"<,>.?/}\t'):
Split a string into a list of words, removing punctuation and stop words.
all_words= []
for line in s.splitlines():
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step3: Word counting
Step5: Write a function count_words that takes a list of words and returns a dictionary where the keys in the dictionary are the ... |
11,067 | <ASSISTANT_TASK:>
Python Code::
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, max_error, explained_variance_score, mean_absolute_percentage_error
# initialise & fit Decision Tree Regressor
model = DecisionTreeRegressor(criterion='squared_error',
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
11,068 | <ASSISTANT_TASK:>
Python Code:
a = pk.load(open("slide14.pkl","rb"), encoding='latin1')
print("DATA = ", a)
print("ONE ROW = ", a[0])
plt.rcParams['figure.figsize'] = [6,4]
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 14
plt.rcParams['ytick.labelsize'] = 14
plt.rcParams['legend.fontsize'] = 12... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Visualizing the data
|
11,069 | <ASSISTANT_TASK:>
Python Code:
def function_plot(ω0=1, ω1=1):
# Define x axis range
x = np.linspace(-4*np.pi, 4*np.pi, 100)
# Add labels to x and y axis
plt.xlabel('$x$')
plt.ylabel('$\exp(x/10) \cdot \sin(\omega_{1}x) \cdot \cos(\omega_{0}x)$')
# Limit x axis between start and end point of the ... | <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 with sliders for $\omega_0$ and $\omega_1$ from 0 to 2 with steps of 0.25
Step2: Exercise 08.2 (multiple function plotting)
Step3: Plot o... |
11,070 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
# Show matplotlib graphs inside the notebook.
%matplotlib inline
import os.path
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import plotly
import plotly.offline as py
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
import 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: 2.1 Importing the data
Step2: 2.2 Looking at the data
Step3: Export part of the dataset as HTML files for inspection ByCity, ByCountry,
Step... |
11,071 | <ASSISTANT_TASK:>
Python Code:
# Import necessary packages
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import torch
import helper
import matplotlib.pyplot as plt
### Run this cell
from torchvision import datasets, transforms
# Define a transform to normalize the data
transform ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Now we're going to build a larger network that can solve a (formerly) difficult problem, identifying text in an image. Here we'll use the MNIST ... |
11,072 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
fakedata1 = pd.DataFrame(
[[ 0.862, 2.264],
[ 0.694, 1.847],
[ 0.184, 0.705],
[ 0.41 , 1.246]], columns=['input','output'])
fakedata1.plot(x='input',y='output',kind='scatter')
from sklearn.model_selection import train_test_split
faketr... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: It is pretty clear that there is a linear trend here. If I wanted to predict what would happen if we tried the input of x=0.6, it would be a goo... |
11,073 | <ASSISTANT_TASK:>
Python Code:
!pip install dm-acme
!pip install dm-acme[reverb]
!pip install dm-acme[tf]
!pip install dm-sonnet
!git clone https://github.com/deepmind/deepmind-research.git
%cd deepmind-research
#@title Edit and run
mjkey =
REPLACE THIS LINE WITH YOUR MUJOCO LICENSE KEY
.strip()
mujoco_dir = "$HOME/.... | <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: dm_control
Step4: Machine-locked MuJoCo license.
Step5: Imports
Step6: Data
Step7: Dataset and environment
Step8: D4PG learner
Step9: Trai... |
11,074 | <ASSISTANT_TASK:>
Python Code:
# Load our dependencies
import pybrl as brl
filename = "lorem_ipsum.pdf" # of course :P
pdf_password = None
language = 'english'
# Let's translate the PDF file.
translated = brl.translatePDF(filename, password = pdf_password, language = language) # Easy, right?
# Let's explore what 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: The translatePDF method does the following
|
11,075 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
np.random.seed(123456)
# Generate 100 random patterns for class1
mu_vec1 = np.array([[0],[0]])
cov_mat1 = np.array([[3,0],[0,3]])
x1_samples = np.random.multivariate_normal(mu_vec1.ravel(), cov_mat1, 100)
# Generate 100 random patterns for class2
mu_vec2 = np.array([[9]... | <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: <br>
Step3: <br>
Step5: <br>
Step6: <br>
Step7: <br>
Step8: <br>
Step10: <br>
Step11: <br>
|
11,076 | <ASSISTANT_TASK:>
Python Code:
import subprocess
def run_command(command):
Run bash command and return the result
:param str command: String representation of bash command
:return: Returns a generator of output of the result of running bash command in bytes
:rtype: iter
command = command.... | <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: Then we define two util functions.
Step4: Second one is for writing string in the file.
Step5: Than we define some constants for future use.
S... |
11,077 | <ASSISTANT_TASK:>
Python Code:
# Uncomment these to install fedjax.
# !pip install fedjax
# !pip install --upgrade git+https://github.com/google/fedjax.git
# !pip install tensorflow_datasets
import functools
import itertools
import fedjax
import numpy as np
# We cap max sentence length to 8.
train_fd, test_fd = fedjax... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: What are datasets in federated learning?
Step2: fedjax.FederatedData provides methods for accessing metadata about the federated dataset, like ... |
11,078 | <ASSISTANT_TASK:>
Python Code:
# As usual, a bit of setup
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.cnn import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient
... | <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: Convolutional Networks
Step2: Convolution
Step4: Aside
Step5: Convolution
Step6: Max pooling
Step7: Max pooling
Step8: Fast layers
Step9: ... |
11,079 | <ASSISTANT_TASK:>
Python Code:
from astwro.sampledata import fits_image
frame = fits_image()
from astwro.pydaophot import Daophot, Allstar
dp = Daophot(image=frame)
al = Allstar(dir=dp.dir)
res = dp.FInd(frames_av=1, frames_sum=1)
print ("{} pixels analysed, sky estimate {}, {} stars found.".format(res.pixels, res.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: Daophot object creates temporary working directory (runner directory), which is passed to Allstar constructor to share.
Step2: Daophot got FITS... |
11,080 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
%pylab inline
# Load the data as usual (here the code for Python 2.7)
X = np.loadtxt('data/small_Endometrium_Uterus.csv', delimiter=',', skiprows=1, usecols=range(1, 3001))
y = np.loadtxt('data/small_Endometrium_Uterus.csv', delimiter=',', skiprows=1, usecols=[3001],
... | <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: 2016-10-07
Step2: 1. L1-Regularized Logistic Regression
Step3: Question Compute the cross-validated predictions of the l1-regularized logistic... |
11,081 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
df = pd.read_csv('olympics.csv', index_col=0, skiprows=1)
for col in df.columns:
if col[:2]=='01':
df.rename(columns={col:'Gold'+col[4:]}, inplace=True)
if col[:2]=='02':
df.rename(columns={col:'Silver'+col[4:]}, inplace=True)
if col[:2]=='0... | <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: Question 0 (Example)
Step2: Question 1
Step3: Question 2
Step4: Question 3
Step5: Question 4
Step6: Part 2
Step7: Question 6
Step8: Quest... |
11,082 | <ASSISTANT_TASK:>
Python Code:
units = 64
embedding_dim = 64
loss = 'binary_crossentropy'
def create_model(batch_size=None):
x = x_in = Input(shape=(maxlen,), batch_size=batch_size, dtype=tf.int32)
x = Embedding(input_dim=max_features, output_dim=embedding_dim)(x)
x = Activation('linear', name='embedding_act')(x)... | <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: Replacing with quantized layers
Step2: Converting a Model Automatically
Step3: Quantizing a Model With AutoQKeras
|
11,083 | <ASSISTANT_TASK:>
Python Code:
import mne
from mne.datasets import sample
from mne.preprocessing import create_ecg_epochs, create_eog_epochs
# getting some data ready
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
raw = mne.io.read_raw_fif(raw_fname, preload=True)
(raw.copy(... | <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: Low frequency drifts and line noise
Step2: we see high amplitude undulations in low frequencies, spanning across tens of
Step3: On MEG sensors... |
11,084 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import ctypes
from ctypes import *
import pycuda.gpuarray as gpuarray
import pycuda.driver as cuda
import pycuda.autoinit
from pycuda.compiler import SourceModule
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import math
import time
%matplotlib inline ... | <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 FFT routines
Step2: Initializing Data
Step3: $W$ TRANSFORM FROM AXES-0
Step4: Forward Transform
Step5: Inverse Transform
Step6: $W$... |
11,085 | <ASSISTANT_TASK:>
Python Code:
# Import relevant modules
%matplotlib inline
%load_ext autoreload
%autoreload 2
import numpy as np
from NPTFit import nptfit # module for performing scan
from NPTFit import create_mask as cm # module for creating the mask
from NPTFit import dnds_analysis # module for analysing the output
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Step 1
Step2: Step 2
Step3: This time we add a non-Poissonian template correlated with the Galactic Center Excess and also one spatially distr... |
11,086 | <ASSISTANT_TASK:>
Python Code:
import jax
import jax.numpy as jnp
import jax.scipy.stats as stats
import matplotlib.pyplot as plt
import numpy as np
import blackjax
%load_ext watermark
%watermark -d -m -v -p jax,jaxlib,blackjax
jax.devices()
loc, scale = 10, 20
observed = np.random.normal(loc, scale, size=1_000)
def l... | <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: The problem
Step3: HMC
Step4: Set the initial state
Step5: Build the kernel and inference loop
Step6: BlackJAX does not provide a default in... |
11,087 | <ASSISTANT_TASK:>
Python Code:
gabr_tweets = extract_users_tweets("gabr_ibrahim", 2000)
gabr_dict = dict()
gabr_dict['gabr_ibrahim'] = {"content" : [], "hashtags" : [], "retweet_count": [], "favorite_count": []}
for tweet in gabr_tweets:
text = extract_text(tweet)
hashtags = extract_hashtags(tweet)
rts = t... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Step 2
Step2: Step 3
Step3: Step 4
Step4: Let's now proceed to tokenize these tweets in addition to lemmatizing them! This will help improve ... |
11,088 | <ASSISTANT_TASK:>
Python Code:
import tangent
import tensorflow as tf
def f(x):
a = x * x
b = x * a
c = a + b
return c
df = tangent.grad(f)
df
df(33)
forward_df = tangent.grad(f, mode='forward')
forward_df(33, dx=1)
forward_df(33, dx=2)
forward_df(33, dx=-1)
def f(x):
a = x * x * x
b = a * x *... | <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: Forward mode
Step2: Hessian-vector products
|
11,089 | <ASSISTANT_TASK:>
Python Code:
%load_ext sql
%config SqlMagic.connect_args="{'kerberos_service_name':'impala', 'auth_mechanism':'GSSAPI'}"
%sql impala://impalasrv-prod:21050/test2
%%sql
select * from emp
Employee_name="SCOTT"
%sql select * from emp where ename=:Employee_name
myResultSet=%sql select ename "Employee... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 2. Connect to the target database
Step2: 3. Run SQL on the target using the %%sql cell magic or %sql line magic
Step3: Bind variables
Step4: ... |
11,090 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
from dh_py_access import package_api
import dh_py_access.lib.datahub as datahub
import xarray as xr
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from po_data_process import comparison_bar_chart, make_comparison_plot
import ... | <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: <font color='red'>Please put your datahub API key into a file called APIKEY and place it to the notebook folder or assign your API key directly ... |
11,091 | <ASSISTANT_TASK:>
Python Code:
%env CLIENT bitrepository-client-1.9-RC1
!wget -Nq "https://sbforge.org/download/attachments/25395346/${CLIENT}.zip"
!unzip -quo ${CLIENT}.zip
%alias bitmag ${CLIENT}/bin/bitmag.sh %l
%env TESTFILE1 README.md
%bitmag put-file -c integrationtest1 -f $TESTFILE1 -i $TESTFILE1
!md5sum $TEST... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Describes the tests needed to validate the GetChecksums functionality.
Step2: Basic getChecksum
Step3: Request the checksum of a file in the c... |
11,092 | <ASSISTANT_TASK:>
Python Code:
import os
import tensorflow as tf
import numpy as np
from google.cloud import bigquery
PROJECT = 'cloud-training-demos' # REPLACE WITH YOUR PROJECT ID
BUCKET = 'cloud-training-demos-ml' # REPLACE WITH YOUR BUCKET NAME
REGION = 'us-central1' # REPLACE WITH YOUR BUCKET REGION e.g. us-centra... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: We will use this helper funciton to write lists containing article ids, categories, and authors for each article in our database to local file.
... |
11,093 | <ASSISTANT_TASK:>
Python Code:
DISK_LENGTH = 272
def invert(string):
return ''.join(('1' if x == '0' else '0' for x in string))
print(invert('111100001010'))
def fill_disk(string):
while(len(string) < DISK_LENGTH):
string = string + '0' + invert(string[::-1])
return string[:DISK_LENGTH]
with open('.... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Part Two
|
11,094 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
ser = pd.Series([100, 'foo', 300, 'bar', 500], ['tom', 'bob', 'nancy', 'dan', 'eric'])
ser
ser.index
ser.loc[['nancy','bob']]
ser[[4, 3, 1]]
ser.iloc[2]
'bob' in ser
ser
ser * 2
ser[['nancy', 'eric']] ** 2
d = {'one' : pd.Series([100., 200., 300.], index=['apple', 'b... | <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: <p style="font-family
Step2: <p style="font-family
Step3: <p style="font-family
Step4: <p style="font-family
Step5: <p style="font-family
St... |
11,095 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
from tensorflow import keras
import numpy as np
class LogisticEndpoint(keras.layers.Layer):
def __init__(self, name=None):
super(LogisticEndpoint, self).__init__(name=name)
self.loss_fn = keras.losses.BinaryCrossentropy(from_logits=True)
... | <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: Usage of endpoint layers in the Functional API
Step2: Exporting an inference-only model
Step3: Usage of loss endpoint layers in subclassed mod... |
11,096 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Create and convert a TensorFlow model
Step2: Generate data
Step3: Add some noise
Step4: Split our data
Step5: Design a model
Step6: Train t... |
11,097 | <ASSISTANT_TASK:>
Python Code:
from matplotlib import pyplot as plt
%matplotlib inline
import artm
batch_vectorizer = artm.BatchVectorizer(data_path="school.txt", data_format="vowpal_wabbit", target_folder="school_batches",
batch_size=100)
batch_vectorizer = artm.BatchVectorize... | <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: Первое считывание данных (преобразуем удобный для человека формат в формат, который использует модель)
Step2: В следующий раз данные можно счит... |
11,098 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
# import plotting tools
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import matplotlib as mpl
# import numpy
import numpy as np
# import necessary landla... | <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: Topographic grid
Step2: Initalizing and running a FlowDirector
Step3: The FlowDirectors are method specific, so if you want to do D8 flow dire... |
11,099 | <ASSISTANT_TASK:>
Python Code:
# @title Install
!pip install --upgrade --no-cache-dir recsim
# @title Imports
# Generic imports
import functools
from gym import spaces
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
# RecSim imports
from recsim import agent
from recsim import document
from re... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Since we're not about to do anything fancy with this environment, we will initialize it with the provided create_environment function (further d... |
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