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15,900 | <SYSTEM_TASK:>
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Description:
CSAL4243
Step1: Feature Scaling and Mean Normalization
Step2: Initialize Hyper Parameters
Step3: Model/Hypothesis Function
Step5: Cost Function
Ste... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
from sklearn import linear_model
import matplotlib.pyplot as plt
import matplotlib as mpl
# read data in pandas frame
dataframe = pd.read_csv('datasets/house_dataset2.csv', encoding='utf-8')
# check data by printing first few rows
... |
15,901 | <SYSTEM_TASK:>
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Description:
chart module
This module relies on pygal library, so the returned charts are instances of pygal.chart. See options at
pygal site
I made a JavaScript '... | <ASSISTANT_TASK:>
Python Code:
import ee
from geetools import ui
test_site = ee.Geometry.Point([-71, -42])
test_feat = ee.Feature(test_site, {'name': 'test feature'})
test_featcol = ee.FeatureCollection([
test_feat,
test_feat.buffer(100).set('name', 'buffer 100'),
test_feat.buffer(1000).set('name', 'buffer... |
15,902 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Truncated potentials for long-range electrostatics
Björn Stenqvist and Mikael Lund, Div. of Theoretical Chemistry, Lund University, Sweden, 2016
In thi... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from simtk.openmm.app import *
from simtk.openmm import *
from simtk.unit import *
from sys import stdout
import matplotlib, matplotlib.pyplot as plt
import pandas as pd
from io import StringIO
import numpy as np, os
matplotlib.rcParams.update({'font.size': 12})
# simul... |
15,903 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
A simple method for segmenting rectilinear crop plots
The following outlines the method in https
Step1: Read in a test image subset. Replace with your... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import cv2
from geospatial_learn import raster
from geospatial_learn.utilities import do_phasecong, houghseg
from math import ceil
import matplotlib.pyplot as plt
from skimage.color import rgb2gray, label2rgb
from skimage.feature import canny
from skimage.exposure impo... |
15,904 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Basic notebook to look @ convergence of a 2D region in an FES. It will actually call sum hills with the stride you set in cell one , graph the FES and ... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import glob
import os
from matplotlib.patches import Rectangle
# define all variables for convergence script
# these will pass to the bash magic below used to call plumed sum_hills
dir="MetaD_converge" #where the intermediate fes will be... |
15,905 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Batch Normalization – Lesson
What is it?
What are it's benefits?
How do we add it to a network?
Let's see it work!
What are you hiding?
What is Batch N... | <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("... |
15,906 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Training DeepMind's Atari DQN with Chimp
Load Chimp modules
Step1: Load Python packages
Step2: Set training parameters
Step3: You may want to set a ... | <ASSISTANT_TASK:>
Python Code:
from chimp.memories import ReplayMemoryHDF5
from chimp.learners.dqn_learner import DQNLearner
from chimp.learners.chainer_backend import ChainerBackend
from chimp.simulators.atari import AtariSimulator
from chimp.agents import DQNAgent
Explanation: Training DeepMind's Atari DQN with Chimp... |
15,907 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Copyright 2020 Google LLC.
Licensed under the Apache License, Version 2.0 (the 'License');
you may not use this file except in compliance with the Lice... | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import random
import sys
from sklearn import model_selection
import tensorflow as tf
!pip install git+https://github.com/google-research/tensorflow_constrained_optimization
import tensorflow_constrained_optimization as... |
15,908 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Whitening evoked data with a noise covariance
Evoked data are loaded and then whitened using a given noise covariance
matrix. It's an excellent quality... | <ASSISTANT_TASK:>
Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Denis A. Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne import io
from mne.datasets import sample
from mne.cov import compute_covariance
print(__doc__)
Explanation: Whit... |
15,909 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
← Back to Index
NumPy and SciPy
The quartet of NumPy, SciPy, Matplotlib, and IPython is a popular combination in the Python world. We will use eac... | <ASSISTANT_TASK:>
Python Code:
print numpy.arange(5)
print numpy.linspace(0, 5, 10, endpoint=False)
print numpy.zeros(5)
print numpy.ones(5)
print numpy.ones((5,2))
print scipy.randn(5) # random Gaussian, zero-mean unit-variance
print scipy.randn(5,2)
Explanation: ← Back to Index
NumPy and SciPy
The quartet of Num... |
15,910 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Deep Convolutional Neural Network in TensorFlow
In this notebook, we convert our LeNet-5-inspired, MNIST-classifying, deep convolutional network from K... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
np.random.seed(42)
import tensorflow as tf
tf.set_random_seed(42)
Explanation: Deep Convolutional Neural Network in TensorFlow
In this notebook, we convert our LeNet-5-inspired, MNIST-classifying, deep convolutional network from Keras to TensorFlow (compare them side by... |
15,911 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Sparse CNN
The idea of the sparse CNN is to utilize only the important values of its filters and set those less impotant to zero.
For terminology, we'l... | <ASSISTANT_TASK:>
Python Code:
c_out = 7
c_in = 8
conv = torch.nn.Conv2d(c_in, c_out, kernel_size=(2, 2), stride=(1, 1), padding=0, dilation=1, groups=1)
input_tensor = torch.randn(2, c_in, 5, 3)
output_tensor = conv(input_tensor)
# Get params of conv layer.
in_channels = conv.in_channels # Number of channels in the i... |
15,912 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Compute ICA components on epochs
ICA is fit to MEG raw data.
We assume that the non-stationary EOG artifacts have already been removed.
The sources mat... | <ASSISTANT_TASK:>
Python Code:
# Authors: Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne.preprocessing import ICA, create_ecg_epochs
from mne.datasets import sample
print(__doc__)
Explanation: Compute ICA components on epochs
ICA is fit to MEG raw data.
We assume that the non-... |
15,913 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Class Session 4 Exercise
Step1: Now, define a function that returns the index numbers of the neighbors of a vertex i, when the
graph is stored in adj... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import igraph
import timeit
import itertools
Explanation: Class Session 4 Exercise:
Comparing asymptotic running time for enumerating neighbors of all vertices in a graph
We will measure the running time for enumerating the neighbor vertices for three different data str... |
15,914 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Recommender Engine
Perhaps the most famous example of a recommender engine in the Data Science world was the Netflix competition started in 2006, in ... | <ASSISTANT_TASK:>
Python Code:
# Importing the data
import pandas as pd
import numpy as np
header = ['user_id', 'item_id', 'rating', 'timestamp']
data_movie_raw = pd.read_csv('../data/ml-100k/u.data', sep='\t', names=header)
data_movie_raw.head()
Explanation: Recommender Engine
Perhaps the most famous example of a rec... |
15,915 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Unsupervised Analysis of Days of Week
Treating crossing each day as features to learn about the relatinships between various days.
Step1: Downloading ... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
Explanation: Unsupervised Analysis of Days of Week
Treating crossing each day as features to learn about the relatinships between various days.
End of explanation
from urllib import request
FREMONT_URL = 'https://data.seattle.gov/api/view... |
15,916 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
OCCT Bottle Tutorial
Step1: VTK Viewer
The following summarizes the mouse and keyboard commands for interacting with shapes rendered in the viewer.
Ke... | <ASSISTANT_TASK:>
Python Code:
height = 70.
width = 50.
thickness = 30.
pnt1 = [-width/2., 0., 0.]
pnt2 = [-width/2., -thickness/4., 0.]
pnt3 = [0., -thickness/2., 0.]
pnt4 = [width/2., -thickness/4., 0.]
pnt5 = [width/2., 0., 0.]
edge1 = Edge().createLine(start=pnt1, end=pnt2)
edge2 = Ed... |
15,917 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Wind energy production forecast from Met.no weather forecast
With this notebook we illustrate how one might improve weather forecast for wind energy pr... | <ASSISTANT_TASK:>
Python Code:
%matplotlib notebook
import urllib.request
import numpy as np
import simplejson as json
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import warnings
import datetime
import dateutil.parser
import matplotlib.cbook
warnings.filterwarnings("ignore",category=matplotlib.cbo... |
15,918 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
596
Step1: Discovering available alignments
Alignments for a specified transcript
This approach identifies available alignments and then selects the d... | <ASSISTANT_TASK:>
Python Code:
from hgvs.easy import (__version__, parser, hdp, vm)
from hgvs.exceptions import HGVSDataNotAvailableError
__version__
# hgvs_g = "NC_000010.11:g.94762693G>A" # GRCh38
hgvs_g = "NC_000010.10:g.96522450G>A" # GRCh37
hgvs_c = "NM_000769.4:c.-13G>A"
var_c = parser.parse(hgvs_c)
var_g = par... |
15,919 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
The decimal module implements fixed and floating point arithmetic using the model familiar to most people, rather than the IEEE floating point version ... | <ASSISTANT_TASK:>
Python Code:
import decimal
fmt = '{0:<25}{1:<25}'
print(fmt.format('Input', 'Output'))
print(fmt.format('-'*25, '-'*25))
#Integer
print(fmt.format(5, decimal.Decimal(5)))
#String
print(fmt.format('3.14', decimal.Decimal('3.14')))
#Float
f = 0.1
print(fmt.format(repr(f), decimal.Decimal(str(f))))
prin... |
15,920 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
ModelSelection.ipynb
Choosing the number of states and a suitable timescale for hidden Markov models
One of the challenges associated with using hidden... | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sys
from IPython.display import display, clear_output
sys.path.insert(0, 'helpers')
from efunctions import * # load my helper function(s) to save pdf figures, etc.
from hc3 import load_data... |
15,921 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Basics of Marvin
In this notebook, you will learn the common core functionality across many of the Marvin Tools. This includes the basics of accessing... | <ASSISTANT_TASK:>
Python Code:
from marvin.tools import Cube
Explanation: Basics of Marvin
In this notebook, you will learn the common core functionality across many of the Marvin Tools. This includes the basics of accessing and handling MaNGA data from different locations, as well as a beginners guide of interacting ... |
15,922 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
The big reset
So I went ahead and cleared the memory.
Step1: The mystery section remains the same.
Step2: All the blocks are empty.
Step3: The 'Pres... | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.append('..')
import collections
import mido
from commons import dgxdump
from commons.dumpdata import messages, songdata, regdata, regvalues
old_syx_messages = mido.read_syx_file('../data/syxout5.syx')
clear_syx_messages = mido.read_syx_file('../data/clear_bulk.txt')
o_... |
15,923 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Numpy Exercise 4
Imports
Step1: Complete graph Laplacian
In discrete mathematics a Graph is a set of vertices or nodes that are connected to each othe... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
Explanation: Numpy Exercise 4
Imports
End of explanation
import networkx as nx
K_5=nx.complete_graph(5)
nx.draw(K_5)
Explanation: Complete graph Laplacian
In discrete mathematics a Graph is a set o... |
15,924 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
ES-DOC CMIP6 Model Properties - Ocean
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'messy-consortium', 'sandbox-2', 'ocean')
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: MESSY-CONSORTIUM
Source ID: SANDBOX-2
Topic: Oc... |
15,925 | <SYSTEM_TASK:>
Given the following text problem statement, write Python code to implement the functionality described below in problem statement
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Problem:
Given a distance matrix, with similarity between various fruits : | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import sklearn.cluster
simM = load_data()
model = sklearn.cluster.AgglomerativeClustering(affinity='precomputed', n_clusters=2, linkage='complete').fit(simM)
cluster_labels = model.labels_
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15,926 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Vertex SDK
Step1: Restart the Kernel
Once you've installed the Vertex SDK and Google cloud-storage, you need to restart the notebook kernel so it can ... | <ASSISTANT_TASK:>
Python Code:
! pip3 install google-cloud-storage
Explanation: Vertex SDK: Train & deploy a TensorFlow model with hosted runtimes (aka pre-built containers)
Installation
Install the Google cloud-storage library as well.
End of explanation
import os
if not os.getenv("AUTORUN"):
# Automatically resta... |
15,927 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
<p>
<img src="http
Step1: Stirling array $\mathcal{S}$
Step2: power function
Step3: inverse function
Step4: sqrt function
Step5: expt function
Ste... | <ASSISTANT_TASK:>
Python Code:
from sympy import *
from sympy.abc import n, i, N, x, lamda, phi, z, j, r, k, a, alpha
from commons import *
from matrix_functions import *
from sequences import *
import functions_catalog
init_printing()
Explanation: <p>
<img src="http://www.cerm.unifi.it/chianti/images/logo%20unifi_posi... |
15,928 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Sets & Frozen Sets
A set in python is an unordered sequence of unique and immutable objects, although the set itself is mutable. A frozen set is the sa... | <ASSISTANT_TASK:>
Python Code:
# from a list
a = set([1,2,3,4])
a
# using curly braces
a = {1,2,3,4}
a
# using a tuple
a = set((1,2,3,4))
a
# start with and empty set and add elements to it
a = set()
a.add('hello')
a
# be careful in assigning a string as an element to a set. If assigned as below, it'll be broken up and... |
15,929 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Principal Component Analysis
by Rene Zhang and Max Margenot
Part of the Quantopian Lecture Series
Step1: We will introduce PCA with an image processin... | <ASSISTANT_TASK:>
Python Code:
from numpy import linalg as LA
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
Explanation: Principal Component Analysis
by Rene Zhang and Max Margenot
Part of the Quantopian Lecture Series:
www.quantopian.com/lectures
https://github.com/quantopian/research_public
A... |
15,930 | <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: Face Generation
In this project, you'll use generative adversarial networks to generate new images of faces.
Get the Data
You'll be using two da... | <ASSISTANT_TASK:>
Python Code:
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
DON'T MODIFY ANYTHING IN THIS CELL
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Explanation: Face Generation
In this project, you'll use ... |
15,931 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural network that performs sentiment analysis. Using an RNN rather than... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import tensorflow as tf
with open('../sentiment-network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment-network/labels.txt', 'r') as f:
labels_orig = f.read()
reviews[:2000]
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll... |
15,932 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Workshop – Introduction to Neural Networks using TensorFlow and Keras
http
Step1: How does Tensorflow Low Level API look like?
Step2: Interactive usa... | <ASSISTANT_TASK:>
Python Code:
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
%pylab inline
from distutils.version import StrictVersion
import sklearn
print(sklearn.__version__)
assert StrictVersion(sklearn.__version__ ) >= StrictVersion('0.18.1')
import tensorflow as tf
tf.logging.set_verbosity(t... |
15,933 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Qualité de l'air mesurée dans la station Châtelet
Source de données utilisée (fichiers CSV en open data)
Step1: Infos diverses sur le DataFrame
Step2:... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
#%matplotlib notebook
import matplotlib
matplotlib.rcParams['figure.figsize'] = (9, 9)
import pandas as pd
def conv_func(s):
s = s.replace('<', '')
if s == 'ND':
return np.nan
elif s.strip() == '':
return np.nan
else:
return float... |
15,934 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Factorial HMM
Example synthetic data
Step1: Test out learned distribution inside of SMC
We'll compare it against a baseline of "bootstrap" SMC, which ... | <ASSISTANT_TASK:>
Python Code:
devices = factorial_hmm.gen_devices()
T = 50
np.random.seed(20)
X, Y = factorial_hmm.gen_dataset(devices, T)
plt.figure(figsize=(15,3.5))
plt.plot(Y)
plt.figure(figsize=(15,10))
plt.imshow((X*devices).T, interpolation='None', aspect=1);
plt.yticks(np.arange(len(devices)), devices);
print ... |
15,935 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Gaussian Process Regression in Pytorch
Thomas Viehmann, tv@lernapparat.d
... | <ASSISTANT_TASK:>
Python Code:
from matplotlib import pyplot
%matplotlib inline
import IPython
import torch
import numpy
import sys, os
sys.path.append(os.path.join(os.getcwd(),'..'))
pyplot.style.use('ggplot')
import candlegp
import candlegp.training.hmc
Explanation: Gaussian Process Regression in Pytorch
Thomas Viehm... |
15,936 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Basic Principles of Machine Learning
Here we'll dive into the basic principles of machine learning, and how to
utilize them via the Scikit-Learn API.
A... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
Explanation: Basic Principles of Machine Learning
Here we'll dive into the basic principles of machine learning, and how to
utilize them via the Scikit-Learn API.
After briefly introducing scikit-learn's Estimator objec... |
15,937 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
This is functionally similar to the the other notebook. All the operations here have been vectorized. This results in much much faster code, but is als... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
Explanation: This is functionally similar to the the other notebook. All the operations here have been vectorized. This results in much much faster code, but is also much unreadable. The vectorization also necessitated the replacement of the Gauss-Seidel smoother with u... |
15,938 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Lab 1
Step1: The core tables in the data warehouse are derived from 5 separate core operational systems (each with many tables)
Step2: Question
Step3... | <ASSISTANT_TASK:>
Python Code:
%%bigquery
SELECT
dataset_id,
table_id,
-- Convert bytes to GB.
ROUND(size_bytes/pow(10,9),2) as size_gb,
-- Convert UNIX EPOCH to a timestamp.
TIMESTAMP_MILLIS(creation_time) AS creation_time,
TIMESTAMP_MILLIS(last_modified_time) as last_modified_time,
row_count,
CASE ... |
15,939 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Задание 1
Написать функцию на языке Python, формирующую список всех исполнителей композиций жанра Рок. Список должен быть упорядочен в порядке убывания... | <ASSISTANT_TASK:>
Python Code:
def task1():
cursor = db.cursor()
cursor.execute('''
select distinct ar.Name
from tracks t
inner join albums al
on t.albumid = al.albumid
inner join artists ar
on al.artistid = ar.artistid
inner join genres g
on t.genreid = g.genreid
where g.name = 'Rock'
... |
15,940 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Bayesian Hierarchical Stacking
Step1: 1. Exploratory Data Analysis <a class="anchor" id="1"></a>
The data we have to work with looks at households in ... | <ASSISTANT_TASK:>
Python Code:
!pip install -q numpyro@git+https://github.com/pyro-ppl/numpyro
import os
from IPython.display import set_matplotlib_formats
import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.interpolate import BSpline
import seaborn as sns
import jax
imp... |
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Description:
Paris Saclay Center for Data Science
Titanic RAMP
Step1: Exploratory data analysis
Loading the data
Step2: The original training data frame has 891 r... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import os
import glob
import numpy as np
from scipy import io
import matplotlib.pyplot as plt
import pandas as pd
Explanation: Paris Saclay Center for Data Science
Titanic RAMP: survival prediction of Titanic passengers
Benoit Playe (Institut Curie/Mines ParisTech), Chl... |
15,942 | <SYSTEM_TASK:>
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Description:
QuTiP Lecture
Step1: Introduction
The quantum two-level system (TLS) is the simplest possible model for quantum light-matter interaction. In the versi... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import interp2d
from qutip import *
Explanation: QuTiP Lecture: Pulse-wise two-photon interference of emission from a two-level system
K.A. Fischer, ... |
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Description:
Comparing surrogate models
Tim Head, July 2016.
Step1: Bayesian optimization or sequential model-based optimization uses a surrogate model
to model th... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
np.random.seed(123)
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (10, 6)
plt.set_cmap("viridis")
Explanation: Comparing surrogate models
Tim Head, July 2016.
End of explanation
from skopt.benchmarks import branin as _branin
def bra... |
15,944 | <SYSTEM_TASK:>
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Description:
ES-DOC CMIP6 Model Properties - Land
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-3', 'sandbox-1', 'land')
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: TEST-INSTITUTE-3
Source ID: SANDBOX-1
Topic: Land... |
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Problem:
I'm using tensorflow 2.10.0. | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
import numpy as np
np.random.seed(10)
a = tf.constant(np.random.rand(50, 100, 512))
def g(a):
return tf.expand_dims(a, 2)
result = g(a.__copy__())
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Description:
Collation outputs
Introduction
In practice
Table
Step1: Table
Step2: Before moving to the other outputs, try to produce the simple HTML output by cha... | <ASSISTANT_TASK:>
Python Code:
#import the collatex library
from collatex import *
#create an instance of the collateX engine
collation = Collation()
#add witnesses to the collateX instance
collation.add_plain_witness( "A", "The quick brown fox jumped over the lazy dog.")
collation.add_plain_witness( "B", "The brown fo... |
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Problem:
How do I find all rows in a pandas DataFrame which have the max value for count column, after grouping by ['Sp','Value'] columns? | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
df = pd.DataFrame({'Sp':['MM1','MM1','MM1','MM2','MM2','MM2','MM4','MM4','MM4'],
'Value':['S1','S1','S3','S3','S4','S4','S2','S2','S2'],
'Mt':['a','n','cb','mk','bg','dgd','rd','cb','uyi'],
'count':[3,2,5,8,10,1,... |
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Description:
Study of Correlation Between Building Demolition and Associated Features
Capstone Project for Data Science at Scale on Coursera
Repo is located here
Ch... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from IPython.display import Image
%matplotlib inline
Explanation: Study of Correlation Between Building Demolition and Associated Features
Capstone Project for Data Science at Scale on Coursera
Repo is located here
Che... |
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Description:
Histograma e Estatística de uma imagem
Histograma de uma imagem
A imagem normalmente é composta de um grande número de pixels; hoje em dia,
celulares t... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import sys,os
ia898path = os.path.abspath('../../')
if ia898path not in sys.path:
sys.path.append(ia898path)
import ia898.src as ia
!ls -l ../../ia898/data
f = mpimg.imread('../d... |
15,950 | <SYSTEM_TASK:>
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Description:
The Basic Tools of the Deep Life Sciences
Welcome to DeepChem's introductory tutorial for the deep life sciences. This series of notebooks is a step-by... | <ASSISTANT_TASK:>
Python Code:
!pip install --pre deepchem
Explanation: The Basic Tools of the Deep Life Sciences
Welcome to DeepChem's introductory tutorial for the deep life sciences. This series of notebooks is a step-by-step guide for you to get to know the new tools and techniques needed to do deep learning for th... |
15,951 | <SYSTEM_TASK:>
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Description:
DV360 Automation
Step1: 0.2 Setup your GCP project
To utilise the DV360 API, you need a Google Cloud project. For the purpose of this workshop, we've ... | <ASSISTANT_TASK:>
Python Code:
!pip install google-api-python-client
!pip install google-cloud-vision
import csv
import datetime
import io
import json
import pprint
from google.api_core import retry
from google.cloud import vision
from google.colab import files
from google_auth_oauthlib.flow import InstalledAppFlow
fro... |
15,952 | <SYSTEM_TASK:>
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Description:
Graphical User Interfaces
Object oriented programming and particularly inheritance is commonly used for creating GUIs. There are a large number of diff... | <ASSISTANT_TASK:>
Python Code:
import tkinter as tk
class Application(tk.Frame):
def __init__(self, master=None):
tk.Frame.__init__(self, master)
self.pack()
self.createWidgets()
def createWidgets(self):
self.hi_there = tk.Button(self)
self.hi_there["text"] = "Hello World... |
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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:
Basics of NUmpy & Pandas
Numpy
Numpy uses array whereas pandas used scaler <br />
Step1: Array are similar to python list , but it all element must be... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
Explanation: Basics of NUmpy & Pandas
Numpy
Numpy uses array whereas pandas used scaler <br />
End of explanation
num = np.array([3,4,2,5,7,23,56,23,7,23,89,43,676,43])
num
Explanation: Array are similar to python list , but it all element must be of same data type, and... |
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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:
NoSQL (MongoDB) (Ejemplo de Replica Set y Sharding)
Esta hoja muestra cómo crear un clúster (simulado con docker-compose donde se mantienen dos replica... | <ASSISTANT_TASK:>
Python Code:
!pip install --upgrade pymongo
from pprint import pprint as pp
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline
matplotlib.style.use('ggplot')
%%bash
sudo apt-get update
sudo apt-get install -y mongodb-clients
Explanation: NoSQL (MongoDB) (Ejemplo d... |
15,955 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Using a Pre-trained PyTorch Model for Inference
In this demo, we will use a pre-trained model to perform inference on a single image.
There are 3 comp... | <ASSISTANT_TASK:>
Python Code:
import torch
import torchvision
import torchvision.transforms as transforms
import timm
from einops import rearrange
from PIL import Image
Explanation: Using a Pre-trained PyTorch Model for Inference
In this demo, we will use a pre-trained model to perform inference on a single image.
... |
15,956 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Sweden
Step1: 2. Join data to identify common water samples
The recent data from Salar includes more water samples than are currently in RESA2. In add... | <ASSISTANT_TASK:>
Python Code:
# Read data
in_xls = (r'C:\Data\James_Work\Staff\Heleen_d_W\ICP_Waters\TOC_Trends_Analysis_2015'
r'\Swedish_Ca_Data\Missing_Data_25_Swedish_Sites.xlsx')
smhi_df = pd.read_excel(in_xls, sheetname='salar_data')
resa_df = pd.read_excel(in_xls, sheetname='from_resa_10-02-2017')
# Ge... |
15,957 | <SYSTEM_TASK:>
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Description:
Step1: Language Translation
In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a se... | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
Explanation: Language Translation
In this proj... |
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Description:
ES-DOC CMIP6 Model Properties - Aerosol
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributo... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cmcc', 'sandbox-3', 'aerosol')
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: CMCC
Source ID: SANDBOX-3
Topic: Aerosol
Sub-Topics: Tr... |
15,959 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Use Word2Vec in gensim to train a word embedding model using the content from NIPS papers.
Step1: Gensim word2vec
https
Step2: Train a word2vec model... | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
%matplotlib inline
#%config InlineBackend.figure_format = 'svg'
#config InlineBackend.figure_format = 'pdf'
from IPython.core.display import HTML
import gensim as gen
import gensim.models.word2vec as w2v
import matplotlib.pyplot as plt
from nltk.tokenize... |
15,960 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Using ICsound
csoundmagics includes an ICsound class which is adapted from Andrés Cabrera's icsound module. ICsound is bound to the %%csound and %csoun... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%load_ext csoundmagics
Explanation: Using ICsound
csoundmagics includes an ICsound class which is adapted from Andrés Cabrera's icsound module. ICsound is bound to the %%csound and %csound magics command.
This notebook is an adaptation of... |
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Description:
Udacity MLND Capstone Project
"Determination of students’ interaction patterns with an intelligent tutoring system and study of their correlation with ... | <ASSISTANT_TASK:>
Python Code:
# Select test_size and random_state for splitting a subset
test_size=0.1
random_state=0
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import time
import gzip
import shutil
import seaborn as sns
from collections import ... |
15,962 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Introduction to NLTK
We have seen how to do some basic text processing in Python, now we introduce an open source framework for natural language proces... | <ASSISTANT_TASK:>
Python Code:
sampleText1 = "The Elephant's 4 legs: THE Pub! You can't believe it or can you, the believer?"
sampleText2 = "Pierre Vinken, 61 years old, will join the board as a nonexecutive director Nov. 29."
Explanation: Introduction to NLTK
We have seen how to do some basic text processing in Python... |
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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:
SciPy for Economists
Scipy provides many user-friendly and efficient numerical routines, e.g. numerical integration and optimization. The full document... | <ASSISTANT_TASK:>
Python Code:
# standard library
import numpy as np
# Parametrization
num_agents = 1000
num_covars = 3
betas_true = np.array([0.22, 0.30, -0.1]).T
sd_true = 0.01
# Sampling of observables
np.random.seed(123)
X = np.random.rand(num_agents, num_covars)
X[:,0] = 1
# Sampling disturbances
eps = np.rando... |
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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:
Graded = 7/8
Step1: & for multiple parameters
Step2: 2) What genres are most represented in the search results?
Edit your previous printout to also d... | <ASSISTANT_TASK:>
Python Code:
import requests
response = requests.get('https://api.spotify.com/v1/search?query=lil&type=artist&market=US&limit=50')
Explanation: Graded = 7/8
End of explanation
data = response.json()
data.keys()
artist_data = data['artists']
artist_data.keys()
lil_names = artist_data['items']
#lil_name... |
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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:
Convolutional Neural Networks
Step1: Run the next cell to load the "SIGNS" dataset you are going to use.
Step2: As a reminder, the SIGNS dataset is a... | <ASSISTANT_TASK:>
Python Code:
import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import scipy
from PIL import Image
from scipy import ndimage
import tensorflow as tf
from tensorflow.python.framework import ops
from cnn_utils import *
%matplotlib inline
np.random.seed(1)
Explanation: Convolution... |
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Description:
Softmax exercise
Adapt from the Stanford CS231n assignment1, find the original version on the course website.
In this exercise we will
Step5: Load CIF... | <ASSISTANT_TASK:>
Python Code:
import random
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.data_utils import load_CIFAR10
from cs231n.gradient_check import grad_check_sparse
# plotting setting
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rc... |
15,967 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Opinion Game - One Topic in the network
These codes were developed as a part of the our opinion game model.
Import Python built-in functions we need to... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from numpy.random import randn
import pandas as pd
from pandas import Series, DataFrame
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import matplotlib.image as mpimg
from matplotlib import rcParams
import seaborn as sb
Explanation: Opinion Ga... |
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Description:
LogGabor user guide
Table of content
What is the LogGabor package?
Installing
Importing the library
Properties of log-Gabor filters
Testing filter ge... | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
from LogGabor import LogGabor
parameterfile = 'https://raw.githubusercontent.com/bicv/LogGabor/master/default_param.py'
lg = LogGabor(parameterfile)
lg.set_size((32, 32))
Explanation: LogGabor user guide
Table of content
What is the LogGabor package?
In... |
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Description:
Porting Bike-Sharing project-1 to RNN
Step1: Load and prepare the data
A critical step in working with neural networks is preparing the data correctly... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
Explanation: Porting Bike-Sharing project-1 to RNN
End of explanation
data_path = 'Bike-Sharing-Dataset/hour.csv'
rides = pd.read_csv(data_pa... |
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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:
ЦАО
Step1: Собираем ссылки на все квартиры первых тридцати страниц выдачи
Step2: Cтандартный блок, в котором мы получаем по ссылке текст страницы в у... | <ASSISTANT_TASK:>
Python Code:
district = 'http://www.cian.ru/cat.php?deal_type=sale&district%5B0%5D=13&district%5B1%5D=14&district%5B2%5D=15&district%5B3%5D=16&district%5B4%5D=17&district%5B5%5D=18&district%5B6%5D=19&district%5B7%5D=20&district%5B8%5D=21&district%5B9%5D=22&engine_version=2&offer_type=flat&p={}&room1=1... |
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Description:
Table of Contents
<p><div class="lev1"><a href="#Data-preparation"><span class="toc-item-num">1 - </span>Data preparation</a></div><div class="lev1"><a... | <ASSISTANT_TASK:>
Python Code:
# for plotting purposes
%matplotlib inline
from matplotlib.pylab import plt
from matplotlib import rcParams
dark_colors = ["#A51C30", "#808080",
(0.8509803921568627, 0.37254901960784315, 0.00784313725490196),
(0.4588235294117647, 0.4392156862745098, 0.70196... |
15,972 | <SYSTEM_TASK:>
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Description:
Could not find kernel error?
If you ever see a "could not find a kernel for this notebook" error message, it will offer you a pull down menu for you to... | <ASSISTANT_TASK:>
Python Code:
tempF = 212.0
tempC = (5 / 9) * (tempF - 32.0)
tempC
Explanation: Could not find kernel error?
If you ever see a "could not find a kernel for this notebook" error message, it will offer you a pull down menu for you to pick a fitting kernel.
Remember, kernels are the notebook's way to find... |
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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:
Transform EEG data using current source density (CSD)
This script shows an example of how to use CSD [1] [2] [3]_.
CSD takes the spatial Laplacian of t... | <ASSISTANT_TASK:>
Python Code:
# Authors: Alex Rockhill <aprockhill206@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
Explanation: Transform EEG data using current source density (CSD)
Thi... |
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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:
Unsupervised learning
Step1: First, we start with some exploratory clustering, visualizing the clustering dendrogram using SciPy's linkage and dendrog... | <ASSISTANT_TASK:>
Python Code:
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data[:, [2, 3]]
y = iris.target
n_samples, n_features = X.shape
plt.scatter(X[:, 0], X[:, 1], c=y);
Explanation: Unsupervised learning: Hierarchical and density-based clustering algorithms
In a previous notebook, "08 Unsup... |
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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:
Estimating At-Work Subtour Frequency
This notebook illustrates how to re-estimate a single model component for ActivitySim. This process
includes run... | <ASSISTANT_TASK:>
Python Code:
import os
import larch # !conda install larch -c conda-forge # for estimation
import pandas as pd
Explanation: Estimating At-Work Subtour Frequency
This notebook illustrates how to re-estimate a single model component for ActivitySim. This process
includes running ActivitySim in estima... |
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