Unnamed: 0 int64 0 16k | text_prompt stringlengths 110 62.1k | code_prompt stringlengths 37 152k |
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9,000 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Initial investigation of drug-gene networks
Brin Rosenthal (sbrosenthal@ucsd.edu)
April 15, 2016
Prototype for tool to be added to Search
Goals
Step1: Test drug_gene_heatprop module
This... | Python Code:
# import some useful packages
import numpy as np
import matplotlib.pyplot as plt
import seaborn
import networkx as nx
import pandas as pd
import random
import json
# latex rendering of text in graphs
import matplotlib as mpl
mpl.rc('text', usetex = False)
mpl.rc('font', family = 'serif')
% matplotlib inlin... |
9,001 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2020 DeepMind Technologies Limited.
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 ... | Python Code:
!pip install dm-acme
!pip install dm-acme[reverb]
!pip install dm-acme[tf]
!pip install dm-sonnet
Explanation: Copyright 2020 DeepMind Technologies Limited.
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... |
9,002 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Spatial Joins
A spatial join uses binary predicates
such as intersects and crosses to combine two GeoDataFrames based on the spatial relationship
between their geometries.
A common use cas... | Python Code:
%matplotlib inline
from shapely.geometry import Point
from geopandas import datasets, GeoDataFrame, read_file
# NYC Boros
zippath = datasets.get_path('nybb')
polydf = read_file(zippath)
# Generate some points
b = [int(x) for x in polydf.total_bounds]
N = 8
pointdf = GeoDataFrame([
{'geometry': Point(x,... |
9,003 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
EXP 1-Random
In this experiment we generate 1000 sequences each comprising 10 SDRs generated at random. We present these sequences to the TM with learning "on". Each training epoch starts by... | Python Code:
import numpy as np
import random
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from nupic.bindings.algorithms import TemporalMemory as TM
from htmresearch.support.neural_correlations_utils import *
uintType = "uint32"
random.seed(1)
symbolsPerSequence = 10
numSequences = 1000... |
9,004 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Reading the data
Step1: Build clustering model
Here we build a kmeans model , and select the "optimal" of clusters.
Here we see that the optimal number of clusters is 2.
Step2: Build the o... | Python Code:
def loadContributions(file, withsexe=False):
contributions = pd.read_json(path_or_buf=file, orient="columns")
rows = [];
rindex = [];
for i in range(0, contributions.shape[0]):
row = {};
row['id'] = contributions['id'][i]
rindex.append(contributions['id'][i])
... |
9,005 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Cell Magic Tutorial
Interactions with MLDB occurs via a REST API. Interacting with a REST API over HTTP from a Notebook interface can be a little bit laborious if you're using a general-purp... | Python Code:
%reload_ext pymldb
Explanation: Cell Magic Tutorial
Interactions with MLDB occurs via a REST API. Interacting with a REST API over HTTP from a Notebook interface can be a little bit laborious if you're using a general-purpose Python library like requests directly, so MLDB comes with a Python library called... |
9,006 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Regression Week 3
Step1: Next we're going to write a polynomial function that takes an SArray and a maximal degree and returns an SFrame with columns containing the SArray to all the powers... | Python Code:
import pandas as pd
import numpy as np
Explanation: Regression Week 3: Assessing Fit (polynomial regression)
In this notebook you will compare different regression models in order to assess which model fits best. We will be using polynomial regression as a means to examine this topic. In particular you wil... |
9,007 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Advanced Topics
Step1: Using scalar aggregates in filters
Step2: We could always compute some aggregate value from the table and use that in another expression, or we can use a data-derive... | Python Code:
import ibis
import os
hdfs_port = os.environ.get('IBIS_WEBHDFS_PORT', 50070)
hdfs = ibis.hdfs_connect(host='quickstart.cloudera', port=hdfs_port)
con = ibis.impala.connect(host='quickstart.cloudera', database='ibis_testing',
hdfs_client=hdfs)
ibis.options.interactive = True
Explan... |
9,008 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Filtro dos 10 crimes com mais ocorrências em abril
Step1: Todas as ocorrências criminais de abril
Step2: As 5 regiões com mais ocorrências
Step3: Acima podemos ver que a região 1 teve o m... | Python Code:
all_crime_tipos.head(10)
all_crime_tipos_top10 = all_crime_tipos.head(10)
all_crime_tipos_top10.plot(kind='barh', figsize=(12,6), color='#3f3fff')
plt.title('Top 10 crimes por tipo (Abr 2017)')
plt.xlabel('Número de crimes')
plt.ylabel('Crime')
plt.tight_layout()
ax = plt.gca()
ax.xaxis.set_major_formatter... |
9,009 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step2: Example 1b
Step3: Simulation 1
Step4: Simulation 2
Step5: Simulation 3
Step6: Simulation 4
Step7: Simulation 5
Step8: Create Plot | Python Code:
import contextlib
import time
import numpy as np
from scipy.optimize import curve_fit
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
from qutip import *
from qutip.ipynbtools import HTMLProgressBar
from qutip.nonmarkov.heom import HEOMSolver, BosonicBath, DrudeLorentzBath, DrudeLorent... |
9,010 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Using Variational Autoencoder to Generate Digital Numbers
Variational Autoencoders (VAEs) are very popular approaches to unsupervised learning of complicated distributions. In this example, ... | Python Code:
# a bit of setup
import numpy as np
from bigdl.nn.criterion import *
from bigdl.dataset import mnist
from zoo.pipeline.api.keras.layers import *
from zoo.pipeline.api.keras.models import Model
from zoo.pipeline.api.keras.utils import *
import datetime as dt
IMAGE_SIZE = 784
IMAGE_ROWS = 28
IMAGE_COLS = 28
... |
9,011 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Compute all-to-all connectivity in sensor space
Computes the Phase Lag Index (PLI) between all gradiometers and shows the
connectivity in 3D using the helmet geometry. The left visual stimul... | Python Code:
# Author: Martin Luessi <mluessi@nmr.mgh.harvard.edu>
#
# License: BSD (3-clause)
import mne
from mne import io
from mne.connectivity import spectral_connectivity
from mne.datasets import sample
from mne.viz import plot_sensors_connectivity
print(__doc__)
Explanation: Compute all-to-all connectivity in sen... |
9,012 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
HyperParameter Tuning
keras.wrappers.scikit_learn
Example adapted from
Step1: Data Preparation
Step2: Build Model
Step3: GridSearch HyperParameters | Python Code:
import numpy as np
np.random.seed(1337) # for reproducibility
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.utils import np_utils
from keras.wrappers.scikit_learn im... |
9,013 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Concatenated all 4 databases into one called "MyLA311_All_Requests.csv"
Step1: Parsed the merged database for all empty coordinates and removed them
Step2: Splitting the CreatedDate column... | Python Code:
fifteen = pd.read_csv("MyLA311_Service_Request_Data_2015.csv", low_memory = False)
sixteen = pd.read_csv("MyLA311_Service_Request_Data_2016.csv", low_memory = False)
seventeen = pd.read_csv("MyLA311_Service_Request_Data_2017.csv", low_memory = False)
eighteen = pd.read_csv("MyLA311_Service_Request_Data_201... |
9,014 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Analysis
When you vizualize your network, you also want to analize the network. In this section, you can learn basic analysis methods to network. The methods used in this section are prepare... | Python Code:
# import data from url
from py2cytoscape.data.cyrest_client import CyRestClient
from IPython.display import Image
# Create REST client for Cytoscape
cy = CyRestClient()
# Reset current session for fresh start
cy.session.delete()
# Load a sample network
network = cy.network.create_from('http://chianti.ucsd.... |
9,015 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Function histogram
Synopse
Image histogram.
h = histogram(f)
f
Step1: Function Code for brute force implementation
Step2: Function code for bidimensional matrix implementation
Step3: Exam... | Python Code:
import numpy as np
def histogram(f):
return np.bincount(f.ravel())
Explanation: Function histogram
Synopse
Image histogram.
h = histogram(f)
f: Input image. Pixel data type must be integer.
h: Output, integer vector.
Description
This function computes the number of occurrence of each pixel value.
The ... |
9,016 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
An Introduction to pandas
Pandas! They are adorable animals. You might think they are the worst animal ever but that is not true. You might sometimes think pandas is the worst library every,... | Python Code:
# import pandas, but call it pd. Why? Because that's What People Do.
import pandas as pd
Explanation: An Introduction to pandas
Pandas! They are adorable animals. You might think they are the worst animal ever but that is not true. You might sometimes think pandas is the worst library every, and that is on... |
9,017 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial Part 15
Step1: To begin, let's import all the libraries we'll need and load the dataset (which comes bundled with Tensorflow).
Step2: Let's view some of the images to get an idea ... | Python Code:
!curl -Lo conda_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py
import conda_installer
conda_installer.install()
!/root/miniconda/bin/conda info -e
!pip install --pre deepchem
import deepchem
deepchem.__version__
Explanation: Tutorial Part 15: Training a Gen... |
9,018 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
First Python Notebook
Step1: There. You've just written your first Python code. You've entered two integers (the 2's) and added them together using the plus sign operator. Not so bad, right... | Python Code:
2+2
Explanation: First Python Notebook: Scripting your way to the story
By Ben Welsh
A step-by-step guide to analyzing data with Python and the Jupyter Notebook.
This tutorial will teach you how to use computer programming tools to analyze data by exploring contributors to campaigns for and again Propositi... |
9,019 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This tutorial takes you through the basics of analysing Mitty data with some help from cytoolz and pandas
Step1: Ex1
Step2: Ex2
Step3: Ex3
Step4: Ex2
More involved example showing, in se... | Python Code:
%load_ext autoreload
%autoreload 2
import time
import matplotlib.pyplot as plt
import cytoolz.curried as cyt
from bokeh.plotting import figure, show, output_file
import mitty.analysis.bamtoolz as bamtoolz
import mitty.analysis.bamfilters as mab
import mitty.analysis.plots as mapl
# import logging
# FORMAT ... |
9,020 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Toy weather data
Here is an example of how to easily manipulate a toy weather dataset using
xarray and other recommended Python libraries
Step1: Examine a dataset with pandas and seaborn
Co... | Python Code:
import numpy as np
import pandas as pd
import seaborn as sns
import xarray as xr
np.random.seed(123)
xr.set_options(display_style="html")
times = pd.date_range("2000-01-01", "2001-12-31", name="time")
annual_cycle = np.sin(2 * np.pi * (times.dayofyear.values / 365.25 - 0.28))
base = 10 + 15 * annual_cycle.... |
9,021 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Getting Started with the Gluon Interface
In this example from the Github repository we will build and train a simple two-layer artificial neural network (ANN) called a multilayer perceptron.... | Python Code:
import mxnet as mx
from mxnet import gluon, autograd, ndarray
import numpy as np
Explanation: Getting Started with the Gluon Interface
In this example from the Github repository we will build and train a simple two-layer artificial neural network (ANN) called a multilayer perceptron.
First, we need to impo... |
9,022 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Create Schema
Using the # operator you can execute sql statements as a script
Note
Step1: Insert some data
Let's insert a single user into the database using the ! operator
Step2: Or inser... | Python Code:
help(queries.create_schema)
queries.create_schema(conn)
Explanation: Create Schema
Using the # operator you can execute sql statements as a script
Note: Variable substitution is not possible
End of explanation
queries.add_user(conn, **{"username": "badger77", "firstname": "Mike", "lastname": "Jones"})
Expl... |
9,023 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Probability distributions - 1
ToC
- Axioms of probability
- Conditional probability
- Bayesian conditional probability
- Random variables
- Properties of discrete random variables
... | Python Code:
import matplotlib.pyplot as plt
%matplotlib inline
from matplotlib_venn import venn2
venn2(subsets = (0.45, 0.15, 0.05), set_labels = ('A', 'B'))
Explanation: Probability distributions - 1
ToC
- Axioms of probability
- Conditional probability
- Bayesian conditional probability
- Random variables
-... |
9,024 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Construct a 5x3 matrix, uninitialized
Step1: Construct a randomly initialized matrix
Step2: Construct a matrix filled zeros and of dtype long
Step3: Construct a tensor directly from data
... | Python Code:
x = torch.empty(5,3)
print(x)
Explanation: Construct a 5x3 matrix, uninitialized:
End of explanation
x = torch.rand(5,3)
print(x)
Explanation: Construct a randomly initialized matrix:
End of explanation
x = torch.zeros(5,3,dtype=torch.long)
print(x)
Explanation: Construct a matrix filled zeros and of dtype... |
9,025 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: TV Script Generation
In this project, you'll generate your own Simpsons TV scripts using RNNs. You'll be using part of the Simpsons dataset of scripts from 27 seasons. The Neural Ne... | Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
Explanation: TV Script Generation
In this project, you'll generate your own Simpsons TV script... |
9,026 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Testing tutor-student matching with rate-based simulations
Step1: Define target motor programs
Step2: Choose target
Step12: General definitions
Here we define some classes and functions t... | Python Code:
%matplotlib inline
import matplotlib as mpl
import matplotlib.ticker as mtick
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('white')
plt.rc('text', usetex=True)
plt.rc('font', family='serif', serif='cm')
plt.rcParams['figure.titlesize'] = 10
plt.rcParams['axes.labelsize'] = 8
plt.rcPa... |
9,027 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
RNNs tutorial
Step1: An LSTM/RNN overview
Step2: Note that when we create the builder, it adds the internal RNN parameters to the ParameterCollection.
We do not need to care about them, bu... | Python Code:
# we assume that we have the dynet module in your path.
import dynet as dy
Explanation: RNNs tutorial
End of explanation
pc = dy.ParameterCollection()
NUM_LAYERS=2
INPUT_DIM=50
HIDDEN_DIM=10
builder = dy.LSTMBuilder(NUM_LAYERS, INPUT_DIM, HIDDEN_DIM, pc)
# or:
# builder = dy.SimpleRNNBuilder(NUM_LAYERS, IN... |
9,028 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Hands-on!
Nessa prática, sugerimos alguns pequenos exemplos para você implementar sobre o Spark.
Apriorando o Word Count Memória Postumas de Brás Cubas
Memórias Póstumas de Brás Cubas é um r... | Python Code:
# Bibliotecas
from pyspark.ml import Pipeline
from pyspark.ml.feature import Tokenizer, StopWordsRemover, CountVectorizer, NGram
livro = sc.textFile("Machado-de-Assis-Memorias-Postumas.txt")
text = ""
for line in livro.collect():
text += " " + line
data = spark.createDataFrame([(0, text)], ["id", "text... |
9,029 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Ne pas faire un "execute all"
Step1: Exercice
Step2: TensorFlow (II)
La même chose mais une session interactive, ce qui nous permet d'être un peu plus lâche dans l'ordre d'opérations.
St... | Python Code:
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
Explanation: Ne pas faire un "execute all" : la dernière cellule est très lourde.
Introduction à TensorFlow
Ce code est basé sur des tutoriel à tensorflow.org.
Nous allons utiliser _so... |
9,030 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Interact Exercise 4
Imports
Step2: Line with Gaussian noise
Write a function named random_line that creates x and y data for a line with y direction random noise that has a normal distribut... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
Explanation: Interact Exercise 4
Imports
End of explanation
def random_line(m, b, sigma, size=10):
Create a line y = m*x + b + N(0,sigm... |
9,031 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Visualizing optimization results
Tim Head, August 2016.
Reformatted by Holger Nahrstaedt 2020
.. currentmodule
Step1: Toy models
We will use two different toy models to demonstrate how
Ste... | Python Code:
print(__doc__)
import numpy as np
np.random.seed(123)
import matplotlib.pyplot as plt
Explanation: Visualizing optimization results
Tim Head, August 2016.
Reformatted by Holger Nahrstaedt 2020
.. currentmodule:: skopt
Bayesian optimization or sequential model-based optimization uses a surrogate
model to mo... |
9,032 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Seaice
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify ... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cmcc', 'cmcc-esm2-sr5', 'seaice')
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: CMCC
Source ID: CMCC-ESM2-SR5
Topic: Seaice
Sub-Topics: Dynamics, Therm... |
9,033 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This is the demonstration how to use NEDO data process utility
Step1: Load the data into a NEDOLocation object
Step2: main_df adds the column names into the raw data and convert it to a pa... | Python Code:
%load_ext autoreload
%autoreload 2
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import os
from pypvcell.solarcell import SQCell,MJCell,TransparentCell
from pypvcell.illumination import Illumination
from pypvcell.spectrum import Spectrum
from pypvcell.metpv_reade... |
9,034 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
문자열을 인쇄하는 다양한 방법 활용
파이썬 2.x에서는 print 함수의 경우 인자들이 굳이 괄호 안에 들어 있어야 할 필요는 없다. 또한 여러 개의 값을 동시에 인쇄할 수도 있다. 이때 인자들은 콤마로 구분지어진다.
Step1: 주의
Step2: 하지만 위와 같이 괄호를 사용하지 않는 방식은 파이썬 3.x에서는 지원되지 않는다.
따라... | Python Code:
a = "string"
b = "string1"
print a, b
print "The return value is", a
Explanation: 문자열을 인쇄하는 다양한 방법 활용
파이썬 2.x에서는 print 함수의 경우 인자들이 굳이 괄호 안에 들어 있어야 할 필요는 없다. 또한 여러 개의 값을 동시에 인쇄할 수도 있다. 이때 인자들은 콤마로 구분지어진다.
End of explanation
print(a, b)
print("The return value is", a)
Explanation: 주의: 아래와 같이 하면 모양이 기대와 다르게 나... |
9,035 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<br>
Weighted kernel density estimation to quickly reproduce the profile of a diffractometer
<br>
<br>
This example shows a work-arround for a quick visualization of a diffractorgram (simila... | Python Code:
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from ImageD11.columnfile import columnfile
from ImageD11 import weighted_kde as wkde
%matplotlib inline
plt.rcParams['figure.figsize'] = (6,4)
plt.rcParams['figure.dpi'] = 150
plt.rcParams['mathtext.fontset'] = 'cm'
plt.rcParams['f... |
9,036 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Welcome to the jupyter notebook! To run any cell, press Shit+Enter or Ctrl+Enter.
IMPORTANT
Step1: Notebook Basics
A cell contains any type of python inputs (expression, function definitio... | Python Code:
# Useful starting lines
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
%load_ext autoreload
%autoreload 2
Explanation: Welcome to the jupyter notebook! To run any cell, press Shit+Enter or Ctrl+Enter.
IMPORTANT : Please have a look at Help->User Interface Tour and Help->Keyboar... |
9,037 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Customer migration from Prestashop to Woocommerce part 1
Step1: Loading the data from Prestashop backend and sql. Note that Prestashop backend can generate some table for you by a default ... | Python Code:
import pandas as pd
import numpy as np
import csv
Explanation: Customer migration from Prestashop to Woocommerce part 1 : Gathering raw customer information from Prestashop
This is the product migration of the book store from Prestashop to Woocommerce format. Unlike the product, users (customers in Woocomm... |
9,038 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Aerosol
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'miroc-es2h', 'aerosol')
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: MIROC
Source ID: MIROC-ES2H
Topic: Aerosol
Sub-Topics: Transport, Emiss... |
9,039 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Notebook 2
Our current dataset suffers from duplicate tweet's from bots, hacked accounts etc.
As such, this notebook will show you how to deal with these duplicates in a manner that does not... | Python Code:
import pandas as pd
import arrow # way better than datetime
import numpy as np
import random
import re
%run helper_functions.py
import string
new_df = unpickle_object("new_df.pkl") # this loads up the dataframe from our previous notebook
new_df.head() #sorted first on date and then time!
new_df.iloc[0, 3]
... |
9,040 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Learning to Resize in Computer Vision
Author
Step1: Define hyperparameters
In order to facilitate mini-batch learning, we need to have a fixed shape for the images
inside a given batch. Thi... | Python Code:
from tensorflow.keras import layers
from tensorflow import keras
import tensorflow as tf
import tensorflow_datasets as tfds
tfds.disable_progress_bar()
import matplotlib.pyplot as plt
import numpy as np
Explanation: Learning to Resize in Computer Vision
Author: Sayak Paul<br>
Date created: 2021/04/30<br>
L... |
9,041 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2018 The TensorFlow Hub Authors.
Licensed under the Apache License, Version 2.0 (the "License");
Step1: ユニバーサルセンテンスエンコーダー Lite の実演
<table class="tfo-notebook-buttons" align="left"... | Python Code:
# Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... |
9,042 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Delayed Extra Sources in NuPyCEE
Created by Benoit Côté
This notebook introduces the general delayed-extra set of parameters in NuPyCEE that allows to include any enrichment source that requ... | Python Code:
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from NuPyCEE import sygma
Explanation: Delayed Extra Sources in NuPyCEE
Created by Benoit Côté
This notebook introduces the general delayed-extra set of parameters in NuPyCEE that allows to include any enrichment source that requires a de... |
9,043 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
$\newcommand{\G}{\mathcal{G}}$
$\newcommand{\V}{\mathcal{V}}$
$\newcommand{\E}{\mathcal{E}}$
$\newcommand{\R}{\mathbb{R}}$
This notebook shows how to apply our graph ConvNet (pa... | Python Code:
from lib import models, graph, coarsening, utils
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Introduction
$\newcommand{\G}{\mathcal{G}}$
$\newcommand{\V}{\mathcal{V}}$
$\newcommand{\E}{\mathcal{E}}$
$\newcommand{\R}{\mathbb{R}}$
This notebook shows how to apply our gr... |
9,044 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Time and coordinates
Step1: We are going to use astropy to find out whether the Large Magellanic Cloud (LMC) is visible from the a given observatory at a given time and date.
In the proces... | Python Code:
import matplotlib
matplotlib.use('Agg')
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Time and coordinates
End of explanation
import astropy.units as u
from astropy.time import Time
from astropy.coordinates import SkyCoord, EarthLocation, AltAz
Explanation: We are goi... |
9,045 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
LDA预处理
方案 1
将每张订单作为一个document
方案2
将每个用户作为一个document
将每个product作为一个Document
将每个aisle作为一个Document
将每个department作为Document
方案3
product name
无结构文本
<font color=red>方案2 part 1</font>
Step1: <font... | Python Code:
#方案2
orders = tle.get_orders()
users_orders = tle.get_users_orders('prior')
# 将product_id转换为str
users_products_matrix = users_orders.groupby(['user_id'])['product_id'].apply(utils.series_to_str)
# 构造vocabulary
tf = CountVectorizer(analyzer = 'word', lowercase = False, max_df=0.95, min_df=2,)
tf_matrix = tf... |
9,046 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Temporal whitening with AR model
Here we fit an AR model to the data and use it
to temporally whiten the signals.
Step1: Plot the different time series and PSDs | Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt
import mne
from mne.time_frequency import fit_iir_model_raw
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
raw_fna... |
9,047 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Land
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify do... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nasa-giss', 'sandbox-3', 'land')
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: NASA-GISS
Source ID: SANDBOX-3
Topic: Land
Sub-Topics: Soil, Snow, Vegetat... |
9,048 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Aufgabe 2
Step1: First we create a training set of size num_samples and num_features.
Step2: Next we run a performance test on the created data set. Therefor we train a random forest class... | Python Code:
# imports
from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
import time
import matplotlib.pyplot as plt
import seaborn as sns
Explanation: Aufgabe 2: Classification
A short test to examine the performance gain when using multiple cores on sklearn's esemble... |
9,049 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Text classification from scratch
Authors
Step1: Load the data
Step2: The aclImdb folder contains a train and test subfolder
Step3: The aclImdb/train/pos and aclImdb/train/neg folders cont... | Python Code:
import tensorflow as tf
import numpy as np
Explanation: Text classification from scratch
Authors: Mark Omernick, Francois Chollet<br>
Date created: 2019/11/06<br>
Last modified: 2020/05/17<br>
Description: Text sentiment classification starting from raw text files.
Introduction
This example shows how to do... |
9,050 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Once we've trained a model, we might want to better understand what sequence motifs the first convolutional layer has discovered and how it's using them. Basset offers two methods to help us... | Python Code:
model_file = '../data/models/pretrained_model.th'
seqs_file = '../data/encode_roadmap.h5'
Explanation: Once we've trained a model, we might want to better understand what sequence motifs the first convolutional layer has discovered and how it's using them. Basset offers two methods to help users explore th... |
9,051 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Speci... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'sandbox-2', 'atmoschem')
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: EC-EARTH-CONSORTIUM
Source ID: SANDBOX-2
Topic: Atmosc... |
9,052 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
编码分类特征
在机器学习中,特征经常不是数值型的而是枚举型的.举个例子,一个人可能有 ["male", "female"],["from Europe", "from US", "from Asia"],["uses Firefox", "uses Chrome", "uses Safari", "uses Internet Explorer"]等枚举类型的特征.这些特征能够被... | Python Code:
from sklearn import preprocessing
enc = preprocessing.OneHotEncoder()
enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]])
enc.transform([[0, 1, 3]]).toarray()
Explanation: 编码分类特征
在机器学习中,特征经常不是数值型的而是枚举型的.举个例子,一个人可能有 ["male", "female"],["from Europe", "from US", "from Asia"],["uses Firefox", "uses Chrome... |
9,053 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Bean Stalk Series
Step1: Now lets import the tetravolume.py module, which in turn has dependencies, to get these volumes directly, based on edge lengths. I'll use the edges given in Fig. 9... | Python Code:
import gmpy2
from gmpy2 import sqrt as rt2
from gmpy2 import mpfr
gmpy2.get_context().precision=200
root2 = rt2(mpfr(2))
root3 = rt2(mpfr(3))
root5 = rt2(mpfr(5))
ø = (root5 + 1)/2
ø_down = ø ** -1
ø_up = ø
E_vol = (15 * root2 * ø_down ** 3)/120 # a little more than 1/24, volume of T module
print(E_vol)
Ex... |
9,054 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: <figure>
<IMG SRC="../../logo/logo.png" WIDTH=250 ALIGN="right">
</figure>
IHE Python course, 2017
CHIRPS data for precipitation (worldwide between 50S and 50N latitude)
Never again... | Python Code:
import numpy as np
from pprint import pprint
def prar(A, ncol=8, maxsize=1000):
prints 2D arrays the Matlab 2 (more readable)
if A.size>1000: # don't try to print a million values, or your pc will hang.
print(A)
return
n = A.shape[1]
# print columns in formatted chu... |
9,055 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Examples and Exercises from Think Stats, 2nd Edition
http
Step1: Survival analysis
If we have an unbiased sample of complete lifetimes, we can compute the survival function from the CDF and... | Python Code:
from __future__ import print_function, division
%matplotlib inline
import warnings
warnings.filterwarnings('ignore', category=FutureWarning)
import numpy as np
import pandas as pd
import random
import thinkstats2
import thinkplot
Explanation: Examples and Exercises from Think Stats, 2nd Edition
http://thin... |
9,056 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
In this notebook we'll look at interfacing between the composability and ability to generate complex visualizations that HoloViews provides, the power of pandas library dataframes for manipu... | Python Code:
import itertools
import numpy as np
import pandas as pd
import seaborn as sb
import holoviews as hv
np.random.seed(9221999)
Explanation: In this notebook we'll look at interfacing between the composability and ability to generate complex visualizations that HoloViews provides, the power of pandas library d... |
9,057 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
For high dpi displays.
Step1: 0. General note
This example compares pressure calculated from pytheos and original publication for the gold scale by Dorogokupets 2015.
1. Global setup
Step2:... | Python Code:
%config InlineBackend.figure_format = 'retina'
Explanation: For high dpi displays.
End of explanation
import matplotlib.pyplot as plt
import numpy as np
from uncertainties import unumpy as unp
import pytheos as eos
Explanation: 0. General note
This example compares pressure calculated from pytheos and orig... |
9,058 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Code Testing and CI
Version 0.1
The notebook contains problems about code testing and continuous integration.
E Tollerud (STScI)
Problem 1
Step1: 1b
Step2: 1d
Step3: 1e
Step4: 1f
Step5: ... | Python Code:
!conda install pytest pytest-cov
Explanation: Code Testing and CI
Version 0.1
The notebook contains problems about code testing and continuous integration.
E Tollerud (STScI)
Problem 1: Set up py.test in you repo
In this problem we'll aim to get the py.test testing framework up and running in the code repo... |
9,059 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Compute ICA on MEG data and remove artifacts
ICA is fit to MEG raw data.
The sources matching the ECG and EOG are automatically found and displayed.
Subsequently, artifact detection and reje... | 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 mne.datasets impor... |
9,060 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Classifying Default of Credit Card Clients
<hr>
The dataset can be downloaded from
Step1: Feature importances with forests of trees
This examples shows the use of forests of trees to evalu... | Python Code:
import os
from sklearn.tree import DecisionTreeClassifier, export_graphviz
import pandas as pd
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn import cross_validation, metrics
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import Bernoulli... |
9,061 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Quiz 1 - Number of rainy days
Step3: count(*)
0 10
Quiz 2 - Temp on Foggy and Nonfoggy Days
Step5: fog max(maxtempi)
0 0 86
1 1 81
Quiz 3 - Mea... | Python Code:
import pandas
import pandasql
def num_rainy_days(filename):
'''
This function should run a SQL query on a dataframe of
weather data.
The SQL query should return:
- one column and
- one row - a count of the `number of days` in the dataframe where
the rain column is equal to 1 (i... |
9,062 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
I have a csv file which looks like | Problem:
from sklearn.cluster import KMeans
df = load_data()
kmeans = KMeans(n_clusters=2)
labels = kmeans.fit_predict(df[['mse']]) |
9,063 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Load and pre-process data
Step1: Impute PE
First, I will impute PE by replacing missing values with the mean PE. Second, I will impute PE using a random forest regressor. I will compare the... | Python Code:
from sklearn import preprocessing
filename = '../facies_vectors.csv'
train = pd.read_csv(filename)
# encode well name and formation features
le = preprocessing.LabelEncoder()
train["Well Name"] = le.fit_transform(train["Well Name"])
train["Formation"] = le.fit_transform(train["Formation"])
data_loaded = tr... |
9,064 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Deep Learning
Assignment 5
The goal of this assignment is to train a Word2Vec skip-gram model over Text8 data.
Step2: Download the data from the source website if necessary.
Step4: Read th... | Python Code:
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
%matplotlib inline
from __future__ import print_function
import collections
import math
import numpy as np
import os
import random
import tensorflow as tf
import zipfile
from matplotlib import pylab... |
9,065 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Basic plot example
Step1: $$c = \sqrt{a^2 + b^2}$$
$$
\begin{align}
c =& \sqrt{a^2 + b^2} \
=&\sqrt{4+16} \
\end{align}
$$
$$
\begin{align}
f(x)= & x^2 \
= & {{x[1]}}
\end{align}
$$
This t... | Python Code:
from matplotlib.pyplot import figure, plot, xlabel, ylabel, title, show
x=linspace(0,5,10)
y=x**2
figure()
plot(x,y,'r')
xlabel('x')
ylabel('y')
title('title')
show()
Explanation: Basic plot example
End of explanation
from IPython.display import display
text = widgets.FloatText()
floatText = widgets.FloatT... |
9,066 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data wrangling
Who we are
Jackie Kazil (@JackieKazil), author of the O'Reilly book Data Wrangling with Python
Abe Epton (@aepton)
What we'll cover
Loading data
Transforming it
Storing it
How... | Python Code:
import agate
table = agate.Table.from_csv('data/contracts_data.csv')
print table
Explanation: Data wrangling
Who we are
Jackie Kazil (@JackieKazil), author of the O'Reilly book Data Wrangling with Python
Abe Epton (@aepton)
What we'll cover
Loading data
Transforming it
Storing it
How we'll do it
We'll be w... |
9,067 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Hands-On Exercise 2
Step1: There is a lot of information for each source, and the overall image, in each of these catalog files. As a demonstration of the parameters available for each sour... | Python Code:
reference_catalog = '../data/PTF_Refims_Files/PTF_d022683_f02_c06_u000114210_p12_sexcat.ctlg'
# select R-band data (f02)
Explanation: Hands-On Exercise 2: Making a Lightcurve from PTF catalog data
Version 0.2
This "hands-on" session will proceed differently from those that are going to follow. Below, we ha... |
9,068 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Functions
Making reusable blocks of code.
Starting point
Step1: What about for $a = 2$, $b = 8$, and $c = 1$?
Step3: Functions
Step5: Observe how this function works.
Step7: Summarize
St... | Python Code:
## Code here
Explanation: Functions
Making reusable blocks of code.
Starting point:
In this exercise, we're going to calculate one of the roots from the quadratic formula:
$r_{p} = \frac{-b + \sqrt{b^{2} - 4ac}}{2a}$
Determine $r_{p}$ for $a = 1$, $b=4$, and $c=3$.
End of explanation
## Code here
Explanat... |
9,069 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Calculate performance of kWIP
The next bit of python code calculates the performance of kWIP against the distance between samples calulcated from the alignments of their genomes.
This code c... | Python Code:
expts = list(map(lambda fp: path.basename(fp.rstrip('/')), glob('data/*/')))
print("Number of replicate experiments:", len(expts))
def process_expt(expt):
expt_results = []
def extract_info(filename):
return re.search(r'kwip/(\d\.?\d*)x-(0\.\d+)-(wip|ip).dist', filename).groups()
... |
9,070 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Transfer function of a position sensor.
Andrés Marrugo, PhD
Universidad Tecnológica de Bolívar.
The transfer function of a small position sensor is evaluated experimentally. The sensor is... | Python Code:
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
d = np.array([0,0.08,0.16,0.24,0.32,0.4,0.48,0.52])
f = np.array([0,0.576,1.147,1.677,2.187,2.648,3.089,3.295])
plt.plot(f,d,'*')
plt.ylabel('Displacement [mm]')
plt.xlabel('Force [mN]')
plt.show()
Explanation: Transfer function of a pos... |
9,071 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Módulo 2
Step9: Construção
Series
Step20: DataFrame
Step21: Acessando valores
Definição das Variáveis
Step22: Slicing
Series
Step30: DataFrame
Step33: * Atribuição de Valores em DataFr... | Python Code:
import numpy as np
import pandas as pd
Explanation: Módulo 2: Introdução à Lib Pandas
Tutorial
Imports para a Aula
End of explanation
Construtor padrão
pd.Series(
name="Compras",
index=["Leite", "Ovos", "Carne", "Arroz", "Feijão"],
data=[2, 12, 1, 5, 2]
)
Construtor padrão: dados desconhec... |
9,072 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
AST 337 In-Class Lab #1
Wednesday, September 6, 2017
In this lab, you'll learn to read in and manipulate tabular data with the python package pandas and plot that data with the plotting modu... | Python Code:
#load packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: AST 337 In-Class Lab #1
Wednesday, September 6, 2017
In this lab, you'll learn to read in and manipulate tabular data with the python package pandas and plot that data with the plotting mo... |
9,073 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2020 The TensorFlow Authors.
Step1: NumPy API on TensorFlow
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step2: Enabling NumPy beha... | 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 writing, software
# dist... |
9,074 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
I started here
Step2: cf. 3.2 Datasets, 3.2.1 MNIST Dataset
Step3: GPU note
Using the GPU
THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python myscriptIwanttorunonthegpu.py
From the... | Python Code:
import theano
import theano.tensor as T
# cf. https://github.com/lisa-lab/DeepLearningTutorials/blob/c4db2098e6620a0ac393f291ec4dc524375e96fd/code/logistic_sgd.py
Explanation: I started here: Deep Learning tutorial
End of explanation
import cPickle, gzip, numpy
import os
os.getcwd()
os.listdir( os.getcwd(... |
9,075 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Vector space tutorial
The goal of this tutorial is to show how word co-occurrence statistics can be used to build their vectors, such that words that are similar in meaning are also close in... | Python Code:
# This is a code cell. It can be executed by pressing CTRL+Enter
print('Hello')
Explanation: Vector space tutorial
The goal of this tutorial is to show how word co-occurrence statistics can be used to build their vectors, such that words that are similar in meaning are also close in a vectorspace.
Getting ... |
9,076 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Building your Deep Neural Network
Step2: 2 - Outline of the Assignment
To build your neural network, you will be implementing several "helper functions". These helper functions will be used... | Python Code:
import numpy as np
import h5py
import matplotlib.pyplot as plt
from testCases_v3 import *
from dnn_utils_v2 import sigmoid, sigmoid_backward, relu, relu_backward
%matplotlib inline
plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rc... |
9,077 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Exercises
Step1: Exercise 1
Step2: b. Spearman Rank Correlation
Find the Spearman rank correlation coefficient for the relationship between x and y using the stats.rankdata function and th... | Python Code:
import numpy as np
import pandas as pd
import scipy.stats as stats
import matplotlib.pyplot as plt
import math
Explanation: Exercises: Spearman Rank Correlation
Lecture Link
This exercise notebook refers to this lecture. Please use the lecture for explanations and sample code.
https://www.quantopian.com/le... |
9,078 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
5. Impulse response functions
Impulse response functions (IRFs) are a standard tool for analyzing the short run dynamics of dynamic macroeconomic models, such as the Solow growth model, in r... | Python Code:
# use tab completion to see the available attributes and methods...
solowpy.impulse_response.ImpulseResponse.
Explanation: 5. Impulse response functions
Impulse response functions (IRFs) are a standard tool for analyzing the short run dynamics of dynamic macroeconomic models, such as the Solow growth model... |
9,079 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Applied example of scraping the Handbook of Birds of the World to get a list of subspecies for a given bird species.
Step1: Introspection of the source HTML of the species web page reveals ... | Python Code:
#Import modules
import requests
from bs4 import BeautifulSoup
#Example URL
theURL = "https://www.hbw.com/species/brown-wood-owl-strix-leptogrammica"
#Get content of the species web page
response = requests.get(theURL)
#Convert to a "soup" object, which BS4 is designed to work with
soup = BeautifulSoup(resp... |
9,080 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Cadenas o strings
En Python las cadenas son definidas como listas de caracteres, por lo que es posible aplicarles rebanado y las demás operaciones que vimos en la sección anterior.
Una caden... | Python Code:
fruta = "banano"
dulce = 'bocadillo'
Explanation: Cadenas o strings
En Python las cadenas son definidas como listas de caracteres, por lo que es posible aplicarles rebanado y las demás operaciones que vimos en la sección anterior.
Una cadena se puede formar usando comillas dobles o sencillas, de la siguien... |
9,081 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Visualize channel over epochs as an image
This will produce what is sometimes called an event related
potential / field (ERP/ERF) image.
Two images are produced, one with a good channel and ... | Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
Explanation: Visualize channel over epochs as an image
This wi... |
9,082 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Homework 2
Step1: If you get an error stating that database "homework2" does not exist, make sure that you followed the instructions above exactly. If necessary, drop the database you creat... | Python Code:
import pg8000
conn = pg8000.connect(database="homework2")
Explanation: Homework 2: Working with SQL (Data and Databases 2016)
This homework assignment takes the form of an IPython Notebook. There are a number of exercises below, with notebook cells that need to be completed in order to meet particular crit... |
9,083 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Analysis_v3 requires
Step1: Create processing pipeline
<br> <br>
Requires
Step2: SweepPoints
Step3: Measured-objects-sweep-points map
Step4: Ramsey
single qubit
Step5: Create pipeline
S... | Python Code:
from pycqed.analysis_v3.processing_pipeline import ProcessingPipeline
# [
# {'node_name1': function_name1, keys_in: keys_in_list1, **node_params1},
# {'node_name2': function_name2, keys_in: keys_in_list2, **node_params2},
# .
# .
# .
# {'node_nameN': function_nameN, keys_in: keys_in... |
9,084 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Python Tutorial
Eine minimale Einführung in Python für Studierende mit Programmiererfahrung die keinen Anspruch auf Vollständigkeit erhebt.
Ausführlichere Einführungen und Tutorials finden s... | Python Code:
print('Hello, world!')
Explanation: Python Tutorial
Eine minimale Einführung in Python für Studierende mit Programmiererfahrung die keinen Anspruch auf Vollständigkeit erhebt.
Ausführlichere Einführungen und Tutorials finden sich an zahlreichen Stellen im Internet. Beispielsweise
Learn Python the Hard Way
... |
9,085 | Given the following text description, write Python code to implement the functionality described.
Description:
Maximize sum of absolute difference between adjacent elements in Array with sum K
Function for maximising the sum ; Difference is 0 when only one element is present in array ; Difference is K when two elements... | Python Code:
def maxAdjacentDifference(N , K ) :
if(N == 1 ) :
return 0 ;
if(N == 2 ) :
return K ;
return 2 * K ;
N = 6 ;
K = 11 ;
print(maxAdjacentDifference(N , K ) ) ;
|
9,086 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Converting incoming CDX files to Parquet
Quick look at file sizes
Step1: Note
Step2: Load in the unzipped file, filetering out any line that starts with a blank or has essentially no conte... | Python Code:
!ls -lh eot2012_surt_index.cdx*
Explanation: Converting incoming CDX files to Parquet
Quick look at file sizes:
End of explanation
!gunzip eot2012_surt_index.cdx.gz
Explanation: Note: Spark can typically load *.gz files just fine, but that support comes from Hive integration, which seems to be missing here... |
9,087 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Guide To Encoding Categorical Values in Python
Supporting notebook for article on Practical Business Python.
Import the pandas, scikit-learn, numpy and category_encoder libraries.
Step1: Ne... | Python Code:
import pandas as pd
import numpy as np
from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder
from sklearn.compose import make_column_transformer
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import cross_val_score
import ... |
9,088 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
What is the equivalent of R's ecdf(x)(x) function in Python, in either numpy or scipy? Is ecdf(x)(x) basically the same as: | Problem:
import numpy as np
grades = np.array((93.5,93,60.8,94.5,82,87.5,91.5,99.5,86,93.5,92.5,78,76,69,94.5,
89.5,92.8,78,65.5,98,98.5,92.3,95.5,76,91,95,61))
def ecdf_result(x):
xs = np.sort(x)
ys = np.arange(1, len(xs)+1)/float(len(xs))
return ys
result = ecdf_result(grades) |
9,089 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Iterators and Generators Homework
Problem 1
Create a generator that generates the squares of numbers up to some number N.
Step1: Problem 2
Create a generator that yields "n" random numbers ... | Python Code:
def gensquares(N):
for i in range(N):
yield i**2
for x in gensquares(10):
print x
Explanation: Iterators and Generators Homework
Problem 1
Create a generator that generates the squares of numbers up to some number N.
End of explanation
import random
random.randint(1,10)
def rand_num(low,hi... |
9,090 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Ritz method for a beam
November, 2018
We want to find a Ritz approximation of the deflection $w$ of a beam under applied
transverse uniform load of intensity $f$ per unit lenght and an end m... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
from sympy import *
%matplotlib notebook
init_printing()
# Graphics setup
gray = '#757575'
plt.rcParams["mathtext.fontset"] = "cm"
plt.rcParams["text.color"] = gray
plt.rcParams["font.size"] = 12
plt.rcParams["xtick.color"] = gray
plt.rcParams["ytick.color... |
9,091 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Chapter 4 – Training Linear Models
This notebook contains all the sample code and solutions to the exercises in chapter 4.
Setup
First, let's make sure this notebook works well in both pytho... | Python Code:
# To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
# to make this notebook's output stable across runs
np.random.seed(42)
# To plot pretty figures
%matplotlib inline
import matplotlib
import matplotlib.pypl... |
9,092 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Autoencoder + UMAP
This notebook extends the last notebook to train the embedding jointly on the reconstruction loss, and UMAP loss, resulting in slightly better reconstructions, and a sligh... | Python Code:
from tensorflow.keras.datasets import mnist
(train_images, Y_train), (test_images, Y_test) = mnist.load_data()
train_images = train_images.reshape((train_images.shape[0], -1))/255.
test_images = test_images.reshape((test_images.shape[0], -1))/255.
Explanation: Autoencoder + UMAP
This notebook extends the l... |
9,093 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Image embeddings in BigQuery for image similarity and clustering tasks
This notebook shows how to do use a pre-trained embedding as a vector representation of an image in Google Cloud Storag... | Python Code:
BUCKET='ai-analytics-solutions-kfpdemo' # CHANGE to a bucket you own
Explanation: Image embeddings in BigQuery for image similarity and clustering tasks
This notebook shows how to do use a pre-trained embedding as a vector representation of an image in Google Cloud Storage.
Given this embedding, we can lo... |
9,094 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Forte Tutorial 1.02
Step2: First we will run psi4 using the function forte.utils.psi4_scf
Step3: Reading options
Step4: Setting the molecular orbital spaces
Step5: Building a ForteIntegr... | Python Code:
import psi4
import forte
import forte.utils
Explanation: Forte Tutorial 1.02: Forte's determinant class
In this tutorial we are going to explore how to create a simple FCI code using forte's Python API.
Import modules
Here we import forte.utils bto access functions to directly run an SCF computation in psi... |
9,095 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introducing the Keras Sequential API
Learning Objectives
1. Build a DNN model using the Keras Sequential API
1. Learn how to use feature columns in a Keras model
1. Learn how to train ... | Python Code:
import datetime
import os
import shutil
import numpy as np
import pandas as pd
import tensorflow as tf
from matplotlib import pyplot as plt
from tensorflow import keras
from tensorflow.keras.callbacks import TensorBoard
from tensorflow.keras.layers import Dense, DenseFeatures
from tensorflow.keras.models i... |
9,096 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1> Machine Learning using tf.estimator </h1>
In this notebook, we will create a machine learning model using tf.estimator and evaluate its performance. The dataset is rather small (7700 s... | Python Code:
# Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.6
import tensorflow as tf
import pandas as pd
import numpy as np
import shutil
print(tf.__version__)
Explanation: <h1> Machine Learning using tf.estimator </h1>
In this notebook, we will create a machine learning model ... |
9,097 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Test datasets
http
Step1: General guides to Bayesian regression
http | Python Code:
import pandas as pd
import statsmodels.api as sm
# Normal response variable
stackloss_conversion = sm.datasets.get_rdataset("stackloss", "datasets")
#print (stackloss_conversion.__doc__)
# Lognormal response variable
engel_food = sm.datasets.engel.load_pandas()
#print (engel_food.data)
# Binary response va... |
9,098 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Building a Regression Model for a Financial Dataset
In this notebook, you will build a simple linear regression model to predict the closing AAPL stock price. The lab objectives are
Step1: ... | Python Code:
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
!pip install --user google-cloud-bigquery==1.25.0
Explanation: Building a Regression Model for a Financial Dataset
In this notebook, you will build a simple linear regression model to predict the closing AAPL stock price. The lab objectives... |
9,099 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Dense Sentiment Classifier
In this notebook, we build a dense neural net to classify IMDB movie reviews by their sentiment.
Load dependencies
Step1: Set hyperparameters
Step2: Load data
Fo... | Python Code:
import keras
from keras.datasets import imdb
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Dense, Flatten, Dropout
from keras.layers import Embedding # new!
from keras.callbacks import ModelCheckpoint # new!
import os # new!
from sklea... |
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