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11,100 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Snapshotting with Devito using the ConditionalDimension
This notebook intends to introduce new Devito users (especially with a C or FORTRAN background) to the best practice on saving snapsho... | Python Code:
#NBVAL_IGNORE_OUTPUT
%reset -f
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Snapshotting with Devito using the ConditionalDimension
This notebook intends to introduce new Devito users (especially with a C or FORTRAN background) to the best practice on saving snapshots... |
11,101 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Corrupt known signal with point spread
The aim of this tutorial is to demonstrate how to put a known signal at a
desired location(s) in a
Step1: First, we set some parameters.
Step2: Load... | Python Code:
import os.path as op
import numpy as np
import mne
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, apply_inverse
from mne.simulation import simulate_stc, simulate_evoked
Explanation: Corrupt known signal with point spread
The aim of this tutorial is to demonstrate how to... |
11,102 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ensegment
Step1: Documentation
Write some beautiful documentation of your program here. | Python Code:
from default import *
Explanation: ensegment: default program
End of explanation
Pw = Pdist(data=datafile("data/count_1w.txt"))
segmenter = Segment(Pw)
with open("data/input/dev.txt") as f:
for line in f:
print(" ".join(segmenter.segment(line.strip())))
Explanation: Documentation
Write some bea... |
11,103 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Feature Selection
Step1: Data Split
Idealy, we'd perform stratified 5x4 fold cross validation, however, given the timeframe, we'll stick with a single split. We'll use an old chunck of data... | Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import bokeh
from bokeh.io import output_notebook
output_notebook()
import os
DATA_STREETLIGHT_CASES_URL = 'https://data.sfgov.org/api/views/c53t-rr3f/rows.json?accessType=DOWNLOAD'
DATA_STREETLI... |
11,104 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: Defining network architecture (we use Arch-2)
We also define some functions to make training convinent here.
Step2: Mounting folder from Google Drive
Step3: Verify t... | Python Code:
# This program will not generate the jet images, it will only train the autoencoder
# and evaluate the results. The jet images can be found in:
# https://drive.google.com/drive/folders/1i5DY9duzDuumQz636u5YQeYQEt_7TYa8?usp=sharing
# Please download those images to your google drive and use the colab - driv... |
11,105 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Plot single trial activity, grouped by ROI and sorted by RT
This will produce what is sometimes called an event related
potential / field (ERP/ERF) image.
The EEGLAB example file, which cont... | Python Code:
# Authors: Jona Sassenhagen <jona.sassenhagen@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne.event import define_target_events
from mne.channels import make_1020_channel_selections
print(__doc__)
Explanation: Plot single trial activity, grouped by ROI and sorted by RT
This will produce what is ... |
11,106 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Examples
Step1: Building on our discussion of modules from last week, we'll use the my_dataset module that I have prepared as a basis. This module is largely identical to what we have buil... | Python Code:
%matplotlib inline
Explanation: Examples: Week 7
This week, we will apply some of our discussions around filtering, splitting and so on to build out comparisons between different variables within the World Bank Economic Indicators dataset.
This dataset, which covers 1960-2016, 264 countries and 1452 variab... |
11,107 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
MMTL Basics Tutorial
The purpose of this tutorial is to introduce the basic classes and flow of the MMTL package within Snorkel MeTaL (not necessarily to motivate or explain multi-task learn... | Python Code:
# Confirm we can import from metal
import sys
sys.path.append('../../metal')
import metal
# Import other dependencies
import torch
import torch.nn as nn
import torch.nn.functional as F
# Set random seed for notebook
SEED = 123
%load_ext autoreload
%autoreload 2
%matplotlib inline
Explanation: MMTL Basics T... |
11,108 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Errors and Exceptions
While executing a python program we may encounter errors. There are 2 types of errors
Step1: Exceptions
Step2: Built-in Exceptions
Python creates an Exception object ... | Python Code:
print('Hello)
Explanation: Errors and Exceptions
While executing a python program we may encounter errors. There are 2 types of errors:
Syntax Errors - When you don't follow the proper structure of the python program (Like missing a quote during initialising a string).
Exceptions - Sometimes even when the ... |
11,109 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Log-Normal or Over-Dispersed Poisson?
We replicate the empirical applications in Harnau (2018a) in Section 2 and Section 6.
The work on this vignette was supported by the European Research C... | Python Code:
import apc
# Turn off FutureWarnings
import warnings
warnings.simplefilter('ignore', FutureWarning)
Explanation: Log-Normal or Over-Dispersed Poisson?
We replicate the empirical applications in Harnau (2018a) in Section 2 and Section 6.
The work on this vignette was supported by the European Research Counc... |
11,110 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2018 The TensorFlow Authors.
Step1: Treine sua primeira rede neural
Step2: Importe a base de dados Fashion MNIST
Esse tutorial usa a base de dados Fashion MNIST que contém 70,000... | 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... |
11,111 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
In this analysis report I would like to find some patterns or characteristics that make some players the best.<br>
After analysing the data I will use data analysis and statisti... | Python Code:
# import libraries
import os
import sys
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from IPython.display import display
%pylab inline
from bokeh.io import output_notebook, show
from bkcharts import Donut
output_notebook()
Explanation: Introduction
In this an... |
11,112 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Project Euler
Step2: Now write a set of assert tests for your number_to_words function that verifies that it is working as expected.
Step4: Now define a count_letters(n) that return... | Python Code:
def number_to_words(n):
Given a number n between 1-1000 inclusive return a list of words for the number.
x = []
a = {1:'one',2:'two',3:'three',4:'four',5:'five',6:'six',7:'seven',8:'eight',9:'nine',10:'ten',
11:'eleven',12:'twelve',13:'thirteen',14:'fourteen',15:'fifteen',16:'sixteen',... |
11,113 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
automaton.shuffle(a1, ...)
The (accessible part of the) shuffle product of automata.
Preconditions
Step1: Boolean Automata
The shuffle product of automata computes the shuffling of their la... | Python Code:
import vcsn
Explanation: automaton.shuffle(a1, ...)
The (accessible part of the) shuffle product of automata.
Preconditions:
- all the labelsets are letterized
See also:
- automaton.conjunction
- automaton.infiltration
- expression.shuffle
Examples
End of explanation
std = lambda exp: vcsn.B.expression(exp... |
11,114 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Working with Streaming Data
Learning Objectives
1. Learn how to process real-time data for ML models using Cloud Dataflow
2. Learn how to serve online predictions using real-time data
Intr... | Python Code:
import numpy as np
import os
import shutil
import tensorflow as tf
from google.cloud import aiplatform
from google.cloud import bigquery
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
from matplotlib import pyplot as plt
from tensorflow import keras
from tensorflow.ker... |
11,115 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Part 2 - Core Query Builder Functions
Step1: Query builders
match_field
Forge has many helper functions to make constructing queries easier. The simplest of the helpers is match_field().
To... | Python Code:
from mdf_forge.forge import Forge
mdf = Forge()
Explanation: Part 2 - Core Query Builder Functions
End of explanation
mdf.match_field("material.elements", "Al")
Explanation: Query builders
match_field
Forge has many helper functions to make constructing queries easier. The simplest of the helpers is match_... |
11,116 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Evaluation of classified contacts between rods and bipolar cells
This notebook contains the code to reproduce all plots in figure 6 showing statistics about the rod-BC contacts
Step1: Numbe... | Python Code:
import numpy as np
import scipy.linalg
from scipy.stats import itemfreq
import matplotlib
import matplotlib.pyplot as plt
from scipy.io import loadmat
import pandas as pd
import seaborn as sns
from sklearn import cross_validation
from sklearn import svm
%matplotlib inline
matplotlib.rc('font',**{'family':'... |
11,117 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
FICHEROS
En Python, para abrir un fichero usaremos la función open, que recibe el nombre del archivo a abrir. Por defecto, si no indicamos nada, el fichero se abre en modo lectura.
OPEN
Step... | Python Code:
%pwd
fichero = open("../datos/cuna.txt")
Explanation: FICHEROS
En Python, para abrir un fichero usaremos la función open, que recibe el nombre del archivo a abrir. Por defecto, si no indicamos nada, el fichero se abre en modo lectura.
OPEN: MODO LECTURA
End of explanation
ls "../datos"
fichero= open("../da... |
11,118 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Part3
Using the models.ldamodel module from the gensim library, run topic modeling over the corpus. Explore different numbers of topics (varying from 5 to 50), and settle for the parameter w... | Python Code:
# imports
import pandas as pd
import numpy as np
from nltk.corpus import stopwords
from gensim import corpora, models, utils
from nltk.stem import WordNetLemmatizer
data = pd.read_csv('hillary-clinton-emails/Emails.csv', index_col=0).dropna()
texts = pd.concat((data.ExtractedBodyText ,data.ExtractedSubject... |
11,119 | 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', 'ncc', 'noresm2-mm', 'land')
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: NCC
Source ID: NORESM2-MM
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energ... |
11,120 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Examples of plots and calculations using the tmm and colorpy package
This example uses tmm and colorpy package to calculate the surface of stacked layers.
Note that tmm and colorpy packages ... | Python Code:
from __future__ import division, print_function, absolute_import
%load_ext autoreload
%autoreload 2
from pypvcell.tmm_core import (coh_tmm, unpolarized_RT, ellips, absorp_in_each_layer,
position_resolved, find_in_structure_with_inf)
from numpy import pi, linspace, inf, array
import n... |
11,121 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1. Party game
Step1: 'numbers' is a list of lists. Using a list comprehension, flatten 'numbers' so it is a list of only numbers (not list of lists).
use the newly flattened 'numbers' and f... | Python Code:
numbers = [[1,2,3],[4,5,6],[7,8,9]]
words = ['if','i','could','just','go','outside','and','have','an','ice','cream']
Explanation: 1. Party game: squeezed
One guessing game, called “squeezed”, is very common in parties. It consists of a player,
the chooser, who writes down a number between 00–99. The other ... |
11,122 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Mesh examples
this notebook illustrates the basic ways of interacting with the pyro2 mesh module. We create some data that lives on a grid and show how to fill the ghost cells. The pretty_... | Python Code:
from __future__ import print_function
import numpy as np
import mesh.boundary as bnd
import mesh.patch as patch
import matplotlib.pyplot as plt
%matplotlib inline
# for unit testing, we want to ensure the same random numbers
np.random.seed(100)
Explanation: Mesh examples
this notebook illustrates the basic... |
11,123 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Reversible (Diffusion-limited)
This is for an integrated test of E-Cell4. Here, we test a simple reversible association/dissociation model in volume.
Step1: Parameters are given as follows.... | Python Code:
%matplotlib inline
from ecell4.prelude import *
Explanation: Reversible (Diffusion-limited)
This is for an integrated test of E-Cell4. Here, we test a simple reversible association/dissociation model in volume.
End of explanation
D = 1
radius = 0.005
N_A = 60
U = 0.5
ka_factor = 10 # 10 is for diffusion-l... |
11,124 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Homework Part 2
Step1: Load 120 seconds of an audio file
Step2: Plot the time-domain waveform of the audio signal
Step3: Play the audio file
Step4: Step 2
Step5: Scale the features to h... | Python Code:
filename1 = 'brahms_hungarian_dance_5.mp3'
url = "http://audio.musicinformationretrieval.com/" + filename1
if not os.path.exists(filename1):
urllib.urlretrieve(url, filename=filename1)
Explanation: Homework Part 2: Genre Classification
Goals
Extract features from an audio signal.
Train a genre classifi... |
11,125 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: PWC-Net-small model training (with multisteps learning rate schedule)
In this notebook, we
Step2: TODO
Step3: Pre-train on FlyingChairs+FlyingThings3DHalfRes mix
Load the dataset
St... | Python Code:
pwcnet_train.ipynb
PWC-Net model training.
Written by Phil Ferriere
Licensed under the MIT License (see LICENSE for details)
Tensorboard:
[win] tensorboard --logdir=E:\\repos\\tf-optflow\\tfoptflow\\pwcnet-sm-6-2-multisteps-chairsthingsmix
[ubu] tensorboard --logdir=/media/EDrive/repos/tf-optflow/t... |
11,126 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Fitting a diagonal covariance Gaussian mixture model to text data
In a previous assignment, we explored k-means clustering for a high-dimensional Wikipedia dataset. We can also model this da... | Python Code:
import graphlab
Explanation: Fitting a diagonal covariance Gaussian mixture model to text data
In a previous assignment, we explored k-means clustering for a high-dimensional Wikipedia dataset. We can also model this data with a mixture of Gaussians, though with increasing dimension we run into two importa... |
11,127 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Explicit feedback movie recommendations
In this example, we'll build a quick explicit feedback recommender system
Step1: The dataset object is an instance of an Interactions class, a fairly... | Python Code:
import numpy as np
from spotlight.datasets.movielens import get_movielens_dataset
dataset = get_movielens_dataset(variant='100K')
print(dataset)
Explanation: Explicit feedback movie recommendations
In this example, we'll build a quick explicit feedback recommender system: that is, a model that takes into a... |
11,128 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Anna KaRNNa
In this notebook, we'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book... | Python Code:
import time
from collections import namedtuple
import re
import numpy as np
import tensorflow as tf
Explanation: Anna KaRNNa
In this notebook, we'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
Th... |
11,129 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Notebook 6
Step1: Download the sequence data
Sequence data for this study are archived on the NCBI sequence read archive (SRA). Below I read in SraRunTable.txt for this project which contai... | Python Code:
### Notebook 6
### Data set 6 (Finches)
### Authors: DaCosta & Sorenson (2016)
### Data Location: SRP059199
Explanation: Notebook 6:
This is an IPython notebook. Most of the code is composed of bash scripts, indicated by %%bash at the top of the cell, otherwise it is IPython code. This notebook includes co... |
11,130 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Ocnbgchem
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', 'cmcc', 'cmcc-esm2-hr5', 'ocnbgchem')
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: CMCC
Source ID: CMCC-ESM2-HR5
Topic: Ocnbgchem
Sub-Topics: Tracer... |
11,131 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Aiida and the aiida-plugins
1. aiida-v0.12.1 installation (released in Jan 2018)
aiida-v0.12.1 was released in Summer, 2018, hence I removed the previous v0.11.0 in my mac, and installed the... | Python Code:
conda create -n aiida-debug python=2.7 #set a veritual environment
conda activate aiida-debug
#sometimes in mac, such a command might be requested
# sudo ln -s /Users/ywfang/miniconda3/etc/profile.d/conda.sh /etc/profile.d/conda.sh
conda install postgresql
Explanation: Aiida and the aiida-plugins
1. aiida-... |
11,132 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Section 6.5.1
Hantush wells introduction type curves
IHE, module transient groundwater
Olsthoorn, 2019-01-03
Hantush (1956) considered the transient flow due to a well with a constant extrac... | Python Code:
from scipy.special import exp1
from scipy.integrate import quad
import numpy as np
import matplotlib.pyplot as plt
Explanation: Section 6.5.1
Hantush wells introduction type curves
IHE, module transient groundwater
Olsthoorn, 2019-01-03
Hantush (1956) considered the transient flow due to a well with a cons... |
11,133 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Topological Sorting
The function topo_sort implements <em style="color
Step1: Graphical Representation
Step2: The function toDot(Edges, Order) takes two arguments
Step3: Testing | Python Code:
def topo_sort(T, D):
Parents = { t: set() for t in T } # dictionary of parents
Children = { t: set() for t in T } # dictionary of children
for s, t in D:
Children[s].add(t)
Parents [t].add(s)
Orphans = { t for (t, P) in Parents.items() if len(P) == 0 }
Sorted = []
... |
11,134 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
그래프를 그리기 위해서 matplotlib을 임포트 합니다. %matplotlib inline은 새로운 창을 띄우지 않고 주피터 노트북 안에 이미지를 삽입하여 줍니다.
Step1: 텐서플로우를 tf 란 이름으로 임포트 하세요.
tf.Session()을 사용하여 세션 객체를 하나 만드세요.
sess = tf.Session()
임의의 샘플 ... | Python Code:
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: 그래프를 그리기 위해서 matplotlib을 임포트 합니다. %matplotlib inline은 새로운 창을 띄우지 않고 주피터 노트북 안에 이미지를 삽입하여 줍니다.
End of explanation
x_raw = ...
x = ...
Explanation: 텐서플로우를 tf 란 이름으로 임포트 하세요.
tf.Session()을 사용하여 세션 객체를 하나 만드세요.
sess = tf.Session()
임의의 샘플 데이터를 만들려고... |
11,135 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Visualize Source time courses
This tutorial focuses on visualization of stcs.
Surface Source Estimates
First, we get the paths for the evoked data and the time courses (stcs).
Step1: Then, ... | Python Code:
import os
import mne
from mne.datasets import sample
from mne.minimum_norm import apply_inverse, read_inverse_operator
from mne import read_evokeds
data_path = sample.data_path()
sample_dir = os.path.join(data_path, 'MEG', 'sample')
subjects_dir = os.path.join(data_path, 'subjects')
fname_evoked = data_pat... |
11,136 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Fitting a Mixture Model with Gibbs Sampling
Step1: Suppose we receive some data that looks like the following
Step2: It appears that these data exist in three separate clusters. We want to... | Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
from scipy import stats
from collections import namedtuple, Counter
Explanation: Fitting a Mixture Model with Gibbs Sampling
End of explanation
data = pd.Series.from_csv("clusters.csv")
_=data.hist(bins=... |
11,137 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Epoching and averaging (ERP/ERF)
Step1: In MNE, epochs refers to a collection of single trials or short segments
of time locked raw data. If you haven't already, you might want to check out... | Python Code:
import os.path as op
import numpy as np
import mne
Explanation: Epoching and averaging (ERP/ERF)
End of explanation
data_path = mne.datasets.sample.data_path()
fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(fname)
raw.set_eeg_reference('average', projection=T... |
11,138 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The Inspection Paradox is Everywhere
Allen Downey 2019
MIT License
Step1: Class size
Here's the data summarizing the distribution of undergraduate class sizes at Purdue University in 2013-1... | Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from empiricaldist import Pmf
from utils import decorate
# set the random seed so we get the same results every time
np.random.seed(17)
# make the directory for the figures
import os
if not os.path.exists('inspecti... |
11,139 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="http
Step1: Molecules
Let's start with something relaxing.
Execute each cell by selecting it and pressing Shift + Return
Step2: You've just created your first molecule in buckybal... | Python Code:
# This cell sets up both the python and notebook environments
%matplotlib inline
import moldesign as mdt # import the buckyball package
from moldesign import units as u # import the buckyball unit system
Explanation: <a href="http://moldesign.bionano.autodesk.com" target="_blank"><img src="img/Top.png"... |
11,140 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
神经网络算法实现的核心之一是对代价函数的反向求导，Theano和Tensorflow中都定义了求导的符号函数，同样地，作为深度学习平台，自动求导（autograd）功能在pytorch中也扮演着核心功能，不同的是，pytorch的动态图功能使其更灵活（define by run）， 比如甚至在每次迭代中都可以通过改变pytorch中Variable的属性，从而使其加入亦或退出反... | Python Code:
x = Variable(T.ones(2,2), requires_grad=True)
print x
y = T.exp(x + 2)
yy = T.exp(-x-2)
print y
z = (y + yy)/2
out = z.mean()
print z, out
make_dot(out)
out.backward(T.FloatTensor(1), retain_graph=True)
x.grad
T.randn(1,1)
from __future__ import print_function
xx = Variable(torch.randn(1,1), requires_grad ... |
11,141 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
6차시
Step1: 2. 학습 데이터 불러오기
Step2: 3. 학습 데이터 살펴보기
Step3: 4. Convolution Neural Network
Step4: 5. Transfer Learning
Step5: 6. Transfer Learning Finetune | Python Code:
# 도구 준비
import os
import random
import tensorflow as tf # 텐서플로우
import tensorflow_hub as hub
import matplotlib.pyplot as plt # 시각화 도구
%matplotlib inline
import numpy as np
import PIL.Image as Image
print(f'Tensorflow 버전을 확인합니다: {tf.__version__}')
Explanation: 6차시: 텐서플로우 2.x 활용 전이 학습 이미지 분류
AI 맛보기 6주차: 2020... |
11,142 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
OKCupid Clean Data
OKCupid's website returns some partially hidden text when it is too long for their layout.
Lets skip these and just focus on the fully named places.
Step1: Feature
Step2:... | Python Code:
%matplotlib inline
import time
import pylab
import numpy as np
import pandas as pd
import pycupid.locations
people = pd.read_json('/Users/ajmendez/data/okcupid/random.json')
print('Scraping archive found {:,d} random people'.format(len(people)))
Explanation: OKCupid Clean Data
OKCupid's website returns som... |
11,143 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
OP2 Demo
The iPython notebook for this demo can be found in
Step1: Sets default precision of real numbers for pandas output
Step2: As with the BDF, we can use the long form and the short f... | Python Code:
import os
import copy
import numpy as np
import pyNastran
pkg_path = pyNastran.__path__[0]
from pyNastran.utils import print_bad_path
from pyNastran.op2.op2 import read_op2
from pyNastran.utils import object_methods, object_attributes
import pandas as pd
Explanation: OP2 Demo
The iPython notebook for this ... |
11,144 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Ch 11
Step1: First, define the constants.
Let's say we're dealing with 1-dimensional vectors, and a maximum sequence size of 3.
Step2: Next up, define the placeholder(s).
We only need on... | Python Code:
import tensorflow as tf
Explanation: Ch 11: Concept 01
Multi RNN
All we need is TensorFlow:
End of explanation
input_dim = 1
seq_size = 3
Explanation: First, define the constants.
Let's say we're dealing with 1-dimensional vectors, and a maximum sequence size of 3.
End of explanation
input_placeholder = t... |
11,145 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
Step1: What is Data?
A dataset consists of multiple data rows.
Each row describes an item with its features.
Think of features as properties of a sample. E.g. an apple has colo... | Python Code:
# importing numpy, pandas & matplotlib
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import random
%matplotlib inline
Explanation: Introduction
End of explanation
# Load Iris dataset
from sklearn.datasets import load_iris
iris = load_iris()
iris.feature_names
print(iris.DESCR)... |
11,146 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Neural net painter
This notebook demonstrates a fun experiment in training a neural network to do regression from the color (r,g,b) of a pixel in an image, given its (x,y) position in the im... | Python Code:
%matplotlib inline
import time
from PIL import Image
import numpy as np
import keras
from matplotlib.pyplot import imshow, figure
from keras.models import Sequential
from keras.layers import Dense
Explanation: Neural net painter
This notebook demonstrates a fun experiment in training a neural network to do... |
11,147 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Author
Step1: Preprocess Data
For protyping I randomly sampled 10% the original dataset w/ this command
Step2: Data preprocessing
Step3: Categorical Features
Since all the features are ca... | Python Code:
import pandas as pd
import numpy as np
%pylab inline
import matplotlib.pyplot as plt
Explanation: Author: Alex Egg
This is my submission for the Machine Learning Scientist role at Amazon Development Centre (Scotland). I spent about 2 hours on it.
Intro
My old professor at UCSD, Yoav Freund, is one of the o... |
11,148 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Interact Exercise 2
Imports
Step1: Plotting with parameters
Write a plot_sin1(a, b) function that plots $sin(ax+b)$ over the interval $[0,4\pi]$.
Customize your visualization to make it eff... | Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
Explanation: Interact Exercise 2
Imports
End of explanation
import math as math
def plot_sine1(a, b):
x = np.linspace(0,4*math.pi,... |
11,149 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Making a movie of reaction-diffusion concentrations
We recommend creating and using a virtual environment for NetPyNE tutorials. To do so, enter the following commands into your terminal
St... | Python Code:
plotArgs = {
'speciesLabel': 'ca',
'regionLabel' : 'ecs',
'saveFig' : 'movie',
'showFig' : False,
'clim' : [1.9997, 2.000],
}
Explanation: Making a movie of reaction-diffusion concentrations
We recommend creating and using a virtual environment for NetPyNE tutorials. To... |
11,150 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Learning from Data
Decision Trees are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target va... | Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('fivethirtyeight')
df = pd.read_csv("data/creditRisk.csv")
df.head()
df.dtypes
Explanation: Learning from Data
Decision Trees are a non-parametric supervised learning method used for classification and r... |
11,151 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2018 The TensorFlow Authors.
Step1: 使用 SavedModel 格式
<table class="tfo-notebook-buttons" align="left">
<td data-segment-approved="false"><a target="_blank" href="https
Step2: 我... | 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... |
11,152 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Regression Week 1
Step1: Load house sales data
Dataset is from house sales in King County, the region where the city of Seattle, WA is located.
Step2: Split data into training and testing
... | Python Code:
import sys
sys.path.append('C:\Anaconda2\envs\dato-env\Lib\site-packages')
import graphlab
Explanation: Regression Week 1: Simple Linear Regression
In this notebook we will use data on house sales in King County to predict house prices using simple (one input) linear regression. You will:
* Use graphlab SA... |
11,153 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Segmentation Evaluation <a href="https
Step2: Utility method for display
Step3: Fetch the data
Retrieve a single CT scan and three manual delineations of a liver tumor. Visual inspection o... | Python Code:
import SimpleITK as sitk
import numpy as np
%run update_path_to_download_script
from downloaddata import fetch_data as fdata
%matplotlib inline
import matplotlib.pyplot as plt
import gui
from ipywidgets import interact, fixed
Explanation: Segmentation Evaluation <a href="https://mybinder.org/v2/gh/InsightS... |
11,154 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Objective
In this notebook I am testing a reduced workflow that will
Step1: Import test image. The colormap is Matlab's Jet
Step2: Reduce number of colours
I use here Scikit-learn segmenta... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
from skimage import data, io, segmentation, color
from skimage.future import graph
%matplotlib inline
import requests
from PIL import Image
from io import StringIO
Explanation: Objective
In this notebook I am testing a reduced workflow that will:
1) input ... |
11,155 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
W3C prov based provenance storage in Neo4j
This notebook tries to provides a nearly complete mapping between a W3C prov standard based provenance descriptions and a Neo4j graph representatio... | Python Code:
from IPython.display import display, Image
Image(filename='key-concepts.png')
Explanation: W3C prov based provenance storage in Neo4j
This notebook tries to provides a nearly complete mapping between a W3C prov standard based provenance descriptions and a Neo4j graph representation.
The approach taken is a... |
11,156 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2021 The TensorFlow Authors.
Step1: Object Detection with TensorFlow Lite Model Maker
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
S... | 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... |
11,157 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Advanced
Step1: As always, let's do imports and initialize a logger and a new Bundle.
Step2: Changing Hierarchies
Some of the built-in constraints depend on the system hierarchy, and will ... | Python Code:
#!pip install -I "phoebe>=2.4,<2.5"
Explanation: Advanced: Constraints and Changing Hierarchies
Setup
Let's first make sure we have the latest version of PHOEBE 2.4 installed (uncomment this line if running in an online notebook session such as colab).
End of explanation
import phoebe
from phoebe import u ... |
11,158 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Time series prediction using RNNs + Estimators
This notebook illustrates how to
Step1: Describing the data set and the model
We're using a weather dataset....[DESCRIBE DATA SET, HOW THE DAT... | Python Code:
#!/usr/bin/env python
# Copyright 2017 Google Inc. 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
#
# Un... |
11,159 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Theano
A language in a language
Dealing with weights matrices and gradients can be tricky and sometimes not trivial.
Theano is a great framework for handling vectors, matrices and high dimen... | Python Code:
import theano
import theano.tensor as T
Explanation: Theano
A language in a language
Dealing with weights matrices and gradients can be tricky and sometimes not trivial.
Theano is a great framework for handling vectors, matrices and high dimensional tensor algebra.
Most of this tutorial will refer to Thea... |
11,160 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Content
Glossary
1. Somename
Next
Step1: Import section specific modules | Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
Explanation: Content
Glossary
1. Somename
Next: 1.2 Somename 3
Import standard modules:
End of explanation
pass
Explanation: Import section specific modul... |
11,161 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
VectorView and OPM resting state datasets
Here we compute the resting state from raw for data recorded using
a Neuromag VectorView system and a custom OPM system.
The pipeline is meant to mo... | Python Code:
# sphinx_gallery_thumbnail_number = 14
# Authors: Denis Engemann <denis.engemann@gmail.com>
# Luke Bloy <luke.bloy@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
from mne.filter import next_fast_len
from mayavi import mlab
import mne
pr... |
11,162 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
This notebook demostrates the core functionality of pymatgen, including the core objects representing Elements, Species, Lattices, and Structures.
By convention, we import pyma... | Python Code:
import pymatgen as mg
Explanation: Introduction
This notebook demostrates the core functionality of pymatgen, including the core objects representing Elements, Species, Lattices, and Structures.
By convention, we import pymatgen as mg.
End of explanation
si = mg.Element("Si")
print("Atomic mass of Si is {... |
11,163 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Asymmetric Encryption
Use hard math problems called “Trapdoor Functions”
Example
Step1: Risk
Attacker can use known plaintext and A's public Ke to test a generated private key.
Computation
... | Python Code:
#Lets test this idea...
import cProfile, pstats, StringIO
Prime1=307 #Try some others 7907 15485857 7919 15485863
Prime2=293
#or get some big primes from here:https://primes.utm.edu/
def factors(n):
return set(reduce(list.__add__,
([i, n//i] for i in range(1, int(n**0.5) + 1) if n... |
11,164 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Installation
Follow directions at the PySAL-ArcGIS-Toolbox Git Repository [https
Step1: Example
Step2: Use the PySAL-ArcGIS Utilities to Read in Spatial Weights Files
Step3: Run the Auto ... | Python Code:
import arcpy as ARCPY
import arcgisscripting as ARC
import SSDataObject as SSDO
import SSUtilities as UTILS
import WeightsUtilities as WU
import numpy as NUM
import scipy as SCIPY
import pysal as PYSAL
import os as OS
import pandas as PANDAS
Explanation: Installation
Follow directions at the PySAL-ArcGIS-T... |
11,165 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Analyze Issue Label Bot
This notebook is used to compute metrics to evaluate performance of the issue label bot.
Step2: Setup Authorization
If you are using a service account run
%%bash
Act... | Python Code:
import altair as alt
import collections
import importlib
import logging
import sys
import os
import datetime
from dateutil import parser as dateutil_parser
import glob
import json
import numpy as np
import pandas as pd
from pandas.io import gbq
# A bit of a hack to set the path correctly
sys.path = [os.pat... |
11,166 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Ch 08
Step1: Define an abstract class called DecisionPolicy
Step2: Here's one way we could implement the decision policy, called a random decision policy
Step3: That's a good baseline. No... | Python Code:
%matplotlib inline
from yahoo_finance import Share
from matplotlib import pyplot as plt
import numpy as np
import random
import tensorflow as tf
import random
Explanation: Ch 08: Concept 01
Reinforcement learning
The states are previous history of stock prices, current budget, and current number of shares ... |
11,167 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Genetic Algorithm Workshop
In this workshop we will code up a genetic algorithm for a simple mathematical optimization problem.
Genetic Algorithm is a
* Meta-heuristic
* Inspired by N... | Python Code:
%matplotlib inline
# All the imports
from __future__ import print_function, division
from math import *
import random
import sys
import matplotlib.pyplot as plt
# TODO 1: Enter your unity ID here
__author__ = "sbiswas4"
class O:
Basic Class which
- Helps dynamic updates
- Pretty P... |
11,168 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
PmodTMP2 Sensor example
In this example, the Pmod temperature sensor is initialized and set to log a reading every 1 second.
This examples required the PmodTMP2 sensor, and assumed it is at... | Python Code:
from pynq import Overlay
Overlay("base.bit").download()
from pynq.iop import Pmod_TMP2
from pynq.iop import PMODB
mytmp = Pmod_TMP2(PMODB)
temperature = mytmp.read()
print(str(temperature) + " C")
Explanation: PmodTMP2 Sensor example
In this example, the Pmod temperature sensor is initialized and set to lo... |
11,169 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Logarithmic Parameters
This notebook explores Bayesian optimisation of a function who's parameter is best thought of logarithmically (the order of magnitude is more important than the value ... | Python Code:
%load_ext autoreload
%autoreload 2
from IPython.core.debugger import Tracer # debugging
from IPython.display import clear_output, display
import time
%matplotlib inline
#%config InlineBackend.figure_format = 'svg'
import matplotlib.pyplot as plt
import seaborn as sns; sns.set() # prettify matplotlib
import... |
11,170 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2020 The TensorFlow Authors.
Step1: Fairness Indicators Lineage Case Study
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step2: Down... | 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... |
11,171 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
On this notebook the best models and input parameters will be searched for. The problem at hand is predicting the price of any stock symbol 7 days ahead, assuming one model for all the symbo... | Python Code:
# Basic imports
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
import scipy.optimize as spo
import sys
from time import time
from sklearn.metrics import r2_score, median_absolute_error
%matplotlib inline
%pylab inline
pylab.rcParams['figure.figsize'] ... |
11,172 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2021 The TensorFlow Authors.
Step1: Data validation using TFX Pipeline and TensorFlow Data Validation
Note
Step2: Install TFX
Step3: Did you restart the runtime?
If you are usin... | 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... |
11,173 | Given the following text description, write Python code to implement the functionality described below step by step
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 other by edges or lines. If those edges don... | 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 of vertices or node... |
11,174 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Keywords Stuff
Step1: Documetns with similar sets of keywords should have similar content
a document can be represented by a vector indicating whether a keyword is present or absent for the... | Python Code:
from collections import defaultdict
keycounts = defaultdict(int)
def updateKeycounts(kws):
for kw in kws:
keycounts[kw] += 1
_ = keywords.apply(lambda x: updateKeycounts(x.keywords), axis=1)
keycounts = pandas.DataFrame({"word" : [w for w in keycounts.keys()],
"cou... |
11,175 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
for d in ds[1
Step1: with open("models/bar_models", "rb") as f | Python Code:
# !say "Finished"
eval_complex_model(a)
save_model(a, "attempt_two/zero_models")
Explanation: for d in ds[1:]:
trainerer(a,d[:10], 1000,l_r = 0.006, batches= 5 )
trainerer(a,ds_three[:200], 1000,l_r = 0.002, batches= 1 )
End of explanation
trainerer(a,ds_twos[:100], 10,l_r = 0.002, batches = 5)
vs = Co... |
11,176 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
A Simple Autoencoder
We'll start off by building a simple autoencoder to compress the MNIST dataset. With autoencoders, we pass input data through an encoder that makes a compressed represen... | Python Code:
%matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', validation_size=0)
Explanation: A Simple Autoencoder
We'll start off by building a simple autoencoder to c... |
11,177 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Clustering with KMeans in Shogun Machine Learning Toolbox
Notebook by Parijat Mazumdar (GitHub ID
Step1: The toy data created above consists of 4 gaussian blobs, having 200 points each, cen... | Python Code:
from numpy import concatenate, array
from numpy.random import randn
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
num = 200
d1 = concatenate((randn(1,num),10.*randn(1,num)),0)
d2 = concatenate((randn(1,num),10.*randn(1,num)),0)+array([[10.],[0.]])
d3 = concatenate((randn(1,num),10... |
11,178 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
In this little experiment, I printed the likelihoods after each iteration.
The test case was failing with 10% probability and the history had 10 locations. And I requested a certainty for te... | Python Code:
with open('example_run.csv') as f: s = f.read()
N = 10
runs = [[1/N for _ in range(N)]]
for line in s.split('\n'):
line = line.strip('[]')
if len(line) > 0:
li = [float(i) for i in line.split(',')]
runs.append(li)
Explanation: In this little experiment, I printed the likelihoods aft... |
11,179 | 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... |
11,180 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Examples
reproduced from http
Step1: Example 2
Step2: Example 3
Exception
The function2 plot_with_table1() and plot_with_table2() are exceptions with respect to the idea of this module
Ste... | Python Code:
df = hc.sample.df_timeseries(N=2, Nb_bd=15+0*3700) #<=473
df.info()
display(df.head())
display(df.tail())
g = hc.Highstock()
g.chart.width = 650
g.chart.height = 550
g.legend.enabled = True
g.legend.layout = 'horizontal'
g.legend.align = 'center'
g.legend.maxHeight = 100
g.tooltip.enabled = True
g.tooltip.... |
11,181 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<p>
<img src="http
Step1:
Step2:
Step3: | Python Code:
from itertools import repeat
from sympy import *
#from type_system import *
%run ../../src/commons.py
%run ./type-system.py
Explanation: <p>
<img src="http://www.cerm.unifi.it/chianti/images/logo%20unifi_positivo.jpg"
alt="UniFI logo" style="float: left; width: 20%; height: 20%;">
<div align="righ... |
11,182 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial
Step1: basics
Step2: A print and a plot function are implemented to represent kernel objects.
Step3: Implemented kernels
Many kernels are already implemented in GPy. The follow... | Python Code:
import GPy
import numpy as np
Explanation: Tutorial : A kernel overview
Nicolas Durrande and James Hensman, 2013, 2014
The aim of this tutorial is to give a better understanding of the kernel objects in GPy and to list the ones that are already implemented.
First we import the libraries we will need
End of... |
11,183 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
It Starts with a Dataset
Step1: Transforming Text to Numbers
Example Predictions
Step2: Creating the Input Data
Step3: And now we can initialize our (empty) input layer as vector of 0s. W... | Python Code:
def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r')
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r')
labels = list(map(lambda x:x[:-1].upper(),g.readlines()))
g.close()
reviews[0]
labels[0]
print("... |
11,184 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Create a training set, a test set and a set to predict for
Step1: Inspect the features, I know these features (at leasr spectral indices) are correlated but also have high variance, I could... | Python Code:
features=wisps.INDEX_NAMES
Explanation: Create a training set, a test set and a set to predict for
End of explanation
#remove infinities and nans
def remove_infinities_and_nans(array):
array=np.log10(array)
infinbools=np.isinf(array)
nanbools=np.isnan(array)
mask=np.logical_or(infinbools, n... |
11,185 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Clipper Tutorial
Step1: Extract the images
Now, we must extract the data into a format we can load. This will make use of the provided extract_cifar.py
This dataset has 50,000 training data... | Python Code:
cifar_loc = ""
%run ./download_cifar.py $cifar_loc
Explanation: Clipper Tutorial: Part 1
This tutorial will walk you through the process of starting Clipper, creating and querying a Clipper application, and deploying models to Clipper. In the first part of the demo, you will set up Clipper and create an ap... |
11,186 | Given the following text description, write Python code to implement the functionality described.
Description:
Replace each element of Array with it 's corresponding rank
Function to assign rank to array elements ; Copy input array into newArray ; Sort newArray [ ] in ascending order ; Dictionary to store the rank of t... | Python Code:
def changeArr(input1 ) :
newArray = input1 . copy()
newArray . sort()
ranks = { }
rank = 1
for index in range(len(newArray ) ) :
element = newArray[index ] ;
if element not in ranks :
ranks[element ] = rank
rank += 1
for index in range(len(input1 ) ) :
element = input1[index ]... |
11,187 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Rewriting rules in ReGraph
In the context of ReGraph, by rewriting rules we mean the rules of sesqui-pushout rewriting (see more details here). A rewriting rule consists of the three graphs
... | Python Code:
from regraph import NXGraph, Rule, plot_rule
Explanation: Rewriting rules in ReGraph
In the context of ReGraph, by rewriting rules we mean the rules of sesqui-pushout rewriting (see more details here). A rewriting rule consists of the three graphs: $p$ – preserved part, $lhs$ – left hand side, $rhs$ – righ... |
11,188 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
验证码识别 简单版本
Step1: 生成验证码
Step2: 模型
Step3: 模型一共有36.5万参数。 | Python Code:
import time
import os
from multiprocessing import Pool
from captcha.image import ImageCaptcha
import numpy as np
import skimage.io as io
import tensorflow as tf
import matplotlib.pylab as plt
%matplotlib inline
Explanation: 验证码识别 简单版本
End of explanation
IMG_H = 64
IMG_W = 160
IMG_CHANNALS = 1
CAPTCHA_SIZE ... |
11,189 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Paragraph to mem prototype
Import modules
Step1: Define some constant variables
Chagne path to your books!
all_books
Step2: Harry paragraphs
We define the harryness of a paragraph as the ... | Python Code:
#import sys
#sys.path.append('/Users/michaellomnitz/Documents/CDIPS-AI/pensieve/pensieve')
import pensieve as pens
import textacy
from collections import defaultdict
from random import random
import numpy as np
import matplotlib.pyplot as plt
Explanation: Paragraph to mem prototype
Import modules
End of ex... |
11,190 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Host-guest complex setup and simulation using SMIRNOFF
This notebook takes a SMILES string for a guest and a 3D structure for a host, and generates an initial structure of the complex using ... | Python Code:
# NBVAL_SKIP
from openeye import oechem # OpenEye Python toolkits
import oenotebook as oenb
# Check license
print("Is your OEChem licensed? ", oechem.OEChemIsLicensed())
from openeye import oeomega # Omega toolkit
from openeye import oequacpac #Charge toolkit
from openeye import oedocking # Docking toolkit... |
11,191 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Wet Bulb Calculation Analysis
Common/Helper Methods
Step2: Ranges and Sensor Accuracy
Analysis Assumptions
Step3: Sensor Errors
Step4: Tim Brice and Todd Hall Wet Bulb calculation
... | Python Code:
%matplotlib inline
import math
from numpy import *
import matplotlib.pyplot as plt
from matplotlib import cm
from pylab import *
from operator import itemgetter
import hygrometry
def frange(x, y, jump):
Like range(), but works with floats.
x=float(x)
y=float(y)
jump = float(jump)
while ... |
11,192 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Proof of work for framework migrations
Jupyter Notebook Demo with Python, pandas and matplotlib.
Context
This analysis shows the progress of the rewrite work from Technical Requirement AB311... | Python Code:
import pandas as pd
log = pd.read_csv("../dataset/git_log_refactoring_simple.csv", parse_dates=[3])
log.head()
Explanation: Proof of work for framework migrations
Jupyter Notebook Demo with Python, pandas and matplotlib.
Context
This analysis shows the progress of the rewrite work from Technical Requiremen... |
11,193 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Planar Point Patterns in PySAL
Author
Step1: Creating Point Patterns
From lists
We can build a point pattern by using Python lists of coordinate pairs $(s_0, s_1,\ldots, s_m)$ as follows
St... | Python Code:
import pysal.lib as ps
import numpy as np
from pysal.explore.pointpats import PointPattern
Explanation: Planar Point Patterns in PySAL
Author: Serge Rey sjsrey@gmail.com and Wei Kang weikang9&#... |
11,194 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2020 The TensorFlow Authors.
Step1: Better ML Engineering with ML Metadata
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step2: Inst... | 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... |
11,195 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Working with data files
Reading and writing data files is a common task, and Python offers native support for working with many kinds of data files. Today, we're going to be working mainly w... | Python Code:
import csv
Explanation: Working with data files
Reading and writing data files is a common task, and Python offers native support for working with many kinds of data files. Today, we're going to be working mainly with CSVs.
Import the csv module
We're going to be working with delimited text files, so the f... |
11,196 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Title
Step1: Load Image As Greyscale
Step2: Blur Image
Step3: View Image | Python Code:
# Load image
import cv2
import numpy as np
from matplotlib import pyplot as plt
Explanation: Title: Blurring Images
Slug: blurring_images
Summary: How to blurring images using OpenCV in Python.
Date: 2017-09-11 12:00
Category: Machine Learning
Tags: Preprocessing Images
Authors: Chris Albon
Preliminar... |
11,197 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Vertex SDK
Step1: Install the latest GA version of google-cloud-storage library as well.
Step2: Note
Step3: Restart the kernel
Once you've installed the additional packages, you need to r... | Python Code:
import os
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG
Explanation: Vertex SDK: Custom Training Tabular Regression Models for Online Prediction and Explainab... |
11,198 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2019 The TensorFlow Authors.
Step1: Post-training weight quantization
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step2: Train a T... | 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... |
11,199 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Ordinary Differential Equations Exercise 3
Imports
Step1: Damped, driven nonlinear pendulum
The equations of motion for a simple pendulum of mass $m$, length $l$ are
Step4: Write a functio... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from scipy.integrate import odeint
from IPython.html.widgets import interact, fixed
Explanation: Ordinary Differential Equations Exercise 3
Imports
End of explanation
g = 9.81 # m/s^2
l = 0.5 # length of pendul... |
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