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2,500 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Normalizing Flows - Introduction (Part 1)
This tutorial introduces Pyro's normalizing flow library. It is independent of much of Pyro, but users may want to read about distribution shapes in... | Python Code:
import torch
import pyro
import pyro.distributions as dist
import pyro.distributions.transforms as T
import matplotlib.pyplot as plt
import seaborn as sns
import os
smoke_test = ('CI' in os.environ)
Explanation: Normalizing Flows - Introduction (Part 1)
This tutorial introduces Pyro's normalizing flow libr... |
2,501 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
This work is inspired by this paper from Elie and Celine Bursztein and will try to reproduce their findings applying some different ideas.
Step1: Cards data
Load the collectib... | Python Code:
from hearthpricer import hearthpricer
import numpy
import os.path
import pandas
Explanation: Introduction
This work is inspired by this paper from Elie and Celine Bursztein and will try to reproduce their findings applying some different ideas.
End of explanation
all_sets_filename = os.path.join('data', '... |
2,502 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Class Session 17 - Date Hubs and Party Hubs
Comparing the histograms of local clustering coefficients of date hubs and party hubs
In this class, we will analyze the protein-protein interacti... | Python Code:
import igraph
import numpy
import pandas
import matplotlib.pyplot
Explanation: Class Session 17 - Date Hubs and Party Hubs
Comparing the histograms of local clustering coefficients of date hubs and party hubs
In this class, we will analyze the protein-protein interaction network for two classes of yeast pr... |
2,503 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Toplevel
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specif... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'messy-consortium', 'sandbox-3', 'toplevel')
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: MESSY-CONSORTIUM
Source ID: SANDBOX-3
Sub-Topics: Radiative... |
2,504 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Building an image classification model using very little data
Based on the tutorial by Francois Chollet @fchollet https
Step1: Imports
Step2: Small Conv Net
Model architecture definition
S... | Python Code:
##This notebook is built around using tensorflow as the backend for keras
#!pip install pillow
!KERAS_BACKEND=tensorflow python -c "from keras import backend"
import os
import numpy as np
from keras.models import Sequential
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.preprocessi... |
2,505 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
阅读笔记
作者：方跃文
Email
Step1: Tab 键自动完成
在python shell中，输入表达式时候，只要按下Tab键，当前命名空间中任何已输入的字符串相匹配的变量（对象、函数等）就会被找出来：
Step2: 此外，我们还可以在任何对象之后输入一个句点来方便地补全方法和属性的输入：
Step3: Tab键自动完成成功不只可以搜索命名空间和自动完成对象或... | Python Code:
a = 5
a
import numpy as np
from numpy.random import randn
data = {i: randn() for i in range(7)}
print(data)
data1 = {j: j**2 for j in range(5)}
print(data1)
Explanation: 阅读笔记
作者：方跃文
Email: fyuewen@gmail.com
时间：始于2017年9月12日
第三章笔记始于2017年9月28日23:38，结束于 2017年10月17日
第三章 IPtyhon: 一种交互式计算和开发环境
IPython鼓励一种... |
2,506 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example 5
Step1: A very simple pipeline to show how registers are inferred.
Step2: Simulation of the core | Python Code:
import pyrtl
pyrtl.reset_working_block()
class SimplePipeline(object):
def __init__(self):
self._pipeline_register_map = {}
self._current_stage_num = 0
stage_list = [method for method in dir(self) if method.startswith('stage')]
for stage in sorted(stage_list):
... |
2,507 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
[Your name]
Homework 1
The maximum score of this homework is 100+20 points. Grading is listed in this table
Step1: 1.2 Replace rare words (10 points)
Write a function that takes a text and ... | Python Code:
def group_by_retval(sequence, grouper_func):
# TODO
l = ["ab", 12, "cd", "d", 3]
assert(group_by_retval(l, lambda x: isinstance(x, str)) == {True: ["ab", "cd", "d"], False: [12, 3]})
assert(group_by_retval([1, 1, 2, 3, 4], lambda x: x % 3) == {0: [3], 1: [1, 1, 4], 2: [2]})
Explanation: [Your name]
Hom... |
2,508 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Statsmodels - glm, mixed models, survival analysis
Data scientists normally use R for statistical heavy lifting, with a few exceptions
Step1: Above, the Region variable was treated as cathe... | Python Code:
import statsmodels.api as sm
import statsmodels.formula.api as smf
import numpy as np
import pandas
df = sm.datasets.get_rdataset("Guerry", "HistData").data
df = df[['Lottery', 'Literacy', 'Wealth', 'Region']].dropna()
df.head()
mod = smf.ols(formula='Lottery ~ Literacy + Wealth + Region', data=df)
res = m... |
2,509 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Loading and Fomatting the Required Data
Step2: Helper Functions For The PreProcessing
Taken from the original letter merging python script
Step3: Compute the RV Coefficient on the Log Chan... | Python Code:
# Load the database of letters and numbers
subject_folders_path = os.path.join(os.getcwd(), "DB_wacomPaper_v2")
subject_folders = os.listdir(subject_folders_path)
letters_db = dict()
trajectories = dict()
for subject in tqdm(subject_folders):
letters_db[subject] = dict()
trajectories[subject] = di... |
2,510 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Hey there! Here's more-or-less the steps you'll be taking to reduce our data, and, using those reduced data, extract some flux-calibrated lightcurves of WR 124!
First things first, copy this... | Python Code:
#Now, let's import some useful libraries
import numpy as np
from matplotlib import pyplot as plt
from adapt import *
from phot_tools import *
from glob import glob
import os
from astropy.io import fits
from astropy.coordinates import SkyCoord
from astropy.table import vstack, Table
%matplotlib inline
#What... |
2,511 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<!--<img width=700px; src="../img/logoUPSayPlusCDS_990.png"> -->
<p style="margin-top
Step1: 1. Let's start with a showcase
Case 1
Step2: Starting from reading this dataset, to answering q... | Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
pd.options.display.max_rows = 8
Explanation: <!--<img width=700px; src="../img/logoUPSayPlusCDS_990.png"> -->
<p style="margin-top: 3em; margin-bottom: 2em;"><b><big><big><big><big>Introduction to Pandas</big></big></... |
2,512 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Table of Contents
Preface
Step1: Introductory textbook for Kalman filters and Bayesian filters. The book is written using Jupyter Notebook so you may read the book in your browser and also ... | Python Code:
from __future__ import division, print_function
%matplotlib inline
#format the book
import book_format
book_format.set_style()
Explanation: Table of Contents
Preface
End of explanation
import numpy as np
x = np.array([1, 2, 3])
print(type(x))
x
Explanation: Introductory textbook for Kalman filters and Baye... |
2,513 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Screenshots and Movies with WebGL
One can use the REBOUND WebGL ipython widget to capture screenshots of a simulation. These screenshots can then be easily compiled into a movie.
The widget ... | Python Code:
import rebound
sim = rebound.Simulation()
sim.add(m=1) # add a star
for i in range(10):
sim.add(m=1e-3,a=0.4+0.1*i,inc=0.03*i,omega=5.*i) # Jupiter mass planets on close orbits
sim.move_to_com() # Move to the centre of mass frame
w = sim.getWidget()
w
Explanation: Screenshots and Movies with WebGL
One ... |
2,514 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementat... | Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
Explanation: Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of t... |
2,515 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Predicting sentiment from product reviews
The goal of this first notebook is to explore logistic regression and feature engineering with existing GraphLab functions.
In this notebook you wil... | Python Code:
from __future__ import division
import graphlab
import math
import string
Explanation: Predicting sentiment from product reviews
The goal of this first notebook is to explore logistic regression and feature engineering with existing GraphLab functions.
In this notebook you will use product review data from... |
2,516 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Convenience
This is an example of aneris' convenience module. This module doesn't have anywhere near the error checking of aneris' other features, but it does make it slightly simpler to cal... | Python Code:
import matplotlib.pyplot as plt
import pandas as pd
import aneris.tutorial
import aneris.convenience
plt.rcParams["figure.figsize"] = (12, 8)
Explanation: Convenience
This is an example of aneris' convenience module. This module doesn't have anywhere near the error checking of aneris' other features, but i... |
2,517 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Flowers Image Classification with TensorFlow on Cloud ML Engine
This notebook demonstrates how to do image classification from scratch on a flowers dataset using the Estimator API.
Step1: I... | Python Code:
import os
PROJECT = "cloud-training-demos" # REPLACE WITH YOUR PROJECT ID
BUCKET = "cloud-training-demos-ml" # REPLACE WITH YOUR BUCKET NAME
REGION = "us-central1" # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
MODEL_TYPE = "cnn"
# do not change these
os.environ["PROJECT"] = PROJECT
os.environ["BUCKET"... |
2,518 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Examining racial discrimination in the US job market
Background
Racial discrimination continues to be pervasive in cultures throughout the world. Researchers examined the level of racial dis... | Python Code:
%matplotlib inline
from __future__ import division
import matplotlib
matplotlib.rcParams['figure.figsize'] = (15.0,5.0)
import pandas as pd
import numpy as np
from scipy import stats
data = pd.io.stata.read_stata('data/us_job_market_discrimination.dta')
print "Total count: ",len(data)
print "race == 'b': "... |
2,519 | 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', 'test-institute-1', 'sandbox-1', 'atmoschem')
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: TEST-INSTITUTE-1
Source ID: SANDBOX-1
Topic: Atmoschem
Su... |
2,520 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Dogs vs Cats
https
Step1: データ整形
https
Step2: 訓練データからランダムに選んだ2000画像をvalidationデータとする
Step3: PyTorchで読み込みやすいようにクラスごとにサブディレクトリを作成する
Kaggleのテストデータは正解ラベルがついていないため unknown というサブディレクトリにいれる
Step4... | Python Code:
mkdir
%matplotlib inline
Explanation: Dogs vs Cats
https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html
https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition
http://aidiary.hatenablog.com/entry/20170108/1483876657
http://aidiary.hatenablog.com/entry/2017060... |
2,521 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Processing Multiple Pandas Series in Parallel
Introduction
Python's Pandas library for data processing is great for all sorts of data-processing tasks. However, one thing it doesn't support ... | Python Code:
from multiprocessing import Pool, cpu_count
def process_Pandas_data(func, df, num_processes=None):
''' Apply a function separately to each column in a dataframe, in parallel.'''
# If num_processes is not specified, default to minimum(#columns, #machine-cores)
if num_processes==None:
... |
2,522 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
iPython Cookbook - Monte Carlo III - Principal Components
Generating a Monte Carlo vector using eigenvector decomposition
Theory
Before we go into the implementation, a bit of theory on Mont... | Python Code:
import numpy as np
d = 3
R = np.random.uniform(-1,1,(d,d))+np.eye(d)
C = np.dot(R.T, R)
#C = np.array(((5,2,3),(2,5,4),(3,4,5)))
C
Explanation: iPython Cookbook - Monte Carlo III - Principal Components
Generating a Monte Carlo vector using eigenvector decomposition
Theory
Before we go into the implementati... |
2,523 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2019 The TensorFlow IO Authors.
Step1: 解码用于医学成像的 DICOM 文件
<table class="tfo-notebook-buttons" align="left">
<td><a target="_blank" href="https
Step2: 安装要求的软件包，然后重新启动运行时
Step3: ... | 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... |
2,524 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
CS228 Python Tutorial
Adapted from the CS231n Python tutorial by Justin Johnson (http
Step1: Python versions
There are currently two different supported versions of Python, 2.7 and 3.6. Som... | Python Code:
def quicksort(arr):
if len(arr) <= 1:
return arr
pivot = arr[int(len(arr) / 2)]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(right)
print (quicksort([3,6,8,10,1,... |
2,525 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
automaton.is_functional
Whether the automaton is functional, i.e. each input (string) is transduced to a unique output (string). There may be multiple paths, however, that contain this input... | Python Code:
import vcsn
Explanation: automaton.is_functional
Whether the automaton is functional, i.e. each input (string) is transduced to a unique output (string). There may be multiple paths, however, that contain this input and output string pair.
Precondition:
- The automaton is transducer
Examples
End of explana... |
2,526 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
决策树在 sklearn 中的实现简介
0. 预前
本文简单分析 scikit-learn/scikit-learn 中决策树涉及的代码模块关系。
分析的代码版本信息是：
```shell
~/W/s/sklearn ❯❯❯ git log -n 1 ... | Python Code:
SVG("./res/uml/Model__tree_0.svg")
Explanation: 决策树在 sklearn 中的实现简介
0. 预前
本文简单分析 scikit-learn/scikit-learn 中决策树涉及的代码模块关系。
分析的代码版本信息是：
```shell
~/W/s/sklearn ❯❯❯ git log -n 1 ... |
2,527 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
DKRZ data ingest information handling
The submission_forms package provides a collection of components to support the management of information related to data ingest related activities (dat... | Python Code:
## the following libraries are needed to interact with
## json based form submissions
from dkrz_forms import form_handler, utils, checks,wflow_handler
from datetime import datetime
## info_file = "path to json file"
info_file = "../Forms/../xxx.json"
# load json file and convert to Form object for simple ... |
2,528 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
2A.ML101.8
Step1: Hyperparameters, Over-fitting, and Under-fitting
The issues associated with validation and
cross-validation are some of the most important
aspects of the practice of mach... | Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
Explanation: 2A.ML101.8: Parameter selection, Validation & Testing
The content in this section is adapted from Andrew Ng's excellent Coursera course.
Source: Course on machine learning with scikit-learn by Gaël Varoquaux
End of explanati... |
2,529 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
with open('inputcode.txt',encoding="utf8") as f
Step1: def build_dataset(words)
Step2: #testing
#symbols_in_keys = [ [dictionary[ str(training_data[i])]] for i in range(offset, offset+n_in... | Python Code:
training_data = read_data(training_file)
print("Loaded training data...")
print(training_data)
training_data = list(map(int, training_data))
print(training_data)
print(training_data[:10])
print(len(training_data))
Explanation: with open('inputcode.txt',encoding="utf8") as f:
content = f.read()
data... |
2,530 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Mixed Invasion Percolation
Until now we have demonstrated percolation that assumed that the important entry pressures are determined by the throat connections, i.e. Bond Percolation. When mo... | Python Code:
import warnings
import numpy as np
import openpnm as op
%config InlineBackend.figure_formats = ['svg']
from openpnm.algorithms import MixedInvasionPercolation as mp
import matplotlib as mpl
import matplotlib.pyplot as plt
from ipywidgets import interact, IntSlider
%load_ext autoreload
%autoreload 2
%matplo... |
2,531 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Work through Geometric Factor for Sullivan 1971
How do the results depend on stackup?
Both the full formula and a bounded formula
How do the results depend on diameter?
Both the full ... | Python Code:
from pprint import pprint
import numpy as np
import pymc3 as pm
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(font_scale=1.5)
sns.set_context("notebook", rc={"lines.linewidth": 3})
%matplotlib inline
def getBoundedNormal_dist(mean=None, FWHM=None, name=None, lower=0, up... |
2,532 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
WMI Win32_Process Class and Create Method for Remote Execution
Metadata
| Metadata | Value |
|
Step1: Download & Process Security Dataset
Step2: Analytic I
Look for wmiprvse.exe... | Python Code:
from openhunt.mordorutils import *
spark = get_spark()
Explanation: WMI Win32_Process Class and Create Method for Remote Execution
Metadata
| Metadata | Value |
|:------------------|:---|
| collaborators | ['@Cyb3rWard0g', '@Cyb3rPandaH'] |
| creation date | 2019/08/10 |
| modification d... |
2,533 | Given the following text description, write Python code to implement the functionality described.
Description:
Return median of elements in the list l.
This is how the function will work:
median([3, 1, 2, 4, 5])
3
This is how the function will work:
median([-10, 4, 6, 1000, 10, 20])
15.0
| Python Code:
def median(l: list):
l = sorted(l)
if len(l) % 2 == 1:
return l[len(l) // 2]
else:
return (l[len(l) // 2 - 1] + l[len(l) // 2]) / 2.0 |
2,534 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2020 The Cirq Developers
Step1: Protocols
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step2: Introduction
Cirq's protocols are ver... | 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... |
2,535 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Toplevel
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specif... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nasa-giss', 'sandbox-3', 'toplevel')
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: NASA-GISS
Source ID: SANDBOX-3
Sub-Topics: Radiative Forcings.
Pr... |
2,536 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Know your customer (KYC) - [Lead Scoring]
Marketing a new product to customers
In this short note we discuss customer targeting through telemarketing phone calls to sell long-term deposits. ... | Python Code:
import graphlab as gl
import pandas as pd
from datetime import datetime
from sklearn.cross_validation import StratifiedKFold
## load data set from a locally saved csv file
bank_marketing = gl.SFrame.read_csv('./../../../04.UCI.ML.REPO/Bank_Marketing/bank-additional/bank-additional-full.csv',
... |
2,537 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Setup
We're going to download the collected works of Nietzsche to use as our data for this class.
Step1: Sometimes it's useful to have a zero value in the dataset, e.g. for padding
Step2: ... | Python Code:
path = get_file('nietzsche.txt', origin="https://s3.amazonaws.com/text-datasets/nietzsche.txt")
text = open(path).read()
print('corpus length:', len(text))
chars = sorted(list(set(text)))
vocab_size = len(chars)+1
print('total chars:', vocab_size)
Explanation: Setup
We're going to download the collected wo... |
2,538 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Objectives
* Learn how to parse html.
* Create models that capture different aspects of the problem.
* How to learn processes in parallel ?
Step1: Text Features based on the boiler plate
T... | Python Code:
import pandas as pd
import numpy as np
import os, sys
import re, json
from urllib.parse import urlparse
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import Imputer, FunctionTransformer
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.preprocessing impo... |
2,539 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
StateFarm Distracted Driver Detection Full Dataset
Step1: Create Batches
Step2: Use Previous Conv sample model on full dataset
The previous model used in the sample data should work better... | Python Code:
%cd /home/ubuntu/kaggle/state-farm-distracted-driver-detection
# Make sure you are in the main directory (state-farm-distracted-driver-detection)
%pwd
# Create references to key directories
import os, sys
from glob import glob
from matplotlib import pyplot as plt
import numpy as np
import keras
np.set_prin... |
2,540 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
MNIST数据集介绍
大多数例子使用了手写数字的MNIST数据集。它包含了60000个训练数据和10000个测试数据。这些数字的尺寸已标准化，同时做了居中处理，所以每个数据可以表示成一个值为0到1大小为28 * 28矩阵。
预览
用法
在例子中，我们使用TFinput_data.py脚本来加载数据集。这对管理数据相当好用，具体操作：
数据集下载
加载整个数据集成numpy数组
... | Python Code:
# 导入MNIST
from tensorflow.examples.tutorials.mnist import input_data
# 加载数据
X_train = mnist.train.images
Y_train = mnist.train.labels
X_test = mnist.test.images
Y_test = mnist.test.labels
print(X_train.shape)
print(Y_train.shape)
print(X_test.shape)
print(Y_test.shape)
Explanation: MNIST数据集介绍
大多数例子使用了手写数字的... |
2,541 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
K-nearest neighbors and scikit-learn
Review of the iris dataset
Step1: Terminology
150 observations (n=150)
Step2: K-nearest neighbors (KNN) classification
Pick a value for K.
Search for t... | Python Code:
%matplotlib inline
import pandas as pd
url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'
col_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']
iris = pd.read_csv(url, header=None, names=col_names)
iris.head()
Explanation: K-nearest neighbors and... |
2,542 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Algo - jeux de dictionnaires, plus grand suffixe commun
Les dictionnaires sont très utilisés pour associer des choses entre elles, surtout quand ces choses ne sont pas entières. Le notebook ... | Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
Explanation: Algo - jeux de dictionnaires, plus grand suffixe commun
Les dictionnaires sont très utilisés pour associer des choses entre elles, surtout quand ces choses ne sont pas entières. Le notebook montre l'intérêt de perdre un peu de tem... |
2,543 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
2D Registration Example
Most ndreg functions are convinence wrappers around the SimpleITK registration framework. Functions provided by ndreg should work reasonably well with most types of ... | Python Code:
import matplotlib.pyplot as plt
from ndreg import *
inImg = imgDownload("checkerBig")
refImg = imgDownload("checkerSmall")
Explanation: 2D Registration Example
Most ndreg functions are convinence wrappers around the SimpleITK registration framework. Functions provided by ndreg should work reasonably well ... |
2,544 | 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', 'thu', 'sandbox-1', 'aerosol')
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: THU
Source ID: SANDBOX-1
Topic: Aerosol
Sub-Topics: Transport, Emissions, ... |
2,545 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Title
Step1: Create dataframe
Step2: Scatterplot of preTestScore and postTestScore, with the size of each point determined by age
Step3: Scatterplot of preTestScore and postTestScore with... | Python Code:
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
Explanation: Title: Making A Matplotlib Scatterplot From A Pandas Dataframe
Slug: matplotlib_scatterplot_from_pandas
Summary: Making A Matplotlib Scatterplot From A Pandas Dataframe
Date: 2016-05-01 12:00
Category: Py... |
2,546 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1. Import the necessary packages to read in the data, plot, and create a linear regression model
Step1: 2. Read in the hanford.csv file
Step2: <img src="images/hanford_variables.png">
3. C... | Python Code:
import pandas as pd
import statsmodels.formula.api as smf
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
Explanation: 1. Import the necessary packages to read in the data, plot, and create a linear regression model
End of explanation
df = pd.read_csv('../data/hanford.csv')
df.head()
E... |
2,547 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Generalized Linear Models (Formula)
This notebook illustrates how you can use R-style formulas to fit Generalized Linear Models.
To begin, we load the Star98 dataset and we construct a formu... | Python Code:
import statsmodels.api as sm
import statsmodels.formula.api as smf
star98 = sm.datasets.star98.load_pandas().data
formula = "SUCCESS ~ LOWINC + PERASIAN + PERBLACK + PERHISP + PCTCHRT + \
PCTYRRND + PERMINTE*AVYRSEXP*AVSALK + PERSPENK*PTRATIO*PCTAF"
dta = star98[
[
"NABOVE",
... |
2,548 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
We will build a logistic regression model to predict whether a student gets admitted into a university.
We want to determine each applicant’s chance of admission based on their results on tw... | Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('ex2data1.txt', header=None)
df.columns = ["score1", "score2", "res"]
pos = df[(df.res == 1)]
neg = df[(df.res == 0)]
plt.scatter(pos['score1'], pos['score2'], label='admitted')
plt.scatter(neg['score1'], neg['score2']... |
2,549 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Advanced Dictionaries
Unlike some of the other Data Structures we've worked with, most of the really useful methods available to us in Dictionaries have already been explored throughout this... | Python Code:
d = {'k1':1,'k2':2}
Explanation: Advanced Dictionaries
Unlike some of the other Data Structures we've worked with, most of the really useful methods available to us in Dictionaries have already been explored throughout this course. Here we will touch on just a few more for good measure:
End of explanation
... |
2,550 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Different ways to load an input graph
We recommend using the GML graph format to load a graph. You can also use the DOT format, which requires additional dependencies (either pydot or pygrap... | Python Code:
import os, sys
import random
sys.path.append(os.path.abspath("../../../"))
import numpy as np
import pandas as pd
import dowhy
from dowhy import CausalModel
from IPython.display import Image, display
Explanation: Different ways to load an input graph
We recommend using the GML graph format to load a graph.... |
2,551 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
OM10 Tutorial
In this notebook we demonstrate the basic functionality of the om10 package, including how to
Step1: Selecting Mock Lens Samples
Let's look at what we might expect from DES an... | Python Code:
from __future__ import division, print_function
import os, numpy as np
import matplotlib
matplotlib.use('TkAgg')
%matplotlib inline
import om10
%load_ext autoreload
%autoreload 2
Explanation: OM10 Tutorial
In this notebook we demonstrate the basic functionality of the om10 package, including how to:
Make s... |
2,552 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Part I
Step1: We're going to investigate the set of data on the passengers of the Titanic. The datasets I'm providing come from the website http
Step2: Note that the above summary gives yo... | Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize']=(8,5) # optional
plt.style.use('bmh') # optional
Explanation: Part I
End of explanation
#change the paths as needed
train = pd.read_csv('../data/titanic_train.csv')
test = pd.read_csv('... |
2,553 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Below done so far
Step1: Create variables for URLs. The base_url is for the search_recipes API call. The metadata_url is for searching for valid search terms.
Step2: Extracting data
Step3:... | Python Code:
# imports
import requests
import json
import pandas as pd
import numpy as np
# ID and Key
app_id = 'e2b9bebc'
app_key = '4193215272970d956cfd5384a08580a9'
Explanation: Below done so far:
- access Yummly API with "Search Recipes API Call"
- search for "chicken" recipes
- convert JSON into dicts and lists wi... |
2,554 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Getting info on Priming experiment dataset that's needed for modeling
Info
Step1: Init
Step2: Loading OTU table (filter to just bulk samples)
Step3: Which gradient(s) to simulate?
Step4: ... | Python Code:
baseDir = '/home/nick/notebook/SIPSim/dev/priming_exp/'
workDir = os.path.join(baseDir, 'exp_info')
otuTableFile = '/var/seq_data/priming_exp/data/otu_table.txt'
otuTableSumFile = '/var/seq_data/priming_exp/data/otu_table_summary.txt'
metaDataFile = '/var/seq_data/priming_exp/data/allsample_metadata_nomock... |
2,555 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: <p style = "font-size
Step2: Preamble
Step3: EM and MNIST
The $\TeX$ markup used here uses the "align*" environment and thus should not be viewed though nbViewer.
Before proceeding,... | Python Code:
## Add JS-based table of contents
from IPython.display import HTML as add_TOC
add_TOC( u<h1 id="tocheading">Table of Contents</h1></br><div id="toc"></div>
<script src="https://kmahelona.github.io/ipython_notebook_goodies/ipython_notebook_toc.js"></script></br></hr></br> )
Explanation: <p style = "font-siz... |
2,556 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Visualize Evoked data
In this tutorial we focus on the plotting functions of
Step1: First we read the evoked object from a file. Check out
tut_epoching_and_averaging to get to this stage f... | Python Code:
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
# sphinx_gallery_thumbnail_number = 9
Explanation: Visualize Evoked data
In this tutorial we focus on the plotting functions of :class:mne.Evoked.
End of explanation
data_path = mne.datasets.sample.data_path()
fname = op.joi... |
2,557 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Bolometric correction grids
Bolometric correction is defined as the difference between the apparent bolometric magnitude of a star and its apparent magnitude in a particular bandpass
Step1: ... | Python Code:
from isochrones.mist.bc import MISTBolometricCorrectionGrid
bc_grid = MISTBolometricCorrectionGrid(['J', 'H', 'K', 'G', 'BP', 'RP', 'g', 'r', 'i'])
bc_grid.df.head()
bc_grid.interp.index_names
bc_grid.interp([5770, 4.44, 0.0, 0.], ['G', 'K'])
Explanation: Bolometric correction grids
Bolometric correction i... |
2,558 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
glstring
using the get_ functions
Each of these functions take a GL String as an argument
Step1: get_alleles() & get_loci()
Each of these functions returns a set of objects.
Step2: get_all... | Python Code:
import glstring
print(glstring.__file__)
from glstring.glstring import *
a = "HLA-A*01:01/HLA-A*01:02+HLA-A*24:02|HLA-A*01:03+HLA-A*24:03^HLA-B*44:01+HLA-B*44:02"
print(a)
Explanation: glstring
using the get_ functions
Each of these functions take a GL String as an argument
End of explanation
get_alleles(a... |
2,559 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1A.algo - Optimisation sous contrainte
L'optimisation sous contrainte est un problème résolu. Ce notebook utilise une librairie externe et la compare avec l'algorithme Arrow-Hurwicz qu'il fa... | Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
Explanation: 1A.algo - Optimisation sous contrainte
L'optimisation sous contrainte est un problème résolu. Ce notebook utilise une librairie externe et la compare avec l'algorithme Arrow-Hurwicz qu'il faudra implémenter. Plus de précision dans... |
2,560 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
probability mass function - maps each value to its probability. Alows you to compare two distributions independently from sample size.
probability - frequency expressed as a fraction of the... | Python Code:
import thinkstats2
pmf = thinkstats2.Pmf([1,2,2,3,5])
#getting pmf values
print pmf.Items()
print pmf.Values()
print pmf.Prob(2)
print pmf[2]
#modifying pmf values
pmf.Incr(2, 0.2)
print pmf.Prob(2)
pmf.Mult(2, 0.5)
print pmf.Prob(2)
#if you modify, probabilities may no longer add up to 1
#to check:
print ... |
2,561 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Decoding (MVPA)
.. include
Step1: Transformation classes
Scaler
The
Step2: PSDEstimator
The
Step3: Source power comodulation (SPoC)
Source Power Comodulation (
Step4: Decoding over tim... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
import mne
from mne.datasets import sample
from mne.decoding import (SlidingEstimator, GeneralizingEstimator, Sc... |
2,562 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Python API to BeakerX Interactive Plotting
You can access Beaker's native interactive plotting library from Python.
Plot with simple properties
Python plots has syntax very similar to Groovy... | Python Code:
from beakerx import *
import pandas as pd
tableRows = pd.read_csv('../resources/data/interest-rates.csv')
Plot(title="Title",
xLabel="Horizontal",
yLabel="Vertical",
initWidth=500,
initHeight=200)
Explanation: Python API to BeakerX Interactive Plotting
You can access Beaker's native int... |
2,563 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2019 The Sonnet 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 m... | Python Code:
import sys
assert sys.version_info >= (3, 6), "Sonnet 2 requires Python >=3.6"
!pip install dm-sonnet tqdm
import sonnet as snt
import tensorflow as tf
import tensorflow_datasets as tfds
print("TensorFlow version: {}".format(tf.__version__))
print(" Sonnet version: {}".format(snt.__version__))
Explanati... |
2,564 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
5. How to Log and Visualize Simulations
Here we explain how to take a log of simulation results and how to visualize it.
Step1: 5.1. Logging Simulations with Observers
E-Cell4 provides spec... | Python Code:
%matplotlib inline
import math
from ecell4.prelude import *
Explanation: 5. How to Log and Visualize Simulations
Here we explain how to take a log of simulation results and how to visualize it.
End of explanation
def create_simulator(f=gillespie.Factory()):
m = NetworkModel()
A, B, C = Species('A',... |
2,565 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
What books topped the Hardcover Fiction NYT best-sellers list on Mother's Day in 2009 and 2010? How about Father's Day?
Step1: 2) What are all the different book categories the NYT ranked i... | Python Code:
#my IPA key b577eb5b46ad4bec8ee159c89208e220
#base url http://api.nytimes.com/svc/books/{version}/lists
import requests
response = requests.get("http://api.nytimes.com/svc/books/v2/lists.json?list=hardcover-fiction&published-date=2009-05-10&api-key=b577eb5b46ad4bec8ee159c89208e220")
best_seller = response.... |
2,566 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Water-filling Visualized
This code is provided as supplementary material of the lecture Machine Learning and Optimization in Communications (MLOC).<br>
This code illustrates
Step1: Specify ... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from ipywidgets import interactive
import ipywidgets as widgets
%matplotlib inline
Explanation: Water-filling Visualized
This code is provided as supplementary material of the lecture Machine Learning and Optimization in Communic... |
2,567 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
A First Look at an X-ray Image Dataset
Images are data. They can be 2D, from cameras, or 1D, from spectrographs, or 3D, from IFUs (integral field units). In each case, the data come packaged... | Python Code:
from __future__ import print_function
import astropy.io.fits as pyfits
import numpy as np
import os
import urllib
import astropy.visualization as viz
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 10.0)
Explanation: A First Look at an X-ray Image Dataset
Images a... |
2,568 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Level 2
Einstieg
In diesem Level werden wir lernen, wie die Ausführung von bestimmten Code an Bedingungen knüpfen. Dafür werden wir erst den Typ des boolean und im Anschluss unsere ersten Ko... | Python Code:
eingabe = input("Bitte etwas eingeben: ")
zahl = int(eingabe)
print(zahl)
Explanation: Level 2
Einstieg
In diesem Level werden wir lernen, wie die Ausführung von bestimmten Code an Bedingungen knüpfen. Dafür werden wir erst den Typ des boolean und im Anschluss unsere ersten Kontrollstrukturen, die if-Bedin... |
2,569 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Intro a Matplotlib
Matplotlib = Libreria para graficas cosas matematicas
Que es Matplotlib?
Matplotlin es un libreria para crear imagenes 2D de manera facil.
Checate mas en
Step1: Crear gr... | Python Code:
import numpy as np # modulo de computo numerico
import matplotlib.pyplot as plt # modulo de graficas
import pandas as pd # modulo de datos
# esta linea hace que las graficas salgan en el notebook
%matplotlib inline
Explanation: Intro a Matplotlib
Matplotlib = Libreria para graficas cosas matematicas
Que es... |
2,570 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Chapter 5 – Support Vector Machines
This notebook contains all the sample code and solutions to the exercises in chapter 5.
Setup
First, let's make sure this notebook works well in both pyth... | 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... |
2,571 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Autoregressions
This notebook introduces autoregression modeling using the AutoReg model. It also covers aspects of ar_select_order assists in selecting models that minimize an information c... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import pandas_datareader as pdr
import seaborn as sns
from statsmodels.tsa.ar_model import AutoReg, ar_select_order
from statsmodels.tsa.api import acf, pacf, graphics
Explanation: Autoregressions
This notebook introduces autoregression... |
2,572 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: Train a DDSP Autoencoder on GPU
This notebook demonstrates how to install the DDSP library and train it for synthesis based on your own data using our command-line scr... | Python Code:
# Copyright 2020 Google LLC. 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 required by applic... |
2,573 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
SMC2017
Step1: IV.1 Particle Metropolis-Hastings
Consider the state-space model
$$
\begin{array}{rcll}
x_t & = & \cos\left(\theta x_{t - 1}\right) + v_t, &\qquad v_t \sim \mathcal{N}(0, 1)\... | Python Code:
import numpy as np
from scipy import stats
from tqdm import tqdm_notebook
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style()
Explanation: SMC2017: Exercise sheet IV
Setup
End of explanation
T = 50
xs_sim = np.zeros((T + 1,))
ys_sim = np.zeros((T,))
# Initial state
xs_s... |
2,574 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
用pandas做数据分析
关于数据分析
根据jetbrains公司2018年对python开发人员的调查, 从事数据分析的python使用者超过了
web开发和自动化测试.
在诸多数据科学的框架和库中,numpy pandas是最流行的
而numpy为pandas提供了基础的底层数据结构和处理函数, 用ndarray和ufunc解决了性能问题.
## pandas的核心数据... | Python Code:
import pandas as pd
x1 = pd.Series([1,2,3,4])
x2 = pd.Series(data=[1,2,3,4], index=['a', 'b', 'c', 'd'])
print("x1".center(100,"*"))
print(x1)
print("x2".center(100,"*"))
print(x2)
d = {'a':1, 'b':2, 'c':3, 'd':4}
x3 = pd.Series(d)
print(x3)
Explanation: 用pandas做数据分析
关于数据分析
根据jetbrains公司2018年对python开发人员的调查... |
2,575 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Workshop 4 - Performance Metrics
In this workshop we study 2 performance metrics(Spread and Inter-Generational Distance) on GA optimizing the POM3 model.
Step2: To compute most measures, da... | Python Code:
%matplotlib inline
# All the imports
from __future__ import print_function, division
import pom3_ga, sys
import pickle
# TODO 1: Enter your unity ID here
__author__ = "tchhabr"
Explanation: Workshop 4 - Performance Metrics
In this workshop we study 2 performance metrics(Spread and Inter-Generational Dista... |
2,576 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Weibel instability
This notebook shows a demonstration of the Weibel (electromagnetic filamentation) instability in the collision of neutral electron/positron plasma clouds. In this example ... | Python Code:
import em2d as zpic
eup = zpic.Species( "electrons up", -1.0, ppc = [2,2],
ufl = [0.0,0.0,0.6], uth = [0.1,0.1,0.1] )
pup = zpic.Species( "positrons up", +1.0, ppc = [2,2],
ufl = [0.0,0.0,0.6], uth = [0.1,0.1,0.1] )
edown = zpic.Species( "electrons down"... |
2,577 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Optimización de funciones escalares diferenciables con Sympy
Mediante optimización se obtienen soluciones elegantes tanto en teoría como en ciertas aplicaciones.
La teoría de optimización u... | Python Code:
# Librería de cálculo simbólico
import sympy as sym
# Para imprimir en formato TeX
from sympy import init_printing; init_printing(use_latex='mathjax')
sym.var('x', real = True)
f = x**2
f
df = sym.diff(f, x)
df
x_c = sym.solve(df, x)
x_c[0]
Explanation: Optimización de funciones escalares diferenciables co... |
2,578 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1. Frame the Problem
"I think, therefore I am"
What type of questions can be answered?
Developing a hypothesis drive approach.
Making the case.
Questions we will answer on alcohol topic acro... | Python Code:
# Import the libraries we need, which is Pandas and Numpy
import pandas as pd
import numpy as np
df1 = pd.read_csv('data/drinks2000.csv')
df1.head()
df1.shape
Explanation: 1. Frame the Problem
"I think, therefore I am"
What type of questions can be answered?
Developing a hypothesis drive approach.
Making t... |
2,579 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2020 The TensorFlow Authors.
Step1: Keras를 사용한 반복적 인 신경망 (RNN)
<table class="tfo-notebook-buttons" align="left">
<td><a target="_blank" href="https
Step2: 내장 RNN 레이어
Step3: 내장... | 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... |
2,580 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Average Speed of Answer, Average Handling Time, and After Call Work for Field Support Center
<em>Chris Rucker, Associate Data Scientist</em>
<em>14 Nov 2016</em>
Observation
Three discrete q... | Python Code:
import pandas as pd
import seaborn as sns
data = pd.read_csv('C:\Users\crucker\calls.csv')
data.head()
Explanation: Average Speed of Answer, Average Handling Time, and After Call Work for Field Support Center
<em>Chris Rucker, Associate Data Scientist</em>
<em>14 Nov 2016</em>
Observation
Three discrete qu... |
2,581 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Testing Neural Network based Anomaly Detection on actual data
This code reads PerfSONAR measured packet loss rates between a specified endpoint and all other endpoints in a selected time ran... | Python Code:
%matplotlib inline
from elasticsearch import Elasticsearch
from elasticsearch.helpers import scan
from time import time
import numpy as np
import pandas as pd
import random
import matplotlib
matplotlib.rc('xtick', labelsize=14)
matplotlib.rc('ytick', labelsize=14)
import matplotlib.pyplot as plt
from sk... |
2,582 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Functions
Introduction to Functions
This lecture will consist of explaining what a function is in Python and how to create one. Functions will be one of our main building blocks when we cons... | Python Code:
def name_of_function(arg1,arg2):
'''
This is where the function's Document String (doc-string) goes
'''
# Do stuff here
#return desired result
Explanation: Functions
Introduction to Functions
This lecture will consist of explaining what a function is in Python and how to create one. Fun... |
2,583 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Table of Contents
<p><div class="lev1 toc-item"><a href="#Short-study-of-the-Lempel-Ziv-complexity" data-toc-modified-id="Short-study-of-the-Lempel-Ziv-complexity-1"><span class="toc-... | Python Code:
def lempel_ziv_complexity(binary_sequence):
Lempel-Ziv complexity for a binary sequence, in simple Python code.
u, v, w = 0, 1, 1
v_max = 1
length = len(binary_sequence)
complexity = 1
while True:
if binary_sequence[u + v - 1] == binary_sequence[w + v - 1]:
v += ... |
2,584 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Directly compare Monaco to Elekta Linac iCOM
This notebook uses the PyMedPhys library to compare the collected iCOM delivery data directly to the recorded plan within Monaco's tel files.
Des... | Python Code:
import pathlib # for filepath path tooling
import lzma # to decompress the iCOM file
import numpy as np # for array tooling
import matplotlib.pyplot as plt # for plotting
Explanation: Directly compare Monaco to Elekta Linac iCOM
This notebook uses the PyMedPhys library to compare the collected iCOM del... |
2,585 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
DFFs and Registers
This example demonstrates the use of d-flip-flops and registers.
Step1: DFF
To use a DFF we import the mantle circuit DFF.
Calling DFF() creates an instance of a DFF.
Alt... | Python Code:
import magma as m
m.set_mantle_target("ice40")
Explanation: DFFs and Registers
This example demonstrates the use of d-flip-flops and registers.
End of explanation
from loam.boards.icestick import IceStick
from mantle import DFF
icestick = IceStick()
icestick.Clock.on() # Need to turn on the clock for seque... |
2,586 | 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 14 days ahead, assuming one model for all the symb... | 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'] ... |
2,587 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Possible data inputs to DataFrame constructor
2D ndarray A matrix of data, passing optional row and column labels
dict of arrays, lists, or tuples Each sequ... | Python Code:
state = ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada']
year = [2000, 2001, 2002, 2001, 2002]
pop = [1.5, 1.7, 3.6, 2.4, 2.9]
print(type(state), type(year), type(pop))
# creating dataframe
df = pd.DataFrame({'state':state, 'year':year, 'pop':pop})
print(df.info())
print(df)
sdata = {'state':state, 'year':year... |
2,588 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1> Text Classification using TensorFlow/Keras on AI Platform </h1>
This notebook illustrates
Step1: We will look at the titles of articles and figure out whether the article came from the... | Python Code:
# change these to try this notebook out
BUCKET = 'cloud-training-demos-ml'
PROJECT = 'cloud-training-demos'
REGION = 'us-central1'
import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT
os.environ['REGION'] = REGION
os.environ['TFVERSION'] = '1.14'
if 'COLAB_GPU' in os.environ: # this is ... |
2,589 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Load and preprocess the data.
Step1: Create train & test sets.
Step2: Define the cost function and how to compute the gradient.<br>
Both are needed for the subsequent optimization procedur... | Python Code:
data_original = np.loadtxt('stanford_dl_ex/ex1/housing.data')
data = np.insert(data_original, 0, 1, axis=1)
np.random.shuffle(data)
Explanation: Load and preprocess the data.
End of explanation
train_X = data[:400, :-1]
train_y = data[:400, -1]
test_X = data[400:, :-1]
test_y = data[400:, -1]
m, n = train_... |
2,590 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Preliminaries
In order to draw the network graphs in these examples (i.e. using r.draw()), you will need graphviz and pygraphviz installed. Please consult the Graphviz documentation for inst... | Python Code:
import warnings
warnings.filterwarnings("ignore")
import tellurium as te
te.setDefaultPlottingEngine('matplotlib')
%matplotlib inline
# model Definition
r = te.loada ('''
#J1: S1 -> S2; Activator*kcat1*S1/(Km1+S1);
J1: S1 -> S2; SE2*kcat1*S1/(Km1+S1);
J2: S2 -> S1; Vm2*S2/(Km2+S2);
... |
2,591 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Practical Bin Packing
This was motivated by a desire to buy just enough materials to get the job done. In this case the job was a chicken coop I was building. I can buy lumber in standard... | Python Code:
import itertools as it
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
stock = np.array([144, 120, 96]) # 12', 10' and 8' lengths
rates = np.array([9.17, 8.51, 7.52 ]) # costs for each length (1x4)
parts = [84, 72, 54, 36, 30, 30, 24, 24] # list of pieces needed (1x4)
minlength ... |
2,592 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Automating Multiple Single-Objective Spatial Optimization Models for Efficiency and Reproducibility
James D. Gaboardi | Association of American Geographers 2016
F... | Python Code:
import IPython.display as IPd
# Local path on user's machine
path = '/Users/jgaboardi/AAG_16/Data/'
Explanation: Automating Multiple Single-Objective Spatial Optimization Models for Efficiency and Reproducibility
James D. Gaboardi | Association of American Geographers 2016
Flori... |
2,593 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Skip-gram word2vec
In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about emb... | Python Code:
import time
import numpy as np
import tensorflow as tf
import utils
Explanation: Skip-gram word2vec
In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in natural lang... |
2,594 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2018 The TensorFlow Authors.
Step1: 回帰：燃費を予測する
<table class="tfo-notebook-buttons" align="left">
<td><a target="_blank" href="https
Step2: Auto MPG データセット
このデータセットはUCI Machine ... | 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... |
2,595 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step4: Copyright 2019 Google LLC.
Licensed under the Apache License, Version 2.0 (the "License");
Step5: Fashion MNIST Classifier Using Keras
The code below was presented during the practic... | Python Code:
# 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
# distribute... |
2,596 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Part 3
Step1: Import libraries
Step2: Configure GCP environment settings
Update the following variables to reflect the values for your GCP environment
Step3: Authenticate your GCP account... | Python Code:
!pip install -q -U pip
!pip install -q tensorflow==2.2.0
!pip install -q -U google-auth google-api-python-client google-api-core
Explanation: Part 3: Create a model to serve the item embedding data
This notebook is the third of five notebooks that guide you through running the Real-time Item-to-item Recomm... |
2,597 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
여러개의 Vector를 list로 만들어서 sum 연산을 하는 경우
Step1: Scalar * Vector의 연산 ex) 2 * [1,2,3,4] = [2,4,6,8]
Step3: vector 의 평균 구하기
Step5: Vector dot product
Step7: 하나의 vector에서 값 element들을 제곱하여 더한 후 ... | Python Code:
# Original book version
def vector_sum(vectors):
return reduce(vector_add, vectors)
vectors = [v,w,v,w,v,w]
vector_sum(vectors)
# Modified version by sc82.choi at Gachon - *은 여러개의 argument를 list로 전환해줌
def vector_sum_modified(vectors):
return [sum(value) for value in zip(*vectors)]
vectors = [v,w,v... |
2,598 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Session 1
Step1: I'll be using a popular image dataset for faces called the CelebFaces dataset. I've provided some helper functions which you can find on the resources page, which will jus... | Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')
Explanation: Session 1: Introduction to Tensorflow
<p class='lead'>
Creative Applications of Deep Learning with Tensorflow<br />
Parag K. Mital<br />
Kadenze, Inc.<br />
</p>
<a name="learning-goals"></a>
Learning... |
2,599 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Image Classification
In this project, you'll classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. You'll preprocess the images... | Python Code:
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
tar_gz_path = 'cifar-10-python.tar.gz'
class DLProgr... |
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