text stringlengths 2.5k 6.39M | kind stringclasses 3
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|---|---|
```
import pandas as pd
import numpy as np
import pickle as pk
file_name = '1_min'
df = pd.read_csv(file_name + '.csv')
df['behavior'] = np.zeros(len(df)).astype(np.int)
intention_2_action_delay = 3000
acc_threshold = 1
# 0 for changing to left
# 1 for changing to right
# 2 for following
next_lane_change_time = dict... | github_jupyter |
### Testing accuracy of RF classifier for lightly loaded, testing and training with all the rotational speeds
```
from jupyterthemes import get_themes
import jupyterthemes as jt
from jupyterthemes.stylefx import set_nb_theme
set_nb_theme('chesterish')
import pandas as pd
data_10=pd.read_csv(r'D:\Acads\BTP\Lightly Loa... | github_jupyter |
```
%matplotlib inline
```
# Demo Axes Grid
Grid of 2x2 images with single or own colorbar.
```
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import ImageGrid
plt.rcParams["mpl_toolkits.legacy_colorbar"] = False
def get_demo_image():
import numpy as np
from matplotlib.cbook import get_sa... | github_jupyter |
### Generator States
Let's look at a simple generator function:
```
def gen(s):
for c in s:
yield c
```
We create an generator object by calling the generator function:
```
g = gen('abc')
```
At this point the generator object is **created**, but we have not actually started running it. To do so, we ca... | github_jupyter |
```
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import sys
sys.path.append('.')
import utils
def f(x):
return x * np.cos(np.pi*x)
utils.set_fig_size(mpl, (4.5, 2.5))
x = np.arange(-1.0, 2.0, 0.1)
fig = plt.figure()
sub... | github_jupyter |
```
!pip install d2l==0.17.2
# implement several utility functions to facilitate data downloading
import hashlib
import os
import tarfile
import zipfile
import requests
DATA_HUB = dict()
DATA_URL = 'http://d2l-data.s3-accelerate.amazonaws.com/'
# download function to download a dataset
def download(name, cache_dir=os.... | github_jupyter |
# Filling in Missing Values in Tabular Records
You can select Run->Run All Cells from the menu to run all cells in Studio (or Cell->Run All in a SageMaker Notebook Instance).
## Introduction
Missing data values are common due to omissions during manual entry or optional input. Simple data imputation such as using th... | github_jupyter |
<a href="https://colab.research.google.com/github/mrklees/pgmpy/blob/feature%2Fcausalmodel/examples/Causal_Games.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Causal Games
Causal Inference is a new feature for pgmpy, so I wanted to develop a fe... | github_jupyter |
# Bounding Box Visualizer
```
try:
import cv2
except ImportError:
cv2 = None
COLORS = [
"#6793be", "#990000", "#00ff00", "#ffbcc9", "#ffb9c7", "#fdc6d1",
"#fdc9d3", "#6793be", "#73a4d4", "#9abde0", "#9abde0", "#8fff8f", "#ffcfd8", "#808080", "#808080",
"#ffba00", "#6699ff", "#009933", "#1c1c1c", "... | github_jupyter |
# Analyze Data Quality with SageMaker Processing Jobs and Spark
Typically a machine learning (ML) process consists of few steps. First, gathering data with various ETL jobs, then pre-processing the data, featurizing the dataset by incorporating standard techniques or prior knowledge, and finally training an ML model ... | github_jupyter |
<p><font size="6"><b> CASE - Observation data - analysis</b></font></p>
> *© 2021, Joris Van den Bossche and Stijn Van Hoey (<mailto:jorisvandenbossche@gmail.com>, <mailto:stijnvanhoey@gmail.com>). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*
---
```
import numpy as np
i... | github_jupyter |
# Classify Images using Residual Network with 50 layers (ResNet-50)
## Import Turi Create
Please follow the repository README instructions to install the Turi Create package.
**Note**: Turi Create is currently only compatible with Python 2.7
```
import turicreate as turi
```
## Reference the dataset path
```
url =... | github_jupyter |
# More To Come. Stay Tuned. !!
If there are any suggestions/changes you would like to see in the Kernel please let me know :). Appreciate every ounce of help!
**This notebook will always be a work in progress**. Please leave any comments about further improvements to the notebook! Any feedback or constructive criticis... | github_jupyter |
### DemIntro02:
# Rational Expectations Agricultural Market Model
#### Preliminary task:
Load required modules
```
from compecon.quad import qnwlogn
from compecon.tools import discmoments
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('dark')
%matplotlib notebook
```
Generate ... | github_jupyter |
# Baseline model classification
The purpose of this notebook is to make predictions for all six categories on the given dataset using some set of rules.
<br>Let's assume that human labellers have labelled these comments based on the certain kind of words present in the comments. So it is worth exploring the comments t... | github_jupyter |
<a href="https://colab.research.google.com/github/hansong0219/Advanced-DeepLearning-Study/blob/master/UNET/UNET_Build.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import numpy as np
import os
import sys
from tensorflow.keras.layers import Inp... | github_jupyter |
# Bayesian GAN
Bayesian GAN (Saatchi and Wilson, 2017) is a Bayesian formulation of Generative Adversarial Networks (Goodfellow, 2014) where we learn the **distributions** of the generator parameters $\theta_g$ and the discriminator parameters $\theta_d$ instead of optimizing for point estimates. The benefits of the B... | github_jupyter |
```
import os
os.environ['CUDA_VISIBLE_DEVICES']='0'
from fasterai.visualize import *
plt.style.use('dark_background')
#Adjust render_factor (int) if image doesn't look quite right (max 64 on 11GB GPU). The default here works for most photos.
#It literally just is a number multiplied by 16 to get the square render r... | github_jupyter |
# AutoRec: Rating Prediction with Autoencoders
Although the matrix factorization model achieves decent performance on the rating prediction task, it is essentially a linear model. Thus, such models are not capable of capturing complex nonlinear and intricate relationships that may be predictive of users' preferences. ... | github_jupyter |
# Automatic generation of Notebook using PyCropML
This notebook implements a crop model.
### Model Cumulttfrom
```
model_cumulttfrom <- function (calendarMoments_t1 = c('Sowing'),
calendarCumuls_t1 = c(0.0),
cumulTT = 8.0){
#'- Name: CumulTTFrom -Version: 1.0, -Time step: 1
#'- Descripti... | github_jupyter |
```
import requests
import requests_cache
requests_cache.install_cache('calrecycle')
import pandas as pd
import time
URL = 'https://www2.calrecycle.ca.gov/LGCentral/DisposalReporting/Destination/CountywideSummary'
params = {'CountyID': 58, 'ReportFormat': 'XLS'}
resp = requests.post(URL, data=params)
resp
import io
def... | github_jupyter |
# A tutorial for the whitebox Python package
This notebook demonstrates the usage of the **whitebox** Python package for geospatial analysis, which is built on a stand-alone executable command-line program called [WhiteboxTools](https://github.com/jblindsay/whitebox-tools).
* Authors: Dr. John Lindsay (https://jblind... | github_jupyter |
```
import lifelines
import pymc as pm
import pyBMA
import matplotlib.pyplot as plt
import numpy as np
from math import log
from datetime import datetime
import pandas as pd
%matplotlib inline
```
The first step in any data analysis is acquiring and munging the data
An example data set can be found at:
https://jak... | github_jupyter |
```
import os
import random
import shutil
from shutil import copyfile
import csv
root_dir = "ISAFE MAIN DATABASE FOR PUBLIC/"
data = "Database/"
global_emotion_dir = "emotions_5/"
# global_emotion_dir = "emotions/"
subject_list = os.path.join(root_dir, data)
x = os.listdir(subject_list)
csv_file = "ISAFE MAIN DATABASE ... | github_jupyter |
```
import numpy as np
import pandas as pd
import datetime
from pandas.tseries.frequencies import to_offset
import niftyutils
from niftyutils import load_nifty_data
import matplotlib.pyplot as plt
start_date = datetime.datetime(2005,8,1)
end_date = datetime.datetime(2020,9,25)
nifty_data = load_nifty_data(start_date... | github_jupyter |
# Modeling and Simulation in Python
Chapter 3
Copyright 2017 Allen Downey
License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)
```
# Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an as... | github_jupyter |
# Tutorial: PyTorch
```
__author__ = "Ignacio Cases"
__version__ = "CS224u, Stanford, Spring 2021"
```
## Contents
1. [Motivation](#Motivation)
1. [Importing PyTorch](#Importing-PyTorch)
1. [Tensors](#Tensors)
1. [Tensor creation](#Tensor-creation)
1. [Operations on tensors](#Operations-on-tensors)
1. [GPU compu... | github_jupyter |
# How to create Popups
## Simple popups
You can define your popup at the feature creation, but you can also overwrite them afterwards:
```
import folium
m = folium.Map([45, 0], zoom_start=4)
folium.Marker([45, -30], popup="inline implicit popup").add_to(m)
folium.CircleMarker(
location=[45, -10],
radius=... | github_jupyter |
##### Copyright 2018 The TensorFlow Probability Authors.
Licensed under the Apache License, Version 2.0 (the "License");
```
#@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# you may not use this file except in compliance with the License.
# You may obtain a copy of th... | github_jupyter |
# CHAPTER 14 - Probabilistic Reasoning over Time
### George Tzanetakis, University of Victoria
## WORKPLAN
The section number is based on the 4th edition of the AIMA textbook and is the suggested
reading for this week. Each list entry provides just the additional sections. For example the Expected reading include ... | github_jupyter |
# <span style="color:green"> Numerical Simulation Laboratory (NSL) </span>
## <span style="color:blue"> Numerical exercises 10</span>
### Exercise 10.1
By adapting your Genetic Algorithm code, developed during the Numerical Exercise 9, write a C++ code to solve the TSP with a **Simulated Annealing** (SA) algorithm. ... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# Plot style
sns.set()
%pylab inline
pylab.rcParams['figure.figsize'] = (4, 4)
# Avoid inaccurate floating values (for inverse matrices in dot product for instance)
# See https://stackoverflow.com/questions/24537791/numpy-matrix-inversion-roun... | github_jupyter |
<a href="https://colab.research.google.com/github/constantinpape/dl-teaching-resources/blob/main/exercises/classification/5_data_augmentation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Data Augmentation on CIFAR10
In this exercise we will us... | github_jupyter |
# KFServing Sample
In this notebook, we provide two samples for demonstrating KFServing SDK and YAML versions.
### Setup
1. Your ~/.kube/config should point to a cluster with [KFServing installed](https://github.com/kubeflow/kfserving/blob/master/docs/DEVELOPER_GUIDE.md#deploy-kfserving).
2. Your cluster's Istio Ing... | github_jupyter |
```
%autosave 0
```
# 4. Evaluation Metrics for Classification
In the previous session we trained a model for predicting churn. How do we know if it's good?
## 4.1 Evaluation metrics: session overview
* Dataset: https://www.kaggle.com/blastchar/telco-customer-churn
* https://raw.githubusercontent.com/alexeygrigor... | github_jupyter |
# 神经网络的训练
作者：杨岱川
时间：2019年12月
github：https://github.com/DrDavidS/basic_Machine_Learning
开源协议：[MIT](https://github.com/DrDavidS/basic_Machine_Learning/blob/master/LICENSE)
参考文献：
- 《深度学习入门》，作者：斋藤康毅；
- 《深度学习》，作者：Ian Goodfellow 、Yoshua Bengio、Aaron Courville。
- [Keras overview](https://tensorflow.google.cn/guide/keras... | github_jupyter |
# AEJxLPS (Auroral electrojets SECS)
> Abstract: Access to the AEBS products, SECS type. This notebook uses code from the previous notebook to build a routine that is flexible to plot either the LC or SECS products - this demonstrates a prototype quicklook routine.
```
%load_ext watermark
%watermark -i -v -p virescli... | github_jupyter |
```
import torch
from torch import nn, optim
from torch.utils.data import DataLoader, Dataset
from torchvision import datasets, transforms
from torchvision.utils import make_grid
import matplotlib
from matplotlib import pyplot as plt
import seaborn as sns
from IPython import display
import torchsummary as ts
import num... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
import torch
from torch import nn as nn
from math import factorial
import random
import torch.nn.functional as F
import numpy as np
import seaborn as sn
import pandas as pd
import os
from os.path import join
import glob
from math import factorial
ttype = torch.cud... | github_jupyter |
```
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
import gc
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
pal = sns.color_palette()
df_train = pd.read_csv('train.csv')
df_train.head()
print('Total number of question pairs... | github_jupyter |
```
# default_exp models.OmniScaleCNN
```
# OmniScaleCNN
> This is an unofficial PyTorch implementation by Ignacio Oguiza - oguiza@gmail.com based on:
* Rußwurm, M., & Körner, M. (2019). Self-attention for raw optical satellite time series classification. arXiv preprint arXiv:1910.10536.
* Official implementation: h... | github_jupyter |
# Natural Language Processing - Unsupervised Topic Modeling with Reddit Posts
###### This project dives into multiple techniques used for NLP and subtopics such as dimensionality reduction, topic modeling, and clustering.
1. [Google BigQuery](#Google-BigQuery)
1. [Exploratory Data Analysis (EDA) & Preprocessing](#Exp... | github_jupyter |
```
from keras.layers import Input, Dense, merge
from keras.models import Model
from keras.layers import Convolution2D, MaxPooling2D, Reshape, BatchNormalization
from keras.layers import Activation, Dropout, Flatten, Dense
def default_categorical():
img_in = Input(shape=(120, 160, 3), name='img_in') ... | github_jupyter |
# Notebook to visualize location data
```
import csv
# count the number of Starbucks in DC
with open('starbucks.csv') as file:
csvinput = csv.reader(file)
acc = 0
for record in csvinput:
if 'DC' in record[3]:
acc += 1
print( acc )
def parse_locations(csv_iterator,state=''):
... | github_jupyter |
# 基于Tensorflow的softmax回归
Tensorflow是近年来非常非常流行的一个分布式的机器学习框架，之前一直想学习但是一直被各种各样的事情耽搁着。这学期恰好选了“人工神经网络”这门课，不得不接触这个框架了。最开始依照书上的教程通过Anaconda来配置环境，安装tensorflow。结果tensorflow是安装好了但是用起来是真麻烦。最后卸载了Anaconda在裸机上用`pip install tensorflow`来安装，可是裸机上的python是3.6.3版本的，似乎不支持tensorflow，于是在电脑上安装了另一个版本的python才算解决了这个问题，哎！说多了都是泪。言归正传，现在通过一个softma... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import statistics
import math
from sklearn.linear_model import LinearRegression
from scipy.optimize import curve_fit
er_cas_100_data = pd.read_csv('proc_er_cas_100.csv')
del er_cas_100_data['Unnamed: 0']
er_500_50_0012 = pd.read_csv('proc_er_5... | github_jupyter |
```
from pymongo import MongoClient
import pandas as pd
import datetime
client = MongoClient()
characters = client.ck2.characters
```
This notebook tries to build a world tree by drawing and edge between every character in the save file with their father and mother. Running this code will generate a network with over ... | github_jupyter |
```
# General
from os import path
from random import randrange
from sklearn.model_selection import train_test_split, GridSearchCV #cross validation
from sklearn.metrics import confusion_matrix, plot_confusion_matrix, make_scorer
from sklearn.metrics import accuracy_score, roc_auc_score, balanced_accuracy_score
from s... | github_jupyter |
<a href="https://colab.research.google.com/github/ashraj98/rbf-sin-approx/blob/main/Lab2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Lab 2
### Ashwin Rajgopal
Start off by importing numpy for matrix math, random for random ordering of sample... | github_jupyter |
To finish, check out: http://articles.adsabs.harvard.edu/cgi-bin/nph-iarticle_query?1992AJ....104.2213L&data_type=PDF_HIGH&whole_paper=YES&type=PRINTER&filetype=.pdf
```
# Third-party
from astropy.io import ascii, fits
import astropy.coordinates as coord
import astropy.units as u
from astropy.constants... | github_jupyter |
# TensorFlow Tutorial
Welcome to this week's programming assignment. Until now, you've always used numpy to build neural networks. Now we will step you through a deep learning framework that will allow you to build neural networks more easily. Machine learning frameworks like TensorFlow, PaddlePaddle, Torch, Caffe, Ke... | github_jupyter |
# Project Description
Another CV2 tutorial
this one from https://pythonprogramming.net/loading-images-python-opencv-tutorial/
```
#http://tsaith.github.io/record-video-with-python-3-opencv-3-on-osx.html
import numpy as np
import cv2
cap = cv2.VideoCapture(0) # Capture video from camera
# Get the width and height... | github_jupyter |
```
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
# import data from the github page of the book
data = pd.read_csv('https://raw.githubusercontent.com/Develop-Packt/Exploring-Absenteeism-at-Work/master/data/Absenteeism_at_work.csv', sep=";")
# print dimensionality of the... | github_jupyter |
## 8.2 创建超链接
超链接指按内容链接，可以从一个文本内容指向文本其他内容或其他文件、网址等。超链接可以分为文本内链接、网页链接以及本地文件链接。LaTeX提供了`hyperref`宏包，可用于生成超链接。在使用时，只需在前导代码中申明宏包即可，即`\usepackage{hyperref}`。
### 8.2.1 超链接类型
#### 文本内链接
在篇幅较大的文档中，查阅内容会比较繁琐，因此，往往会在目录中使用超链接来进行文本内容的快速高效浏览。可以使用`hyperref`宏包创建文本内超链接。
【**例8-4**】使用`\usepackage{hyperref}`创建一个简单的目录链接文本内容的例子。
```t... | github_jupyter |
```
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
from sklearn.preprocessing import normalize
import seaborn as sns
# list of models
# Commented few models because they produced very big results which interfere visualization
models = [
# 'RandomForestRegressor',
# ... | github_jupyter |
```
suppressMessages(library("mc2d"))
library("scales")
library("ggplot2")
library("gridExtra")
```
# Risk Study for REPLACE ME
See the [ISO 27005 Risk Cookbook](http://www.businessofsecurity.com/docs/FAIR%20-%20ISO_IEC_27005%20Cookbook.pdf)
for a more detailed explanation of this template.
# Asset
Define the asset... | github_jupyter |
```
from planaritychecker import PlanarityChecker
from numpy.random import random, randint
import networkx as nx
from planarity.planarity_networkx import planarity
%matplotlib inline
```
# Check $K_5$ and $K_{3,3}$ without one edge
```
almost_K5 = PlanarityChecker(5)
graph_almost_K5 = nx.Graph()
graph_almost_K5.add_n... | github_jupyter |
```
from __future__ import print_function
import os
from netCDF4 import Dataset
import requests
from lxml import etree
import matplotlib.pyplot as plt
from owslib.wps import WebProcessingService, ComplexDataInput
verify_ssl = True if 'DISABLE_VERIFY_SSL' not in os.environ else False
def parseStatus(execute):
o =... | github_jupyter |
# Research
## Imports
```
import pandas as pd
import pandas_datareader as dr
from pandas_datareader import data as web
import matplotlib.pyplot as plt
from matplotlib import style
import numpy as np
import datetime
import mplfinance as mpl
import plotly.graph_objects as go
import plotly
import yfinance as yf
```
## ... | github_jupyter |
[this doc on github](https://github.com/dotnet/interactive/tree/master/samples/notebooks/fsharp/Docs)
# Object formatters
## Default formatting behaviors
When you return a value or a display a value in a .NET notebook, the default formatting behavior is to try to provide some useful information about the object. If ... | github_jupyter |
# Arrays
There are several kinds of sequences in Python. A [list](lists) is one. However, the sequence type that we will use most in the class, is the array.
The `numpy` package, abbreviated `np` in programs, provides Python programmers
with convenient and powerful functions for creating and manipulating arrays.
`... | github_jupyter |
# <center>Introduction on Using Python to access GeoNet's GNSS data
In this notebook we will learn how to get data from one GNSS(Global Navigation Satellite System) station. By the end of this tutorial you will have make a graph like the one below. <img src="plot.png">
## Table of contents
### 1. Introduction
#... | github_jupyter |
```
import keras
import tensorflow as tf
print(keras.__version__)
print(tf.__version__)
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report,confusion_matrix
NGRAMS = 2
S... | github_jupyter |
# Deploy and perform inference on Model Package from AWS Marketplace
This notebook provides you instructions on how to deploy and perform inference on model packages from AWS Marketplace object detection model.
This notebook is compatible only with those object detection model packages which this notebook is linked ... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
```
# Parte 1: Iteração de Rayleigh
Vimos que podemos iterar um vetor $v$ pela matriz $A$, obtendo a sequência de vetores $A^nv$, por multiplicações sucessivas, e que isso permite encontrar um autovetor.
## Questão 1
Implemente uma função `itera(A,v,tol,debug)`... | github_jupyter |
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" property="dct:title"><b>The Knapsack Problem</b></span> by <a xmlns:cc="http://cre... | github_jupyter |
# Content:
1. [Definitions](#1.-Definitions)
2. [The root finding problem](#2.-The-root-finding-problem)
3. [Fixed point iteration](#3.-Fixed-point-iteration)
>3.1 [The cobweb diagram](#3.1-The-cobweb-diagram)
>3.2 [Fixed point iteration theorem](#3.2-Fixed-point-iteration-theorem)
>3.3 [The code](#3.3-The-code)
... | github_jupyter |
# 第二十三讲 微分方程和$e^{At}$
## 微分方程$\frac{du}{dt} = Au$
现有一阶（First-order）微分方程组：$\left\{\begin{matrix} \frac{du_1}{dt} & = & -u_1 & + 2u_2\\ \frac{du_2}{dt} & = & u_1 & -2u_2 \end{matrix}\right.$，其中初始状态 $u(0) = \begin{bmatrix}u_1 \\ u_2 \end{bmatrix} = \begin{bmatrix} 1 \\ 0 \end{bmatrix}$，现在我们需要求解方程的一般形式 $u(t)$。
首先，通过微分方... | github_jupyter |
```
!git clone https://github.com/huggingface/transformers.git
%cd transformers
!pwd
!git reset --hard 52f44dd
!cp ./examples/token-classification/run_ner.py ../
%cd ..
#!wget https://raw.githubusercontent.com/huggingface/transformers/master/examples/token-classification/run_ner.py
!wget https://raw.githubusercontent.c... | github_jupyter |
# Introducing Scikit-Learn
There are several Python libraries which provide solid implementations of a range of machine learning algorithms.
One of the best known is [Scikit-Learn](http://scikit-learn.org), a package that provides efficient versions of a large number of common algorithms.
Scikit-Learn is characterized... | github_jupyter |
```
given = """
Grey BOTTOM BOTTOM 4
Grey BOTTOM BOTTOM 2
Grey RIGHT RIGHT 2
Grey LEFT LEFT 2
BLACK RIGHT RIGHT 2
Grey LEFT LEFT 2
BLACK RIGHT RIGHT 2
Grey EMPTY EMPTY 4
Grey LEFT LEFT 3
BLACK TOP TOP 1
BLACK EMPTY EMPTY 5
Grey TOP TOP 3
Grey RIGHT RIGHT 5
Grey BOTTOM BOTTOM 5
Grey BOTTOM BOTTOM 2
BLACK EMPTY EMPTY 3
B... | github_jupyter |
# Demonstration of integrating POI Points to OSM road network
1. Use anyway you like to get the sample [POI data](https://assets.onemap.sg/shp/supermarkets.zip) consisting of supermarkets from [OneMap SG](https://www.onemap.sg/).
2. Use [OSMnx](https://osmnx.readthedocs.io/en/stable/index.html) to download the pedestri... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive')
# from google.colab import drive
# drive.mount('/content/drive')
!pwd
path = '/content/drive/MyDrive/Research/AAAI/cifar_new/k_001/sixth_run1_'
import torch.nn as nn
import torch.nn.functional as F
import pandas as pd
import numpy as np
import matplotlib... | github_jupyter |
```
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
from six.moves import range
# The folder when dumped big 3D array has been stored from... | github_jupyter |
## 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 important issues associated with using a full... | github_jupyter |
# Text Data Explanation Benchmarking: Emotion Multiclass Classification
This notebook demonstrates how to use the benchmark utility to benchmark the performance of an explainer for text data. In this demo, we showcase explanation performance for partition explainer on an Emotion Multiclass Classification model. The me... | github_jupyter |
# Unsupervised outliers detection (event detection)
```
import drama as drm
import numpy as np
import matplotlib.pylab as plt
from matplotlib import gridspec
from drama.outlier_finder import grid_run_drama
from keras.datasets import mnist
%matplotlib inline
n_try = 5
# MNIST dataset
(x_train, y_train), (x_test, y_... | github_jupyter |
```
# Import conventions we'll be using here. See Part 1
import matplotlib
# matplotlib.use('nbagg')
import matplotlib.pyplot as plt
import numpy as np
```
# Limits, Legends, and Layouts
In this section, we'll focus on what happens around the edges of the axes: Ticks, ticklabels, limits, layouts, and legends.
# Lim... | github_jupyter |
## QE methods and QE_utils
In this tutorial, we will explore various methods needed to handle Quantum Espresso (QE) calculations - to run them, prepare input, and extract output. All that will be done with the help of the **QE_methods** and **QE_utils** modules, which contains the following functions:
**QE_methods**
... | github_jupyter |
# Naive-Bayes Classifier
```
#Baseline SVM with PCA classifier
import sklearn
import numpy as np
import sklearn.datasets as skd
import ast
from sklearn.feature_extraction import DictVectorizer
from sklearn import linear_model
from sklearn import naive_bayes
from sklearn.metrics import precision_recall_fscore_support
f... | github_jupyter |
##### Copyright 2020 The TensorFlow Authors.
```
#@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 ... | github_jupyter |
# Logistic Regression with a Neural Network mindset
Welcome to your first (required) programming assignment! You will build a logistic regression classifier to recognize cats. This assignment will step you through how to do this with a Neural Network mindset, and so will also hone your intuitions about deep learning.... | github_jupyter |
# ACA-Py & ACC-Py Basic Template
## Copy this template into the root folder of your notebook workspace to get started
### Imports
```
from aries_cloudcontroller import AriesAgentController
import os
from termcolor import colored
```
### Initialise the Agent Controller
```
api_key = os.getenv("ACAPY_ADMIN_API_KEY")... | github_jupyter |
# 作业３：设计并训练KNN算法对图片进行分类。
## example1:
```
import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
k=7
test_num=int(input('请输入需要测试的数据数量：'))
#加载TFRecord训练集的数据
reader = tf.TFRecordReader()
filename_queue = tf.train.string_input_producer(["/home/srhyme/ML project/DS/train.t... | github_jupyter |
# Continuous Delivery Explained
> "An introduction to the devops practice of CI/CD."
- toc: false
- branch: master
- badges: true
- comments: true
- categories: [devops, continuous-delivery]
- image: images/copied_from_nb/img/devops/feedback-cycle.png
> *I wrote... | github_jupyter |
# Content-based recommender using Deep Structured Semantic Model
An example of how to build a Deep Structured Semantic Model (DSSM) for incorporating complex content-based features into a recommender system. See [Learning Deep Structured Semantic Models for Web Search using Clickthrough Data](https://www.microsoft.co... | github_jupyter |
# Plots for logistic regression, consistent vs inconsistent noiseless AT, increasing epsilon
```
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches
import dotenv
import pandas as pd
import mlflow
import plotly
import plotly.graph_objects as go
import plotly.express as px
import plotly.subplot... | github_jupyter |
# RNN - LSTM - Toxic Comments
A corpus of manually labeled comments - classifying each comment by its type of toxicity is available on Kaggle. We will aim to do a binary classification of whether a comment is toxic or not.
Approach:
- Learning Embedding with the Task
- LSTM
- BiLSTM
```
import numpy as np
import pan... | github_jupyter |
## Mask Adaptivity Detection Using YOLO
Mask became an essential accessory post COVID-19. Most of the countries are making face masks mandatory to avail services like transport, fuel and any sort of outside activity. It is become utmost necessary to keep track of the adaptivity of the crowd. This notebook contains imp... | github_jupyter |
## Precision-Recall Curves in Multiclass
For multiclass classification, we have 2 options:
- determine a PR curve for each class.
- determine the overall PR curve as the micro-average of all classes
Let's see how to do both.
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.da... | github_jupyter |
## A motivating example: harmonic oscillator
### created by Yuying Liu, 11/02/2019
```
# imports
import os
import sys
import torch
import numpy as np
import scipy as sp
from scipy import integrate
from tqdm.notebook import tqdm
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from mpl_toolkits.m... | github_jupyter |
Decoding with ANOVA + SVM: face vs house in the Haxby dataset
===============================================================
This example does a simple but efficient decoding on the Haxby dataset:
using a feature selection, followed by an SVM.
```
import warnings
warnings.filterwarnings('ignore')
import matplotlib... | github_jupyter |
# Assignment 3: Question Answering
Welcome to this week's assignment of course 4. In this you will explore question answering. You will implement the "Text to Text Transfer from Transformers" (better known as T5). Since you implemented transformers from scratch last week you will now be able to use them.
<img src = ... | github_jupyter |
```
#default_exp dataset_torch
```
# dataset_torch
> Module to load the slates dataset into a Pytorch Dataset and Dataloaders with default train/valid test splits.
```
#export
import torch
import recsys_slates_dataset.data_helper as data_helper
from torch.utils.data import Dataset, DataLoader
import torch
import jso... | github_jupyter |
# Collaborative filtering on the MovieLense Dataset
###### This notebook is based on part of Chapter 9 of [BigQuery: The Definitive Guide](https://www.oreilly.com/library/view/google-bigquery-the/9781492044451/ "http://shop.oreilly.com/product/0636920207399.do") by Lakshmanan and Tigani.
### MovieLens dataset
To illus... | github_jupyter |
```
import neuroglancer
# Use this in IPython to allow external viewing
# neuroglancer.set_server_bind_address(bind_address='192.168.158.128',
# bind_port=80)
from nuggt.utils import ngutils
viewer = neuroglancer.Viewer()
viewer
import numpy as np
import zarr
import os
# working_d... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import warnings
warnings.filterwarnings('ignore')
df1 = pd.read_csv('monday.csv', sep = ";")
df2 = pd.read_csv('tuesday.csv', sep = ";")
df3 = pd.read_csv('wednesday.csv', sep = ";")
df4 = pd.read_csv('thursday.csv', sep = ... | github_jupyter |
# Example 1: How to Generate Synthetic Data (MarginalSynthesizer)
In this notebook we show you how to create a simple synthetic dataset.
# Environment
## Library Imports
```
import numpy as np
import pandas as pd
from pathlib import Path
import os
import sys
```
## Jupyter-specific Imports and Settings
```
# set p... | github_jupyter |
# Simplifying Codebases
Param's just a Python library, and so anything you can do with Param you can do "manually". So, why use Param?
The most immediate benefit to using Param is that it allows you to greatly simplify your codebases, making them much more clear, readable, and maintainable, while simultaneously provi... | github_jupyter |
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