text stringlengths 2.5k 6.39M | kind stringclasses 3
values |
|---|---|
```
# default_exp infousa
```
# Info-USA Intake and Operations
> This notebook uses Info-USA data to generate a portion of BNIA's Vital Signs report.
Todo:
- Wrap as Function
#### __Indicators Used__
- 131 artbusXX Arts and Culture
- 132 artempXX Arts and Culture
- 143 numbusXX Workforce and Economic Development... | github_jupyter |
# Catch that asteroid!
```
import matplotlib.pyplot as plt
plt.ion()
from astropy import units as u
from astropy.time import Time
from astropy.utils.data import conf
conf.dataurl
conf.remote_timeout
```
First, we need to increase the timeout time to allow the download of data occur properly
```
conf.remote_timeout ... | github_jupyter |
This notebook presents how to train ARedsum models, the extractive summarization based models, on ThaiSum dataset.
# Introduction to ARedSumSentRank
Cite from their paper's abstract ["AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization"](https://arxiv.org/abs/2004.06176)... | github_jupyter |
# Bayesian Models
We are now going to dig further into a specific type of **Probabilistic Graphical Model**, specifically **Bayesian Networks**. We will discuss the following:
1. What are Bayesian Models
2. Independencies in Bayesian Networks
3. How is Bayesian Model encoding the Joint Distribution
4. How we do inferen... | github_jupyter |
# Что такое AXON
[AXON](http://intellimath.bitbucket.org/axon) это нотация для сериализованного представления объектов, документов и данных в текстовой форме. Она объединяет в себе *простоту* [JSON](http://www.json.org), *расширяемость* [XML](http://www.w3.org/xml) и *удобочитаемость* [YAML](http://www.yaml.org).
Ес... | github_jupyter |
```
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from tqdm import tqdm
%matplotlib inline
from torch.utils.data import Dataset, DataLoader
import torch
import torchvision
import torch.nn as nn
import torch.optim as optim
from torch.nn import functional as F
device = torch.device("cuda" i... | github_jupyter |
# Waveform and spectrogram display
Select an audio file from the dropdown list to display its waveform and spectrogram.
```
import os
import parselmouth
import numpy as np
from phonlab.utils import dir2df
from bokeh_phon.utils import remote_jupyter_proxy_url_callback, default_jupyter_url
from bokeh_phon.models.audio... | github_jupyter |
# Grouping your data
```
import warnings
warnings.simplefilter('ignore', FutureWarning)
import matplotlib
matplotlib.rcParams['axes.grid'] = True # show gridlines by default
%matplotlib inline
import pandas as pd
```
In last week modules, you saw how to merge two datasets containing a common column to create a
sing... | github_jupyter |
```
from alpaca import Telescope, Camera, FilterWheel
import ciboulette.base.ciboulette as Cbl
import ciboulette.sector.sector as Sct
import ciboulette.utils.ephemcc as Eph
import ciboulette.utils.exposure as Exp
import ciboulette.utils.planning as Pln
```
#### Initialization of objects
```
cbl = Cbl.Ciboulette()
eph... | github_jupyter |
```
%load_ext rpy2.ipython
%matplotlib inline
from fbprophet import Prophet
import pandas as pd
import logging
logging.getLogger('fbprophet').setLevel(logging.ERROR)
import warnings
warnings.filterwarnings("ignore")
%%R
library(prophet)
```
### Forecasting Growth
By default, Prophet uses a linear model for its foreca... | github_jupyter |
```
import os
import lmdb
import caffe
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from snntoolbox.io_utils.common import to_categorical
path_to_dataset = '/home/rbodo/.snntoolbox/Datasets/roshambo'
lmdb_env = lmdb.open(path_to_dataset)
lmdb_txn = lmdb_env.begin()
lmdb_cursor = lmdb_txn.curs... | github_jupyter |
```
import json
import pandas as pd
import numpy as np
import qgrid
from _vars import *
import os
import sys
import zipfile
zip_filepath="json_archive.zip" #input parameter
target_dir="extracted_files/" #input parameter
files_to_extract = ['40171448_final.json', '10171448_final.json', '14171448_final.json'] #WRITE HE... | github_jupyter |
```
import os
import sys
module_path = os.path.abspath(os.path.join('../../'))
print(module_path)
if module_path not in sys.path:
sys.path.append(module_path)
from pydub import AudioSegment
import soundfile as sf
from params import EXCERPT_LENGTH,INPUT_DIR_PARENT,OUTPUT_DIR
# sys.path.insert(0, './models/audioset'... | github_jupyter |
# Assess and Monitor QCs, Internal Standards, and Common Metabolites
## This notebook will guide people to
* ## Identify their files
* ## Specify the LC/MS method used
* ## Specify the text-string used to differentiate blanks, QCs, and experimental injections
* ## Populate the run log with the pass/fail outcome for ea... | github_jupyter |
## Pipeline sequência de execução
## <font color='blue'>Streming de dados no twitter com MongoDB, Pandas e Scikit Learn</font>
## Preparação de conexão com twitter
```
# instalação de pacotes tweepy
!pip install tweepy
# importando os módulos tweepy, Datetime e Json
# listerner, ouvinte, vai ficar ouvindo pelos twit... | github_jupyter |
```
# Useful for debugging
%load_ext autoreload
%autoreload 2
# Nicer plotting
import matplotlib
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
matplotlib.rcParams['figure.figsize'] = (8,4)
```
# Movie example using write_beam
Here we insert write_beam elements into an existing lattice, run, save t... | github_jupyter |
```
activations = [nn.ELU(),nn.LeakyReLU(),nn.PReLU(),nn.ReLU(),nn.ReLU6(),nn.RReLU(),nn.SELU(),nn.CELU(),nn.GELU(),nn.SiLU(),nn.Tanh()]
for activation in activations:
wandb.init(project=PROJECT_NAME,name=f'activation-{activation}')
model = Test_Model(conv1_output=32,conv2_output=8,conv3_output=64,fc1_output=51... | github_jupyter |
```
import numpy as np
np.random.seed(1)
from numpy.linalg import cholesky as llt
import matplotlib.pyplot as plt
plt.rcParams.update({
"text.usetex": True,
"font.family": "sans-serif",
"font.sans-serif": ["Helvetica Neue"],
"font.size": 28,
})
def forward_substitution(L, b):
n = L.shape[0]
x = ... | github_jupyter |
foo.039 Crop Total Nutrient Consumption
http://www.earthstat.org/data-download/
file type: geotiff
```
# Libraries for downloading data from remote server (may be ftp)
import requests
from urllib.request import urlopen
from contextlib import closing
import shutil
# Library for uploading/downloading data to/from S3
i... | github_jupyter |
# Bring your own components (BYOC)
Starting in V4 Clara train is based of MONAI
from their website
"The MONAI framework is the open-source foundation being created by Project MONAI.
MONAI is a freely available, community-supported,
PyTorch-based framework for deep learning in healthcare imaging.
It provides domai... | github_jupyter |
# Семинар 6 - Введение в простые модели ML
Дополнительно понадобятся следующие библиотеки. Раскомментируйте код, чтобы установить их.
```
# !pip install -U scikit-learn
# !pip install pandas
```
# Метрики
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as anima... | github_jupyter |
```
HAM = 0
SPAM = 1
datadir = 'data/section 7'
sources = [
('beck-s.tar.gz', HAM),
('farmer-d.tar.gz', HAM),
('kaminski-v.tar.gz', HAM),
('kitchen-l.tar.gz', HAM),
('lokay-m.tar.gz', HAM),
('williams-w3.tar.gz', HAM),
('BG.tar.gz', SPAM),
('GP.tar.gz', SPAM),
('SH.tar.gz', SPAM)
]
d... | github_jupyter |
# Actividad: Clasificación de SPAM
¿Podemos clasificar un email como spam con árboles y/o ensambles?
Usaremos la base de datos [UCI Spam database](https://archive.ics.uci.edu/ml/datasets/Spambase)
Responda las preguntas y realice las actividades en cada uno de los bloques
Entregas al correo phuijse@inf.uach.cl hast... | github_jupyter |
# Amortized Neural Variational Inference for a toy probabilistic model
Consider a certain number of sensors placed at known locations, $\mathbf{s}_1,\mathbf{s}_2,\ldots,\mathbf{s}_L$. There is a target at an unknown position $\mathbf{z}\in\mathbb{R}^2$ that is emitting a certain signal that is received at the $i$-th... | github_jupyter |
# Assemble Perturb-seq BMDC data
```
import scanpy as sc
import pandas as pd
import scipy.io as io
data_path = '/data_volume/memento/bmdc/'
```
### Process time 0
```
genes = pd.read_csv(
data_path + 'raw0/GSM2396857_dc_0hr_genenames.csv', index_col=0)
var_df = pd.DataFrame(index=genes['0'].str.split('_').str[1]... | github_jupyter |
# Basics
`reciprocalspaceship` provides methods for reading and writing MTZ files, and can be easily used to join reflection data by Miller indices. We will demonstrate these uses by loading diffraction data of tetragonal hen egg-white lysozyme (HEWL).
```
import reciprocalspaceship as rs
print(rs.__version__)
```
T... | github_jupyter |
# Caffe2 Basic Concepts - Operators & Nets
In this tutorial we will go through a set of Caffe2 basics: the basic concepts including how operators and nets are being written.
First, let's import caffe2. `core` and `workspace` are usually the two that you need most. If you want to manipulate protocol buffers generated ... | github_jupyter |
<h1 align="center">Teoría Generalizada del Medio Efectivo de la Polarización Inducida: Inclusiones Esféricas</h1>
<div align="right">Por David A. Miranda, PhD<br>2021</div>
<h2>1. Importa las librerias</h2>
```
import numpy as np
import matplotlib.pyplot as plt
```
# 2. Detalles teóricos
La Teoría Generalizada del M... | github_jupyter |
```
# Erasmus+ ICCT project (2018-1-SI01-KA203-047081)
# Toggle cell visibility
from IPython.display import HTML
tag = HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide()
} else {
$('div.input').show()
}
code_show = !code_show
}
$( document... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
#... | github_jupyter |
# Hide your messy video background using neural nets, Part 2
> "Using our trained model to blur the background of video frames with OpenCV."
- toc: true
- branch: master
- badges: true
- comments: false
- categories: [fastai, privacy, opencv]
- image: images/articles/2021-backgroundblur-2/thumbnail.jpg
- hide: false
... | github_jupyter |
```
from facenet_pytorch import MTCNN
import cv2
from PIL import Image
import numpy as np
from matplotlib import pyplot as plt
from tqdm.notebook import tqdm
import matplotlib.image as mpimg
os.getcwd()
mtcnn = MTCNN(margin=20, keep_all=True, post_process=False, device='cuda:0')
image = "test_image/6_faces.jpg"
imag... | github_jupyter |
# Part 1 - Introduction to Grid
##### Grid is a platform to **train**, **share** and **manage** models and datasets in a **distributed**, **collaborative** and **secure way**.
Grid platform aims to be a secure peer to peer platform. It was created to use pysyft's features to perform federated learning pr... | github_jupyter |
```
# @title Installation
!curl -L https://raw.githubusercontent.com/facebookresearch/habitat-sim/master/examples/colab_utils/colab_install.sh | NIGHTLY=true bash -s
!wget -c http://dl.fbaipublicfiles.com/habitat/mp3d_example.zip && unzip -o mp3d_example.zip -d /content/habitat-sim/data/scene_datasets/mp3d/
!pip unins... | github_jupyter |
```
#importing libraries
import pandas as pd
import numpy as np
import scipy.stats as stats
import statsmodels.formula.api as smf
```
__Q1: Descriptive analysis__
__Q1.1: 1.1 Summary statistics__
```
#Read the data
data = pd.read_csv('progresa-sample.csv.bz2')
#Checking all the columns of the data
data.columns
#Vali... | github_jupyter |
```
# Author: Xiang Zhang (zhan6668)
# Description: This IPython notebook pre-process the movie data for Avatar-Project1-Phase3
import os, sys, re
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from scipy import stats
# Design a function to rename the titles
def renameTitle(x):
title = x
... | github_jupyter |
## Dependencies
```
!pip install --quiet /kaggle/input/kerasapplications
!pip install --quiet /kaggle/input/efficientnet-git
import warnings, glob
from tensorflow.keras import Sequential, Model
import efficientnet.tfkeras as efn
from cassava_scripts import *
seed = 0
seed_everything(seed)
warnings.filterwarnings('ig... | github_jupyter |
<p align="center">
<img src="https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true" width="220" height="240" />
</p>
## Demonstration of Lorenz Coefficient for Quantifying Spatial, Subsurface Heterogeneity
#### Alan Scherman, Rice University, UT PGE 2020 SURI
#### Supervised by:
###... | github_jupyter |
[](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/streamlit_notebooks/healthcare/DEID_EHR_DATA.ipynb)
# **De-identify Structu... | github_jupyter |
# Beautiful Charts
**Inhalt:** Etwas Chart-Formatierung
**Nötige Skills:** Erste Schritte mit Pandas
**Lernziele:**
- Basic Parameter in der Plot-Funktion kennenlernen
- Charts formatieren mit weiteren Befehlen
- Intro für Ready-Made Styles und Custom Styles
- Charts exportieren
**Weitere Ressourcen:**
- Alle Ress... | github_jupyter |
# Microsoft Azure Computer Vision API with Python
This Jupyter Notebook is almost a verbatim copy of that found here:
- https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/quickstarts/python
In order to use this notebook, you must obtain a subscription key:
- https://docs.microsoft.com/en-us/azur... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/JavaScripts/CloudMasking/Landsat8SurfaceReflectance.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
from meijer import Meijer
m = Meijer()
self = m
def get_list(self):
request = dict()
request["url"] = "https://mservices.meijer.com/listmanagement/api/list"
request["headers"] = {
"Accept": "application/meijer.shoppingList.ShoppingList-v1.0+json",
}
r =... | github_jupyter |
# Working with brainsight module
```
from pynetstim.brainsight import BrainsightProject, chunk_samples, plot_chunks
from pynetstim.plotting import plotting_points
from pynetstim.coordinates import FreesurferCoords
from pynetstim.freesurfer_files import Surf
from pynetstim.utils import clean_plot
import numpy as np
imp... | github_jupyter |
# Webscraping Color Palette
## Scraping rules
- You should check a site's terms and conditions before you scrape them. It's their data and they likely have some rules to govern it.
- Be nice - A computer will send web requests much quicker than a user can. Make sure you space out your requests a bit so that you don't ... | github_jupyter |
## Copy your notebook version
[](https://colab.research.google.com/github/Building-ML-Pipelines/building-machine-learning-pipelines/blob/master/chapters/adv_tfx/Custom_TFX_Components.ipynb)
Bit.ly: https://bit.ly/custom_TFX_components
Colab: h... | github_jupyter |
# Comparison of robustness curves for different models
```
import os
os.chdir("../")
import sys
import json
from argparse import Namespace
import numpy as np
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
import foolbox
from sklearn import metrics
from sklearn.metrics import pairwise_distances as dist
impo... | github_jupyter |
## 1. United Nations life expectancy data
<p>Life expectancy at birth is a measure of the average a living being is expected to live. It takes into account several demographic factors like gender, country, or year of birth.</p>
<p>Life expectancy at birth can vary along time or between countries because of many causes:... | github_jupyter |
# Post-Processing
<img src="../images/post-processing.png" alt="Drawing" style="width: 600px;"/>
```
from aif360.metrics.classification_metric import ClassificationMetric
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn.metrics import accuracy_score
from sklearn.linear_model import ... | github_jupyter |
# 自动数据增强
## 概述
MindSpore除了可以让用户自定义数据增强的使用，还提供了一种自动数据增强方式，可以基于特定策略自动对图像进行数据增强处理。
自动数据增强主要分为基于概率的自动数据增强和基于回调参数的自动数据增强。
## 基于概率的自动数据增强
MindSpore提供了一系列基于概率的自动数据增强API，用户可以对各种数据增强操作进行随机选择与组合，使数据增强更加灵活。
关于API的详细说明，可以参见[API文档](https://www.mindspore.cn/doc/api_python/zh-CN/master/mindspore/mindspore.dataset.transforms.htm... | github_jupyter |
# Modification of object properties
### Customize simulation
The aim of this session is to give a better understanding of how our solver works.
Most of all we will present different properties of the classes. This allows to set up very individual simulations, according to the interests one might have.
We recommend t... | github_jupyter |
```
import numpy as np
DHESN_PCA = np.genfromtxt("DHESN_RESULTS/DHESN_data_VARIOUS_DHESN_WITH_PCA_2__2018-03-21.csv", delimiter=',', skip_header=1)
print(DHESN_PCA)
import pandas as pd
data_vae_2 = pd.read_csv("DHESN_RESULTS/DHESN_data_DHESN_WITH_VAE_GRID_SEARCH_epochfix_3_nostd__2018-03-23.csv", delimiter=',')
data_va... | github_jupyter |
# BIDMC Datathon Question #1
# English vs. Non-English Speaker MIMIC-III Cohort
# Notebook 2: Exploratory Analysis
In this notebook, we want to walk you through some basic steps on how to analyze the cohort which we generated in the first notebook. This notebook is meant to simply introduce a few first steps towards ... | github_jupyter |
# Load ranked hyper-params and join selections
### Import/init
```
import os
import csv
import numpy as np
import pandas as pd
from collections import defaultdict
import matplotlib.pyplot as plt
%matplotlib inline
from notebook_helpers import load_params
# Shared base path
path = "/Users/type/Code/azad/data/wythof... | github_jupyter |
<h1><center>Global stuff</center></h1>
```
# Eases updating libs
%load_ext autoreload
%autoreload 2
%matplotlib inline
# Imports
import sys
sys.path.append('../')
from IPython.display import clear_output
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
from google.colab import drive
drive.mount('/content/... | github_jupyter |
```
# This notebook generates barplot with evaluation metrics for all groups specified in groups_eval variable.
basic_metrics = {('wtkappa', 'trim'): [0.7],
('corr', 'trim'): [0.7],
('DSM', 'trim_round'): [0.1, -0.1],
('DSM', 'trim'): [0.1, -0.1],
('R2... | github_jupyter |
```
# default_exp models
```
# Models
> Tree ensemble and decision tree models.
```
#hide
def extra_model_fn():
pass
#export
from decision_tree.imports import *
from decision_tree.core import *
from decision_tree.data import *
```
## Decision Tree
```
#export
class Node():
def __init__(self, depth, pred, s... | github_jupyter |
# Scipy基本使用
本文主要介绍numpy之外的scipy的使用，参考：
- [浅尝则止 - SciPy科学计算 in Python](https://zhuanlan.zhihu.com/p/102395401)
SciPy以NumPy为基础，提供了众多数学、科学、工程计算用的模块，包括但不限于：线性代数、常微分方程求解、信号处理、图像处理、稀疏矩阵处理。
安装：
```Shell
conda install -c conda-forge scipy
```
## 常数
首先，看看物理常数，scipy包括了众多的物理常数。
```
#Constants.py
from scipy import constant... | github_jupyter |
```
%cd -q data/actr_reco
import pandas as pd
import datetime
import numpy as np
data = [
["user1", "song1", datetime.datetime(2000, 1, 1, 0)],
["user1", "song1", datetime.datetime(2000, 1, 1, 0)],
["user1", "song2", datetime.datetime(2000, 1, 1, 1)],
["user1", "song2", datetime.datetime(2000, 1, 1, 1)]... | github_jupyter |
```
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from tqdm import tqdm as tqdm
%matplotlib inline
import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import random
from torch.util... | github_jupyter |
# Trigonometry
```
import numpy as np
import matplotlib.pyplot as plt
```
## Contents
- [Sine, cosine and tangent](#Sine_cosine_and_tangent)
- [Measurements](#Measurements)
- [Small angle approximation](#Small_angle_approximation)
- [Trigonometric functions](#Trigonometric_functions)
- [More trigonometric functions]... | github_jupyter |
## __INTRODUCTION__
### __ARTIFICIAL NEURAL NETWORKS__
* ML models that have a graph structure,inspired by the brain structure, with many interconnected units called artificial naurons https://www.youtube.com/watch?v=3JQ3hYko51Y
* ANN have the ability to learn from raw data imputs, but it also makes them slower ... | github_jupyter |
```
import random
```
The first parameter, learn_speed, is used to control how fast our perceptron will learn. The lower the value, the longer it will take to learn, but the less one value will change each overall weight. If this parameter is too high, our program will change its weights so quickly that they are inacc... | github_jupyter |
```
import math
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.special import logit
from IPython.display import display
from keras.layers import (Input, Dense, Lambda, Flatten, Reshape, BatchNormalization, Layer,
Activation, Dropout, Conv2D, Conv2DTranspose... | github_jupyter |
*Analytical Information Systems*
# Descriptive Statistics in R - Baseball Salaries
Prof. Christoph M. Flath<br>
Lehrstuhl für Wirtschaftsinformatik und Informationsmanagement
SS 2019
<h1>Agenda<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Load-packages" data-toc-modifi... | github_jupyter |
```
import pandas as pd
import os, sys
import numpy as np
os.environ["KERAS_BACKEND"] = 'tensorflow'
from keras.utils import np_utils
from sklearn.ensemble import RandomForestClassifier
import pickle
from sklearn.externals import joblib
pd.options.mode.chained_assignment = None # default='warn'
import re
from floor.da... | github_jupyter |
```
import calendar
from datetime import datetime as pydt
import requests
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import matplotlib.dates as mdates
import seaborn as sns
plt.style.use('seaborn-dark')
url = "https://api.midway.tomtom.com/ranking/liveHourly/ITA_rome"
# Reque... | github_jupyter |
# 使用Mask R-CNN模型实现人体关键节点标注
在之前的[Mask R-CNN](#)案例中，我们对Mask R-CNN模型的整体架构进行简介。Mask R-CNN是一个灵活开放的框架，可以在这个基础框架的基础上进行扩展，以完成更多的人工智能任务。在本案例中，我们将展示如何对基础的Mask R-CNN进行扩展，完成人体关键节点标注的任务。
## Mask-RCNN模型的基本结构
也许您还记得我们之前介绍过的Mask R-CNN整体架构，它的3个主要网络：
- backbone网络，用于生成特征图
- RPN网络，用于生成实例的位置、分类、分割(mask)信息
- head网络，对位置、分类和分割(mask)信息进行训练... | github_jupyter |
```
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import matplotlib
# matplotlib.use("Agg")
import matplotlib.pyplot as plt
import os
import datetime
import numpy as np
from torch.nn import MSELoss
# get data from Oscar, m... | github_jupyter |
<a href="https://colab.research.google.com/github/abegpatel/movie-recomendation-system-using-auto-encoder/blob/master/autoencoder.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
**AUTO ENCODERS:**
.auto encoders
.training of an auto encoders
.overco... | github_jupyter |
# Running a Federated Cycle with Synergos
In a federated learning system, there are many contributory participants, known as Worker nodes, which receive a global model to train on, with their own local dataset. The dataset does not leave the individual Worker nodes at any point, and remains private to the node.
The j... | github_jupyter |
# 应用自动数据增强
[](https://gitee.com/mindspore/docs/blob/master/docs/notebook/mindspore_enable_auto_augmentation.ipynb)
## 概述
自动数据增强（AutoAugment）是在一系列图像增强子策略的搜索空间中，通过搜索算法找到适合特定数据集的图像增强方案。MindSpore的`c_transforms`模块提供了丰富的C++算子来实现AutoAugme... | github_jupyter |
```
from google.colab import drive
drive.mount('GoogleDrive')
!fusermount -u GoogleDrive
import tensorflow as tf
import numpy as np
import scipy.io as scio
import os
v_feature = scio.loadmat('./My_file_path')
v_feature
train_feature = v_feature['feature']
train_feature.shape
train_label = v_feature['label'].flatten()
t... | github_jupyter |
<img src="../../../images/qiskit-heading.gif" alt="Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook" width="500 px" align="left">
# _*Qiskit Chemistry: Compiuting a Molecule's Dissociation Profile Using the Variational Quantum Eigensolver (VQE) Algorithm*_
The... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
from scipy.linalg import expm
```
# Entanglement in the Stern-Gerlach Experiment
In this problem we want to consider Stern-Gerlach experiment with a more realistic approach.
Assume the electrons are shot towards the apparatus. The hamiltonian is as follows:
$H = ... | github_jupyter |
<img src="NotebookAddons/blackboard-banner.png" width="100%" />
<font face="Calibri">
<br>
<font size="5"> <b>Change Detection in <font color='rgba(200,0,0,0.2)'>Your Own</font> SAR Amplitude Time Series Stack </b> </font>
<br>
<font size="4"> <b> Franz J Meyer; University of Alaska Fairbanks & Josef Kellndorfer, <a h... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib notebook
df = pd.read_csv('BinSize_d{}.csv'.format(400))
station_locations_by_hash = df[df['hash'] == 'fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89']
lons = station_locations_by_hash['LONGITUDE'].tolist()
lats = station_locations_by_hash['L... | github_jupyter |
# Dask Array
### What is Dask Array?
- Dask Array is composed of many NumPy or NumPy-like arrays (e.g. CuPy arrays) under the hood
- Dask Array implements a subset of the NumPy ndarray API using blocked algorithms
- These array may be streamed out of the disk of a single computer or multiple/distributed computers
- Da... | github_jupyter |
```
# General purpose libraries
import boto3
import copy
import csv
import datetime
import json
import numpy as np
import pandas as pd
import s3fs
from collections import defaultdict
import time
import re
import random
from sentence_transformers import SentenceTransformer
import sentencepiece
from scipy.spatial import ... | github_jupyter |
```
from __future__ import print_function, division
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
%matplotlib inline
import pandas as pd
```
# Import data & initial guess
```
def create_filepaths(numbers, pre_path):
padded_numbers = []
file_ext = '.... | github_jupyter |
# Comparing machine learning models in scikit-learn
*From the video series: [Introduction to machine learning with scikit-learn](https://github.com/justmarkham/scikit-learn-videos)*
```
#environment setup with watermark
%load_ext watermark
%watermark -a 'Gopala KR' -u -d -v -p watermark,numpy,pandas,matplotlib,nltk,sk... | github_jupyter |
# Importing Libraries
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
from imblearn.over_sampling import SMOTE
```
# Importing Dataset
```
data = pd.read_csv('MIES_Dev_Data/data.csv', '\t')
data.head()
for column in data.columns:
print(column, ... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
from calc_footprint_FFP_adjusted01 import FFP
import matplotlib.pyplot as plt
import numpy as np
from shapely.geometry import Point
from shapely.geometry.polygon import Polygon
from matplotlib.path import Path
import rasterio
import rasterio.plot
import rasterio.mask
import ge... | github_jupyter |
# Chapter 3 Conditional Execution
```
x = 10 # assignment
x
x == 10 # does x equal to 10? True/False
```
# one-way decision
```
x = 20 # sequentional
print(x) # sequentional
if x > 10: # sequentional (condition)
print('x is big') # conditional
print('the value of x is', x) # conditional
print('done') # sequen... | github_jupyter |
```
# ----------------------------------------------------
# Country Tally Plot
# Generate a comprehensive set of plots to visualise
# COVID-19 situation in a country.
#
# For more information, please go to:
# https://github.com/MunchDev/EpidemicSimulator
# ----------------------------------------------------
# Countr... | github_jupyter |
# Kats 204 Forecasting with Meta-Learning
This tutorial will introduce the meta-learning framework for forecasting in Kats. The table of contents for Kats 203 is as follows:
1. Overview of Meta-Learning Framework For Forecasting
2. Introduction to `GetMetaData`
3. Determining Predictability with `MetaLearnP... | github_jupyter |
# PageRank
In this notebook, you'll build on your knowledge of eigenvectors and eigenvalues by exploring the PageRank algorithm.
The notebook is in two parts, the first is a worksheet to get you up to speed with how the algorithm works - here we will look at a micro-internet with fewer than 10 websites and see what it ... | github_jupyter |
# Four Qubit Chip Design
Creates a complete quantum chip and exports it
### Preparations
The next cell enables [module automatic reload](https://ipython.readthedocs.io/en/stable/config/extensions/autoreload.html?highlight=autoreload). Your notebook will be able to pick up code updates made to the qiskit-metal (or ot... | github_jupyter |
# Quantum Process Tomography
* **Last Updated:** June 17, 2019
* **Requires:** qiskit-terra 0.8, qiskit-ignis 0.1.1, qiskit-aer 0.2
This notebook contains examples for using the ``ignis.verification.tomography`` process tomography module.
```
# Needed for functions... | github_jupyter |
---
title: "Card fraud model training"
date: 2021-06-04
type: technical_note
draft: false
---
## Create experiment
```
def experiment_wrapper():
import os
import sys
import uuid
import random
import tensorflow as tf
from tensorflow.keras.callbacks import TensorBoard
from hop... | github_jupyter |
# Assignment 3
## Implementation: EM and Gaussian mixtures
```
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import multivariate_normal as mv_normal
import matplotlib.mlab as mlab
from scipy.stats import chi2
from matplotlib.patches import Ellipse
```
We start off... | github_jupyter |
```
%matplotlib inline
```
Cross Compilation and RPC
=========================
**Author**: `Ziheng Jiang <https://github.com/ZihengJiang/>`_, `Lianmin Zheng <https://github.com/merrymercy/>`_
This tutorial introduces cross compilation and remote device
execution with RPC in TVM.
With cross compilation and RPC, you... | github_jupyter |
# Data Wrangling
# Introduction
This project focused on wrangling data from the WeRateDogs Twitter account using Python, documented in a Jupyter Notebook (wrangle_act.ipynb). This Twitter account rates dogs with humorous commentary. The rating denominator is usually 10, however, the numerators are usually greater th... | github_jupyter |
```
import tensorflow as tf
import numpy as np
import keras
import pandas as pd
x_train = pd.read_csv('trainingfeatures.csv').drop(columns=['Unnamed: 0'])
y_train = pd.read_csv('traininglabels.csv').drop(columns=['Unnamed: 0'])
x_test = pd.read_csv('testingfeatures.csv').drop(columns=['Unnamed: 0'])
y_test = pd.read_c... | github_jupyter |
# Setup dell'ambiente e degli strumenti di lavoro
Questo breve documento vi insegnerà le basi degli ambienti di lavoro necessari per questo corso.
# Installare Python
Cos'è Python?
https://docs.python.org/3/tutorial/index.html
Python è un linguaggio di programmazione potente e facile da imparare. Ha efficienti st... | github_jupyter |
# Theano, Lasagne
and why they matter
### got no lasagne?
Install the __bleeding edge__ version from here: http://lasagne.readthedocs.org/en/latest/user/installation.html
# Warming up
* Implement a function that computes the sum of squares of numbers from 0 to N
* Use numpy or python
* An array of numbers 0 to N - n... | github_jupyter |
# Density Functional Theory: Grid
## I. Theoretical Overview
This tutorial will discuss the basics of DFT and discuss the grid used to evaluate DFT quantities.
As with HF, DFT aims to solve the generalized eigenvalue problem:
$$\sum_{\nu} F_{\mu\nu}C_{\nu i} = \epsilon_i\sum_{\nu}S_{\mu\nu}C_{\nu i}$$
$${\bf FC} = {\b... | github_jupyter |
### Set GPU
```
import os
os.environ['CUDA_VISIBLE_DEVICES'] = "3"
```
## Set Dataset Name
```
# dataset_name = 'CIFAR10'
# dataset_name = 'CIFAR100'
# dataset_name = 'MNIST'
# dataset_name = 'TINYIMAGENET'
dataset_name = 'IMBALANCED_CIFAR10'
```
### Run All Now
```
# from models.resnet_stl import resnet18
import ... | github_jupyter |
# Pyspark
Using pyspark from a Jupyter notebook is quite straightforward when using a local spark instance. This can be installed trivially using conda, i.e.,
```
conda install pyspark
```
Once this is done, a local spark instance can be launched easily from within the notebook.
```
from pyspark import SparkContext... | github_jupyter |
```
import numpy as np
import pandas as pd
import os
import random
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from sklearn.dummy import DummyRegressor
from sklearn.metrics import r2_score
import tensorflow as tf
from tensorflow import keras
from sklearn.model_selection import tra... | github_jupyter |
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