text stringlengths 2.5k 6.39M | kind stringclasses 3
values |
|---|---|
```
from utils import *
import tensorflow as tf
from sklearn.cross_validation import train_test_split
import time
trainset = sklearn.datasets.load_files(container_path = 'data', encoding = 'UTF-8')
trainset.data, trainset.target = separate_dataset(trainset,1.0)
print (trainset.target_names)
print (len(trainset.data))
p... | github_jupyter |
# OCR (Optical Character Recognition) - Implantação
## Utilização das bibliotecas [opencv](https://opencv.org/) e [Tesseract OCR](https://tesseract-ocr.github.io/) para o reconhecimento de texto em imagens e da biblioteca [JiWER](https://github.com/jitsi/jiwer) para cálculo de mérticas de perfomance
* Mais detlalh... | github_jupyter |
```
from __future__ import print_function
import os
import sys
import time
import argparse
import datetime
import math
import pickle
import torchvision
import torchvision.transforms as transforms
from utils.autoaugment import CIFAR10Policy
import torch
import torch.utils.data as data
import numpy as np
import torch... | github_jupyter |
# Chernoff Faces
This notebook is the start of three in total to understand if transforming numerical data to images would improve classification tasks through deep learning techniques such as convolutional neural networks (CNNs). This notebook creates 4 data sets sampled from multi-level gaussian models, and each dat... | github_jupyter |
# IEX Cloud Example Notebooks
In these notebooks, we'll explore some simple functionality using [IEX Cloud](https://iexcloud.io/) data. We'll utilize the official python library, [pyEX](https://github.com/iexcloud/pyEX), as well as some common libraries from the scientific python stack like pandas, matplotlib, etc.
I... | github_jupyter |
```
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
import numpy as np
import tensorflow as tf
import json
with open('dataset-bpe.json') as fopen:
data = json.load(fopen)
train_X = data['train_X']
train_Y = data['train_Y']
test_X = data['test_X']
test_Y = data['test_Y']
EOS = 2
GO = 1
vocab_size = 32000
train_Y ... | github_jupyter |
# Let's figure out these freaking learning rates
```
%load_ext autoreload
%autoreload 2
import re, os
import numpy as np
import xarray as xr
import tensorflow.keras as keras
import datetime
import pdb
import matplotlib.pyplot as plt
from src.utils import *
from src.score import *
from src.data_generator import *
from ... | github_jupyter |
```
import os
from shutil import copyfile
from PIL import Image, ImageDraw,ImageFont
import matplotlib.pyplot as plt
path = 'scd_addit'
my_list = os.listdir(path)
for i in range(50):
directory_path=path+'_sam/'+str(i)
if not os.path.exists(directory_path):
os.makedirs(directory_path)
for j,dir in e... | github_jupyter |
```
import pandas as pd
url = 'https://github.com/eueung/pilrek/raw/master/pilrek.csv'
df = pd.read_csv(url)
df.tail()
import matplotlib.pyplot as plt
%matplotlib inline
df.shape
df.dtypes
df.isna().sum()
#Menghitung nilai modus dari nama calon rektor
CaRekPilihan_mode = df['CaRek Pilihan'].mode()
print(CaRekPilihan_m... | github_jupyter |
# Resumo, Teoria e Prática - Equações Diferenciais
> Autor: Gil Miranda<br>
> Contato: gilsmneto@gmail.com<br>
> Repo: [@mirandagil](https://github.com/mirandagil/university-courses/analise-numerica-edo-2019-1)<br>
> Fontes bibliográficas:
* Rosa, R. (2017). <i>Equações Diferenciais</i>.
* Trefethen, L. & Bau, D. (1997... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import os; import sys; sys.path.append('../')
import warnings
import pandas as pd
warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning)
import socceraction.spadl.api as spadl
## Configure file and folder names
datafolder = "../data"
statsbomb_json = os.pa... | github_jupyter |
# Нейросети и PyTorch (Часть 2)
> 🚀 В этой практике нам понадобятся: `numpy==1.21.2, pandas==1.3.3, matplotlib==3.4.3, scikit-learn==0.24.2, torch==1.9.1`
> 🚀 Установить вы их можете с помощью команды: `!pip install numpy==1.21.2, pandas==1.3.3, matplotlib==3.4.3, scikit-learn==0.24.2, torch==1.9.1`
# Содержание ... | github_jupyter |
```
file_path="/home/arun/workspace/projects/janaganana-data/data/education_level/C-08/csvs/education_all_india.csv"
# Reading csv file for pca data
import pandas as pd
df = pd.read_csv(file_path)
df.head(4)
# only interested in literacy of all ages
# if age specific is required then additional conditions need to be a... | github_jupyter |
```
"""
You can run either this notebook locally (if you have all the dependencies and a GPU) or on Google Colab.
Instructions for setting up Colab are as follows:
1. Open a new Python 3 notebook.
2. Import this notebook from GitHub (File -> Upload Notebook -> "GITHUB" tab -> copy/paste GitHub URL)
3. Connect to an in... | github_jupyter |
```
%%capture
!pip install transformers
!pip install pypinyin
!pip install jieba
!pip install paddlepaddle
%%capture
import sys
sys.path.append("../")
import re,time,json
from collections import defaultdict
from torch.utils.data import DataLoader
from pypinyin import pinyin, Style
from tqdm import tqdm
import pickle
... | github_jupyter |
     
     
     
     
     
   
[Home Page](Start_Here.ipynb)
[Previous Notebook](Multi-stream_pipeline.ipynb)
     
    &e... | github_jupyter |
```
"""
The MIT License (MIT)
Copyright (c) 2021 NVIDIA
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, pub... | github_jupyter |
## pQTL
When trying to understand the mechanisms underlying the association of genetic variants with phenotypes, it can be useful to see whether the variants are also associated with the abundance of a transcript or of a protein. Loci associated with transcript and protein abundances are called expression quantative t... | github_jupyter |
## Introduction
This tutorial is primarily focussed to introduce you to "Natural Language Processing (NLP)". Natural language, i.e. language used by humans for daily communications (like Japanese, English, German, etc.), have evolved over the years and NLP is an attempt for computers to fully understand human language... | github_jupyter |
# Demo: Simulation of Tyrosine NMR Spectrum
This notebook shows how the **nmrsim** library can be used to compose an entire <sup>1</sup>H NMR spectrum from scratch.
The nmrsim.plt routines are convenient for quick plots, but for entire spectrums their small size and low resolution is noticeable (e.g. misleading sign... | github_jupyter |
# Python 入門
本章では、プログラミング言語 Python の基礎的な文法を学んでいきます。
次章以降に登場するコードを理解するにあたって必要となる最低限の知識について、最短で習得するのが目標です。
より正確かつ詳細な知識を確認したい場合には、[公式のチュートリアル](https://docs.python.jp/3/tutorial/index.html)などを参照してください。
Pythonにはバージョンとして 2 系と 3 系の 2 つの系統があり、互換性のない部分もあります。
本チュートリアルでは、3 系である **Python 3.6** 以上を前提とした解説を行っています。
## Python の特徴
プ... | github_jupyter |
This notebook was prepared by [Donne Martin](https://github.com/donnemartin). Source and license info is on [GitHub](https://github.com/donnemartin/interactive-coding-challenges).
# Challenge Notebook
## Problem: Implement a priority queue backed by an array.
* [Constraints](#Constraints)
* [Test Cases](#Test-Cases)... | github_jupyter |
# Converting Ó Raghallaigh (2010)
> "Re-implementation of the 'global' phonetiser, plus Kerry"
- toc: false
- branch: master
- badges: true
- comments: true
- categories: [irish, g2p, kerry]
This notebook contains a re-implementation of the "global" phonetiser from Brian Ó Raghallaigh's Ph.D. thesis using [rbg2p](htt... | github_jupyter |
#Step 01. Install All Dependencies
This installs Apache Spark 2.3.3, Java 8, Findspark library that makes it easy for Python to work on the given Big Data.
```
import os
#OpenJDK Dependencies for Spark
os.system('apt-get install openjdk-8-jdk-headless -qq > /dev/null')
#Download Apache Spark
os.system('wget -q http:... | github_jupyter |
## Analyze A/B Test Results
This project will assure you have mastered the subjects covered in the statistics lessons. The hope is to have this project be as comprehensive of these topics as possible. Good luck!
## Table of Contents
- [Introduction](#intro)
- [Part I - Probability](#probability)
- [Part II - A/B Te... | github_jupyter |
# Using AWS S3 to read/write market data with findatapy
May 2021 - Saeed Amen - https://www.cuemacro.com - saeed@cuemacro.com
## What is S3?
S3 is basically storage in the cloud, which is managed by AWS. Dump as much data as want from anywhere on the web and you don't need to worry about scaling your storage, which ... | github_jupyter |
```
%matplotlib inline
import inspect, os, sys, copy, pytz, re, glob, random, praw, csv
import simplejson as json
import pandas as pd
from dateutil import parser
import datetime
import matplotlib.pyplot as plt # Matplotlib for plotting
import matplotlib.dates as md
import numpy as np
import seaborn as sns
from collec... | github_jupyter |
# Formal Simulated Inference
1. Define F (i.e. your model and assumptions)
2. Formalize test
3. Describe test statistic
4. A. Sample data from F∈ℱ0
B. Sample data from F∈ℱA
5. A. Plot power vs n (i.e. perspective power analysis)
B. Plot power vs n (i.e. perspective power analysis)
6. Apply to data
## Step 1: Def... | github_jupyter |
# dataprep.py example
This notebook will show how the functions contained within the `dataprep.py` module are used to generate hdf5 files for storing raw data and peak fitting results. This module is also the primary way in which the functions contained within spectrafit.py are utilized.
```
import os
import h5py
imp... | github_jupyter |
```
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
```
# Make Segmentation Figures
### Tiles
```
import cv2
import numpy
import os, sys, glob
import numpy as np
%matplotlib inline
from matplotlib import pyplot as plt
imglist = sorted(glob.glob('/... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import sys
sys.path.append('/home/azureuser/cloudfiles/code/Users/src/')
import ee
# Trigger the authentication flow.
ee.Authenticate()
# Initialize the library.
ee.Initialize()
"""
Detect Methane hotspots
------------------------------
Functions to load and detect methane hotsp... | github_jupyter |
```
# write raw rbnf source code.
from rbnf.easy import build_parser, Language, build_language
from typing import NamedTuple, List
my_lisp_definition = """
ignore [space]
space := R'\s'
term := R'[^\(\)\s]'
sexpr ::= '(' [sexpr as head sexpr* as tail] ')' | term as atom
rewrite
if atom:
... | github_jupyter |
# Unity3D Game with Amazon SageMaker RL
---
## Introduction
[Unity](https://unity.com/) is currently the most popular gaming engine used by game developers around the world. Unity engine can be used to create 3D, 2D, virtual reality, and augmented reality games, as well as simulations and other experiences. [ML-Agent... | github_jupyter |
# Codealong 06
```
import os
import numpy as np
import pandas as pd
import csv
import matplotlib.pyplot as plt
from sklearn import neighbors, metrics, grid_search, cross_validation
pd.set_option('display.max_rows', 10)
pd.set_option('display.notebook_repr_html', True)
pd.set_option('display.max_columns', 10)
%matplo... | github_jupyter |
# IST256 Lesson 07
## Files
- Zybook Ch7
- P4E Ch7
## Links
- Participation: [https://poll.ist256.com](https://poll.ist256.com)
- Zoom Chat!
# Agenda
### Go Over Homework H06
### New Stuff
- The importance of a persistence layer in programming.
- How to read and write from files.
- Techniques for reading a file... | github_jupyter |
# Example: PCA applied to MNIST
This notebook shows how PCA can be applied to a non-trivial data set to reduce its dimensionality.
PCA (and its variations, like Incremental PCA) is a useful tool to reduce the dimensionality of your feature space to boost model training performance.
In this example, we will examine how... | github_jupyter |
# Seasonality, Trend and Noise
> You will go beyond summary statistics by learning about autocorrelation and partial autocorrelation plots. You will also learn how to automatically detect seasonality, trend and noise in your time series data. This is the Summary of lecture "Visualizing Time-Series data in Python", via ... | github_jupyter |
```
# default_exp metrics
```
# Metrics
> This contains metrics not included in fastai.
```
#export
import sklearn.metrics as skm
from fastai.metrics import *
from tsai.imports import *
#export
mk_class('ActivationType', **{o:o.lower() for o in ['No', 'Sigmoid', 'Softmax', 'BinarySoftmax']},
doc="All possi... | github_jupyter |
# DataFlow API walkthrough
Suhas Somnath <br>
4/6/2022 <br>
Oak Ridge National Laboratory
## 0. Prepare to use DataFlow's API:
1. Install the ``ordflow`` python package from PyPi via:
``pip install ordflow``
2. Generate an API Key from DataFlow's web interface
**Note**: API Keys are not reusable across DataFlow se... | github_jupyter |
# Stock Entity Recognition Unmasked
```
import pandas
import re
import json
import math
import numpy
import os
import tensorflow as tf
from itertools import chain
from multiprocessing import Pool
from functools import partial
from transformers import TFBertForTokenClassification, BertTokenizerFast
from sklearn.model... | github_jupyter |
# Fuzzingbook Release Notes
This book comes with version numbers; these correspond to the version numbers in [the Python pip package](Importing.ipynb).
## Version 1.0 (in progress)
* We now support (but also require) **Python 3.9 or later**. Earlier versions still required Python 3.6 due to some outdated modules suc... | github_jupyter |
# Matching Market - testing Parameter NL production decline
This simple model consists of a buyer, a supplier, and a market.
The buyer represents a group of customers whose willingness to pay for a single unit of the good is captured by a vector of prices _wta_. You can initiate the buyer with a set_quantity functio... | github_jupyter |
# Classification data using scikit-learn
Classification problems are those in which the feature to be predicted contains categories of values. Each of these categories are considered as a class into which the predicted value will fall into and hence has its name, classification.
In this notebook, we'll use scikit-le... | github_jupyter |
# ---------------------------------------------------------------
# python best courses https://courses.tanpham.org/
# ---------------------------------------------------------------
# 100 numpy exercises
This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow and in th... | github_jupyter |
<div dir='rtl'>
# الوصف
</div>
<div dir='rtl'>
يُستخدم هذا الدفتر لطلب حساب متوسط السلاسل الزمنية لطبقة بيانات WaPOR لمنطقة باستخدام WaPOR API. ستحتاج إلى WaPOR API Token لاستخدام هذا الكمبيوتر المحمول
</div>
<div dir='rtl'>
# الخطوة 1: اقرأ APIToken
</div>
<div dir='rtl'>
احصل على APItoken من https... | github_jupyter |
```
%matplotlib inline
import numpy as np
def _idcg(l):
return sum((1.0 / np.log(i + 2) for i in range(l)))
_idcgs = [_idcg(i) for i in range(101)]
def ndcg(gt, rec):
dcg = 0.0
for i, r in enumerate(rec):
if r in gt:
dcg += 1.0 / np.log(i + 2)
return dcg / _idcgs[len(gt)]
import ... | github_jupyter |
### Practice: gym interfaces
_Reference: based on Practical RL_ [week01](https://github.com/yandexdataschool/Practical_RL/tree/master/week01_intro)
```
# In Google Colab, uncomment this:
# !wget https://bit.ly/2FMJP5K -O setup.py && bash setup.py
# This code creates a virtual display to draw game images on.
# If you... | github_jupyter |
```
%matplotlib inline
```
# Initial Data Cleaning and Exploration
Code for the initial data cleaning and exploration done before modeling
_Author: Jimmy Charité_
_Email: jimmy.charite@gmail.com_
# Directory & Packages
```
import os
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.... | github_jupyter |
# Counts vs. angle
W vs. angle for Figure 6 in the paper.
Enable interactive plots
```
%matplotlib notebook
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import (MultipleLocator, FormatStrFormatter, AutoMinorLocator)
import pandas as pd
import scipy.io as sio
os.getc... | github_jupyter |
```
tupla = ([1, 2, 3], [2, 3, 4])
tupla[1] = [2, 4, 5]
arr = tupla[1]
arr += [2]
tupla
tupla[0] += [4]
tupla
tupla[0].append(5)
tupla
tokens1 = [1, 1, 2, 3, 5]
tokens2 = [1, 2, 3, 3, 4]
from collections import Counter
from random import randint
tokens1 = [randint(0, 50) for _ in range(randint(25, 40))]
tokens2 = [rand... | github_jupyter |
```
import numpy as np
from torch.utils.data import Dataset
import torchvision
import os
import h5py
import pickle # TODO or use h5py instead?
import trimesh
import config as cfg
import dataset.augmentation as Transforms
class DatasetModelnet40(Dataset):
def __init__(self, split, noise_type):
dataset... | github_jupyter |
# Lambda School Data Science Unit 4 Sprint Challenge 4
## RNNs, CNNs, AutoML, and more...
In this sprint challenge, you'll explore some of the cutting edge of Data Science.
*Caution* - these approaches can be pretty heavy computationally. All problems were designed so that you should be able to achieve results withi... | github_jupyter |
# Dropout
Dropout [1] is a technique for regularizing neural networks by randomly setting some features to zero during the forward pass. In this exercise you will implement a dropout layer and modify your fully-connected network to optionally use dropout.
[1] Geoffrey E. Hinton et al, "Improving neural networks by pre... | github_jupyter |
# Modeling with `scikit-learn`
<br>
<center>
<img src="https://raw.githubusercontent.com/uc-r/Advanced-R/f1001a5b40b5e3803e4cd01a40c7129fee3afb39/docs/images/process-icon.svg" alt="fortune-teller.gif" width="1200" height="1200">
</center>
# Introduction to Machine Learning
## Introduction
Machine learning (ML) conti... | github_jupyter |
#Ejemplo 6: Predicción de spam
---
**Objetivo y comprensión del problema**
El objetivo del problema consisten en predecir la posibilidad de que un texto corresponda a un mensaje de spam. Cada una de las tuplas tiene un texto y una clasificación que se utilizará en el entrenamiento.
Se trata de un problema de clasifi... | github_jupyter |
# MLServer - Retrieve and Classify
This notebook retrieves data from an Azure Function server, performs classification using a machine learning model and uploads the results back to the cloud.
All of that is performed using API REST endpoints exposed in Azure.
Segmentation model is DeepLabV3+, at https://github.com/... | github_jupyter |
# Exploratory Data Analysis of Iris
## Importing the Libraries
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
data=pd.read_csv('iris.csv')
```
## Understanding the data
```
# five elements from top
data.head(10)
# five elements from end
data.tail(3)
# shape of the d... | github_jupyter |
```
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import sklearn
import pandas as pd
import os
import sys
import time
import tensorflow as tf
from tensorflow import keras
print(tf.__version__)
print(sys.version_info)
for module in mpl, np, pd, sklearn, tf, keras:
pr... | github_jupyter |
## Traditional Feedforward neural network to approximate a black box function
This is just a toy example to test the basic functionality of Bokeh interactive plot!
```
import torch
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
from torch.autograd import Variabl... | github_jupyter |
```
from SentenceParserPython3 import SentenceParser
import pandas as pd
import numpy as np
from bs4 import BeautifulSoup
import sys
import re
def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '='):
percent = ("{0:." + str(decimals) + "f}").format(100 * (iteration ... | github_jupyter |
## Second Attempt on Language Detection
This code shows the second attempt on processing the corpora and trying to come up with a model for the europar.test file.
This code uses files in the /txt directory.
To build a model, this uses the most common words and most common letters used on each language and builds a f... | github_jupyter |
Deep Learning
=============
Assignment 1
------------
The objective of this assignment is to learn about simple data curation practices, and familiarize you with some of the data we'll be reusing later.
This notebook uses the [notMNIST](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html) dataset to be used... | github_jupyter |
<img align="centre" src="../../figs/Github_banner.jpg" width="100%">
# Southern Africa Cropland Mask
## Background
The notebooks in this folder provide the means for generating a cropland mask (crop/non-crop) for the Southern Africa study region (Figure 1), for the year 2019 at 10m resolution. To obtain classificati... | github_jupyter |
<h1><center><font size="6">Santander Customer Transaction Prediction</font></center></h1>
<h1><center><font size="5">Can you identify who will make a transaction?</font></center></h1>
<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/4/4a/Another_new_Santander_bank_-_geograph.org.uk_-_1710962.jpg/640px-An... | github_jupyter |
# Scraping Job Postings from LinkedIn
This code is adapted and modified from the following article: https://maoviola.medium.com/a-complete-guide-to-web-scraping-linkedin-job-postings-ad290fcaa97f and Cohort 2's work.
### Data Source
LinkedIn job post board. This data collection is focusing on job posts near Rancho C... | github_jupyter |
<a href="https://colab.research.google.com/github/FairozaAmira/AI-programming-1-a/blob/master/Lecture08.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# 第８回目の講義中の練習問題回答
## `for` ループ
1. `for`ループを使って、1 から 5 まで表示しなさい。
```
for N in [1, 2, 3, 4, 5]:
... | github_jupyter |
# Introductory applied machine learning (INFR10069)
# Lab 0: Introduction
To complete this lab you should:
* Set up your IAML environment ready for the course
* __Read the text__ and run all the cells in this notebook and have a play with all the objects created (Don't worry about messing up this notebook - you can ... | github_jupyter |
# Present Value of Liabilities and Funding Ratio
In this lab session, we'll examine how to discount future liabilities to compute the present value of future liabilities, and measure the funding ratio.
The funding ratio is the ratio of the current value of assets to the present value of the liabilities.
In order to ... | github_jupyter |
<h1> CREAZIONE MODELLO SARIMA SPAGNA
```
import pandas as pd
df = pd.read_csv('../../csv/nazioni/serie_storica_sp.csv')
df.head()
df['TIME'] = pd.to_datetime(df['TIME'])
df.info()
df=df.set_index('TIME')
df.head()
```
<h3>Creazione serie storica dei decessi totali
```
df = df.groupby(pd.Grouper(freq='M')).sum()
df.h... | github_jupyter |
```
import glob
import os
import librosa
import numpy as np
!pip install pretty_midi
import pretty_midi
from google.colab import drive
drive.mount('/content/drive', force_remount=True)
```
**Please update the start path and destination path**
```
start ='/content/drive/MyDrive/MUS' # Divide all 9 directories of the M... | github_jupyter |
# SWELL-KW GRU
Adapted from Microsoft's notebooks, available at https://github.com/microsoft/EdgeML authored by Dennis et al.
```
import pandas as pd
import numpy as np
from tabulate import tabulate
import os
import datetime as datetime
import pickle as pkl
import pathlib
from __future__ import print_function
import ... | github_jupyter |
<h1><center>Day05 Machine Learning Using Python</center></h1>
## Day05 Objectives Classification models - 1
- Logistic regression
- Support Vector Machines
```
import pandas as pd
import numpy as np
import seaborn as s... | github_jupyter |
# Create a corpus file from the English wikipedia dump
In this notebook we'll:
1. Process a wikipedia dump that has been transformed into a series of JSONL files
1. Select text section that have contiguous group of sentences, so as to yield a higher quality embedding later on
1. Tokenize the senteces and words
1. Form... | github_jupyter |
```
# pandas
import pandas as pd
from pandas import Series,DataFrame
# numpy, matplotlib, seaborn
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
%matplotlib inline
from IPython.display import display
# remove warnings
import warnings
warnings.filterwarnings('ignor... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
pd.options.display.max_colwidth=300
# Shingle generators
# Arguments : Message string, shingle size {in words}
# Returns : All shingles formed with k words
def shingle_generator(message, k):
message = me... | github_jupyter |
# Kubeflow pipelines
**Learning Objectives:**
1. Learn how to deploy a Kubeflow cluster on GCP
1. Learn how to create a experiment in Kubeflow
1. Learn how to package you code into a Kubeflow pipeline
1. Learn how to run a Kubeflow pipeline in a repeatable and traceable way
## Introduction
In this notebook,... | github_jupyter |
# Dynamic Programming
Basically we solve the Bellman optimality equation using these methods:
* Value Iteration
* Policy Iteration
From the perspective of the quality of the policy found both methods will work, but they are the base of more advanced methodologies.
### References
* [Artificial Intelligence](https://git... | github_jupyter |
# Vitessce Widget Tutorial
# Visualization of single-cell RNA seq data
## 1. Import dependencies
We need to import the classes and functions that we will be using from the corresponding packages.
```
import os
from os.path import join
from urllib.request import urlretrieve
from anndata import read_h5ad
import scanp... | github_jupyter |
## Task: Question Answering for Game of Thrones
<img style="float: right;" src="https://upload.wikimedia.org/wikipedia/en/d/d8/Game_of_Thrones_title_card.jpg">
Question Answering can be used in a variety of use cases. A very common one: Using it to navigate through complex knowledge bases or long documents ("search ... | github_jupyter |
# Lesson 2 - Image Classification Models from Scratch
## Lesson Video:
```
#hide_input
from IPython.lib.display import YouTubeVideo
YouTubeVideo('_SKqrTlXNt8')
#hide
#Run once per session
!pip install fastai wwf -q --upgrade
#hide_input
from wwf.utils import state_versions
state_versions(['fastai', 'fastcore', 'wwf']... | github_jupyter |
# CER044 - Install signed Controller certificate
This notebook installs into the Big Data Cluster the certificate signed
using:
- [CER034 - Sign Controller certificate with cluster Root
CA](../cert-management/cer034-sign-controller-generated-cert.ipynb)
NOTE: At the end of this notebook the Controller pod and ... | github_jupyter |
# Goal
## Questions
* How is incorporator identification accuracy affected by the percent isotope incorporation of taxa?
* How variable is sensitivity depending on model stochasticity
* Each simulation has differing taxa as incorporators, therefore, the incorporators then differ by GC and abundance between simulati... | 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 |
```
!pip install autokeras
!pip install git+https://github.com/keras-team/keras-tuner.git@1.0.2rc1
```
## A Simple Example
The first step is to prepare your data. Here we use the [California housing
dataset](https://scikit-learn.org/stable/datasets/index.html#california-housing-dataset) as an example.
```
from sklear... | github_jupyter |
# Scikit-Learn IRIS Model
* Wrap a scikit-learn python model for use as a prediction microservice in seldon-core
* Run locally on Docker to test
* Deploy on seldon-core running on a kubernetes cluster
## Dependencies
* [S2I](https://github.com/openshift/source-to-image)
```bash
pip install sklearn
pip inst... | github_jupyter |
```
from uberpy import Uber
with open('uber.txt','r') as f:
client_id = f.readline().strip()
server_token = f.readline().strip()
secret= f.readline().strip()
uber = Uber(client_id, server_token, secret)
from pprint import pprint
import pandas as pd
import random
from pyDOE import *
import math
... | github_jupyter |
The goal of this notebook is to demonstrate how to obtain a superpixel segmentation of a raster and then store these as a vector format for visualization and later analyses.
A nice example of superpixel segmentation using the module we are using can be found [here](https://scikit-image.org/docs/dev/auto_examples/segme... | github_jupyter |
```
import numpy as np
from tqdm import tqdm
from sklearn.metrics import roc_auc_score
from numpy.testing import assert_almost_equal
from myfunc.my_roc_auc import my_roc_auc
```
# Scikit-learnと自作のAUC比較
## サンプルデータで一致確認
```
# 実装がsklearnのAUCと一致するかテスト
n_samples = 1000
np.random.seed(n_samples)
# generate sample data
... | github_jupyter |
# Quick Numbers for Paper
In today's meeting we are going through the paper looking for holes. I am just taking a quick look and filling some of them.
```
import os
import sys
import re
from pathlib import Path
from io import StringIO
from yaml import load
from IPython.display import display, HTML, Markdown
import n... | github_jupyter |
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://colab.research.google.com/github/PreferredAI/tutorials/blob/master/recommender-systems/07_explanations.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a>
</td>
<td>
<a tar... | github_jupyter |
# 2 qubits gate
Let's check the 2 qubits gate.
## What we'll learn this time
1. 2qubits gate
2. Implementation example
## Install Blueqat
Install Blueqat from pip.
```
!pip install blueqat
```
## Two qubit gate
Two qubits gate is mainly one qubit gate with a control bit added to it.
### CX, CY, CZ
CX, CY, CZ gates... | github_jupyter |
[Table of Contents](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb)
# Gaussian Probabilities
```
#format the book
%matplotlib inline
from __future__ import division, print_function
from book_format import load_style
load_style()
```
## Introduction... | github_jupyter |
<center>
<img src="https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/IDSNlogo.png" width="300" alt="cognitiveclass.ai logo" />
</center>
# Write and Save Files in Python
Estimated time needed: **25** minutes
## Objectives
After completing this l... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import rosbag
import pymap3d as pm
import numba as nb
from scipy.signal import savgol_filter
%matplotlib inline
def wrap_angle(angle):
return (angle + np.pi) % (2 * np.pi) - np.pi
@nb.njit()
def to_euler(x, y, z, w):
"""Dari Coursera: ... | github_jupyter |
# Classes
Classes allow you to define how to package data with functions to create objects. An object is an instance of a class, which contains its own data, and its own copy of functions that can operate on that data.
You use classes to define objects that represent the concepts and things that your program will wor... | github_jupyter |
# # Appendix E - Avoid hyperparameters
```
import pandas as pd
import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import SpectralClustering
from sklearn.metrics import pairwise_distances
from sklearn.cluster import KMeans
from numba import jit, prange
import plotly.graph_obje... | github_jupyter |
```
import pandas as pd
import numpy as np
import os
import regex as re
from collections import Counter, defaultdict
import sys
CONST_A = 0
CONST_C = 1
CONST_G = 2
CONST_T = 3
CONST_NT_MAP = ['A', 'C', 'G', 'T']
def remove_duplicates_round(df,hamm_thres=4,merge_counts=False):
seqs = list(df.Seq.values)
count... | github_jupyter |
```
#copied from https://colab.research.google.com/drive/1pTuQhug6Dhl9XalKB0zUGf4FIdYFlpcX#scrollTo=Z474sSC6oe7A
# import tensorflow as tf
# # Get the GPU device name.
# device_name = tf.test.gpu_device_name()
# # The device name should look like the following:
# if device_name == '/device:GPU:0':
# print('Found ... | github_jupyter |
# Polynomial Regression using MinMax Scaler
This code template is for the regression analysis using Polynomial Regression and feature rescaling technique called MinMaxScaler
### Required Packages
```
import warnings
import numpy as np
import pandas as pd
import seaborn as se
import matplotlib.pyplot as plt
from... | github_jupyter |
>[Prerequisites (downloading tensorflow_models and checkpoints)](#scrollTo=T_cETKXHDTXu)
>[Checkpoint based inference](#scrollTo=fxMe7_pkk_Vo)
>[Frozen inference](#scrollTo=PlwvpK3ElBk6)
# Prerequisites (downloading tensorflow_models and checkpoints)
```
!git clone https://github.com/tensorflow/models
from __future... | github_jupyter |
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