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# Playground 4: Segmentation workflows for curvi-linear structures
This notebook contains the workflows for Sec61 beta, Tom20 and lamin B1 (mitosis-specific), and serves as a starting point for developing a classic segmentation workflow for your data with curvilinear shapes.
----------------------------------------
... | github_jupyter |
# Clustering Categorical Peoples Interests - Random Forest
Welcome to this project, the codes was created by Benyamin Dariadi.
This project uses a dataset from kaggle. All of the datasets are come from this [link](https://www.kaggle.com/rainbowgirl/clustering-categorical-peoples-interests)
Description
The datasets ... | github_jupyter |
# Idle tomography
This tutorial demonstrates how to run idle tomography on a multi-qubits system. Idle tomography is a protocol which characterizes the errors present in an idle operation using data from a small number of intuitive circuits. If $\tilde{I}$ is the noisy idle operation being characterized and we write ... | github_jupyter |
## AirBnB Seattle Analysis with CRISP-DM (Cross Industry Standard Process for Data Mining)
A brief analysis example using CRISP-DM methodology. This methodology suggest a data analysis in the following steps:
* Business Understanding
* Data understanding
* Data Preparation
* Modeling
* Evaluation
* Deployment
### B... | github_jupyter |
```
# import the necessary packages
import matplotlib.pyplot as plt
from imutils import paths
import numpy as np
import argparse
import imutils
import pickle
import cv2
import os
from sklearn.preprocessing import LabelEncoder
from sklearn.svm import SVC
from imutils.video import VideoStream
from imutils.video import FP... | github_jupyter |
# Visualizacion del Coronavirus (COVID19) Mundial con plotly
por: Jose R. Zapata - https://joserzapata.github.io/
Link: https://joserzapata.github.io/post/covid19-visualizacion/
He visto en las redes sociales varias visualizaciones de los datos del COVID 19 y queria realizarlos en Python para tener la actualizacion ... | github_jupyter |
This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, fig... | github_jupyter |
```
import pandas as pd
import numpy as np
data = pd.read_csv('/home/kirill/Desktop/regex_by_comm.csv')
data.head()
```
Уникальных тегов:
```
data['tag'].nunique()
```
Размер:
```
data['tag'].shape
from collections import Counter
from tqdm import tqdm
data = data.drop(columns='Unnamed: 0', axis=1)
```
Убираем данн... | github_jupyter |
# T81-558: Applications of Deep Neural Networks
**Module 2: Python for Machine Learning**
* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), School of Engineering and Applied Science, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)
* For more information vi... | github_jupyter |
```
#!pip install -e .. --upgrade
from pyjsg.validate_json import JSGPython
```
# JSG Syntax
The names of the various components defined in [Introducing JSON](https://json.org/) are referenced in ***bold italics*** in the document below. Example: A member definition defines the ***string***/***value*** pairs that may... | github_jupyter |
```
# export
import traitlets
import time
import json
import base64
import ipyvuetify
import ipywidgets
import pandas as pd
from markdown import markdown
import ipyvuetify as v
from nbdev.imports import *
from vvapp.outputs import *
from vvapp.inputs import *
# default_exp app_templates
#hide
from nbdev.showdoc import ... | github_jupyter |
**Chapter 4 – Training Models**
_This notebook contains all the sample code and solutions to the exercises in chapter 4._
<table align="left">
<td>
<a href="https://colab.research.google.com/github/ageron/handson-ml3/blob/main/04_training_linear_models.ipynb" target="_parent"><img src="https://colab.research.go... | github_jupyter |
# Labelling Pipeline (Label & Tag)
### Interactive Audio Annotation Tool
```
import librosa
import ipywidgets
from IPython.display import display, Audio
import matplotlib.pyplot as plt
import pandas as pd
import glob, os
import matplotlib.gridspec as gridspec
import numpy as np
import re
os.chdir('/home/abdullah/aveng... | github_jupyter |
# this note book will demonstrate how to simulate diffraction pattern
```
import numpy as np
import matplotlib.pyplot as plt
import pickle
import os
# customized module
import hexomap
from hexomap import reconstruction # g-force caller
from hexomap import MicFileTool # io for reconstruction rst
from hexomap impor... | github_jupyter |
- Import package
```
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.tree import De... | github_jupyter |
# Trabalhando com RDDs de pares chave/valor
#### [Baseado em "Introduction to Spark with Python, by Jose A. Dianes"](https://github.com/jadianes/spark-py-notebooks)
O Spark fornece funções específicas para lidar com RDDs cujos elementos são pares de chave/valor. Eles geralmente são usados para realizar agregações e o... | github_jupyter |
<h2>--- Day 5: Hydrothermal Venture ---</h2>
[](https://mybinder.org/v2/gh/oddrationale/AdventOfCode2021FSharp/main?urlpath=lab%2Ftree%2FDay05.ipynb)
<p>You come across a field of <a href="https://en.wikipedia.org/wiki/Hydrothermal_vent" target="_blank">hydrothermal vents... | github_jupyter |
```
import sys
sys.path.append('../')
import numpy as np
from bareml.supervised.linear_regression import LassoRegression
from bareml.utils.metrics import rmse
from bareml.utils.validation import train_test_split, KFold
from sklearn.linear_model import Ridge, Lasso, ElasticNet
from sklearn.datasets import load_boston... | github_jupyter |
## 1. Welcome to the world of data science
<p>Throughout the world of data science, there are many languages and tools that can be used to complete a given task. While you are often able to use whichever tool you prefer, it is often important for analysts to work with similar platforms so that they can share their code... | github_jupyter |
# Hello World!
Here's an example notebook with some documentation on how to access CMIP data.
```
%matplotlib inline
import xarray as xr
import intake
# util.py is in the local directory
# it contains code that is common across project notebooks
# or routines that are too extensive and might otherwise clutter
# the... | github_jupyter |
**Chapter 15 – Processing Sequences Using RNNs and CNNs**
_This notebook contains all the sample code in chapter 15._
<table align="left">
<td>
<a target="_blank" href="https://colab.research.google.com/github/ageron/handson-ml2/blob/master/15_processing_sequences_using_rnns_and_cnns.ipynb"><img src="https://ww... | github_jupyter |
# Simple Evolutionary Exploration Walkthrough
This notebook contains instructions on how to use the SEE module, along with several examples. These instructions will cover the following parts:
* [Import Image Files](#Import_Image_Files)
* [Manual Search](#Manual_Search)
* [Genetic Algorithm Search](#Genetic_Algorithm... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
```
# SED for Trappist-1
This notebook executes the lines of code presented in Filippazzo et al. (submitted to PASP). Below I generate and analyze an SED for the M8 dwarfs Trappist-1 and Gl 752B. Then I create a catalog of SEDs for analysis.
```
# Imports
from astropy import uni... | github_jupyter |
## Probabilistic Discriminative Models
### Multinomial Distribution
Before getting into Softmax regression, we need to define what is a nultinomial distribution. A discrete random variable is said to have a multinomial distribution when it have multiple levels instead of 2 levels as was the case in binomial distributio... | github_jupyter |
# Code Coverage
In the [previous chapter](Fuzzer.ipynb), we introduced _basic fuzzing_ – that is, generating random inputs to test programs. How do we measure the effectiveness of these tests? One way would be to check the number (and seriousness) of bugs found; but if bugs are scarce, we need a _proxy for the likel... | github_jupyter |
# Linear regression
```
import tensorflow as tf
print(tf.__version__)
```
### This is new chapter
```
%matplotlib inline
import matplotlib.pyplot as plt
from tensorflow.keras import Model
```
Let's create noisy data (100 points) in form of `m * X + b = Y`:
```
def make_noisy_data(w=0.1, b=0.3, n=100):
x = tf.r... | github_jupyter |
We are going to run the entire network on GRWL Tile and then use USGS width data for validation. This provides another avenue for validation. This mirrors the notebook for GRWL validation.
```
import rasterio
import matplotlib.pyplot as plt
import numpy as np
from pathlib import Path
import pyproj
import geopandas as ... | github_jupyter |
##### Copyright 2019 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 |
```
%pylab inline
```
# General
```
import json
import requests
```
### Helper methods
```
import time
def get_unix_time_minus_days(subtract_days):
return int(time.time())-(86400*subtract_days)
def parse_unix_date(posix_time):
return datetime.datetime.utcfromtimestamp(posix_time).strftime('%Y-%m-%dT%H:%M:... | github_jupyter |
```
import pandas as pds
import numpy as np
from pandasql import sqldf
pysqldf = lambda q: sqldf(q, globals()) # define pysqldf function for queries
```
## Load MIxS 5 enviromental package data
```
df = pds.read_excel("data/mixs_v5.xlsx", sheet_name="environmental_packages")
df.head()
```
### Find distinct terms an... | github_jupyter |
# Introduction
This is a basic example of using TOAST interactively for LiteBIRD simulations. This uses an extra package to help displaying things in the notebook. You can install that with `pip install wurlitzer` and restart this notebook kernel.
```
# Built-in modules
import sys
import os
from datetime import dat... | github_jupyter |
```
from itertools import combinations
from collections import defaultdict
import numba
import pandas as pd
import json
```
# Hand Ratings
This notebook calculates and outputs a rating for each subset of cards in a cribbage hand (completely ignoring suit and flushes, which are going to be rare in a 5x5 grid game).
W... | github_jupyter |
# Des succès immédiats
Avec les problèmes à suivre, vous serez amenés à mettre en œuvre rapidement les structures de contrôle.
## Un problème capital
Mettez en majuscules les mots de la liste grâce à une boucle.
```
words = ["A", "Lannister", "always", "pays", "his", "debts."]
```
## Ma liste de courses
Concevez ... | github_jupyter |
## Define the Convolutional Neural Network
After you've looked at the data you're working with and, in this case, know the shapes of the images and of the keypoints, you are ready to define a convolutional neural network that can *learn* from this data.
In this notebook and in `models.py`, you will:
1. Define a CNN w... | github_jupyter |
# Visualizing Periodic Signals Using a River Plot
---
## Learning Goals
By the end of this tutorial, you will:
- Understand what a river plot is.
- Understand when river plots are useful.
- Be able to create and interpret a river plot.
## Introduction
A "river plot" is a method to visualize periodic signals that v... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
from sklearn.datasets import make_classification
X, y = make_classification(n_samples = 2000, n_classes = 2, weights = [1,1], random_state = 1)
#no. of rows # no. of... | github_jupyter |
# Best Value, Fastest Growth, Most Montentum
<img src="../reports/figures/valuation.png" alt="Drawing" width="200">
```
import pandas as pd
import numpy as np
import re
import matplotlib.pyplot as plt
import yfinance as yf
from datetime import date, timedelta, datetime as dt
from selenium import webdriver
from seleni... | github_jupyter |
```
from local_vars import root_folder
data_folder = r"CirclesA"
image_size = 128
batch_size = 50
import time
start_time = time.time()
import itertools
import keras
from keras.models import Sequential
from keras.layers import Activation, GlobalAveragePooling2D
from keras.layers.core import Dense, Dropout, Flatten
f... | github_jupyter |
<center><h2><strong><font color="blue">Social Network Analysis (SNA)</font></strong></h2></center>
<center><h3><strong><font color="blue"><a href="https://taudata.blogspot.com">https://taudata.blogspot.com</a></font></strong></h3></center>
<img alt="" src="images/covers/taudata-cover.jpg"/>
<center><h2><strong><font ... | github_jupyter |
# Detect Prediction Anomalies with Model Monitor
Model Monitor captures the input, output and metadata for model predictions. You can continuously analyze and monitor data quality.
Based on these notebooks:
* https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker_model_monitor/introduction/Sag... | github_jupyter |
# Getting Started Guide
## Table of Contents
- [Using Coach from the Command Line](#Using-Coach-from-the-Command-Line)
- [Using Coach as a Library](#Using-Coach-as-a-Library)
- [Preset based - using `CoachInterface`](#Preset-based---using-CoachInterface)
- [Training a preset](#Training-a-preset)
- ... | github_jupyter |
# DSGRN Python Interface Tutorial
This notebook shows the basics of manipulating DSGRN with the python interface.
```
import DSGRN
```
## Network
The starting point of the DSGRN analysis is a network specification.
We write each node name, a colon, and then a formula specifying how it reacts to its inputs.
```
netwo... | github_jupyter |
# Model-Based Reinforcement Learning
## Principle
We consider the optimal control problem of an MDP with a **known** reward function $R$ and subject to **unknown deterministic** dynamics $s_{t+1} = f(s_t, a_t)$:
$$\max_{(a_0,a_1,\dotsc)} \sum_{t=0}^\infty \gamma^t R(s_t,a_t)$$
In **model-based reinforcement learning... | github_jupyter |
```
'''
Adapted from Stanford ADMM Linear SVM code
https://web.stanford.edu/~boyd/papers/admm/svm/linear_svm.html
'''
import matplotlib.pyplot as plt
import cvxpy as cp
import numpy as np
plt.rcParams.update({
"text.usetex": True,
"font.family": "sans-serif",
"font.sans-serif": ["Helvetica Neue"],
"fon... | github_jupyter |
# 2.1 Архитектура HDFS
## HDFS хорошо подходит для
- Хранение больших файлов:
- Терабайты, петабайты.
- Миллионы, но не миллиарды файлов.
- Файлы размером от 100 мб. Желательно не хранить маленькие файлы.
- Стриминг данных:
- Паттерн *write once / read many times*. Лучше не использовать, если данные ча... | github_jupyter |
# End to End example to manage lifecycle of ML models deployed on the edge using SageMaker Edge Manager + GreenGrass v2
**SageMaker Studio Kernel**: Data Science
## Contents
* Use Case
* Workflow
* Setup
* Building and Deploying the ML Model
* Deploy Wind Turbine application to EC2 with Greengrass V2
* Cleanup
## ... | github_jupyter |
# Model Evaluation
```
import os
import gym
import gym_donkeycar
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cv2 as cv
import time
import pickle
#import birds_eye_vector_space
import basis_my_cv
import random
from pandas import Series, DataFrame
from collections import deque
#from ke... | github_jupyter |
```
import os
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
from torchvision import models
import numpy as np
```
### Load ResNet50 pre-trained model
```
# Download the ResNet50 pre-trained model.
resnet50_model = models.resnet50(pret... | github_jupyter |
## Final Team BuzzFeed Predictor
### Step 1: Load Data and Clean it up
#### A. Features: Clean Null
#### B. Target: Normalize - use (freq, Impressions) and max_impressions
Use Viral, Non-Viral (Pick -1 Std. Dev. as an arbitrary marker)
Try Multiple Classes: 1 Buzz (Bottom quartile), 2 (Middle 50%) Buzz and 3(Top Quar... | github_jupyter |
# Deep Q-Network (DQN)
---
In this notebook, you will implement a DQN agent with OpenAI Gym's LunarLander-v2 environment.
### 1. Import the Necessary Packages
```
import gym
import random
import torch
import numpy as np
from collections import deque
import matplotlib.pyplot as plt
%matplotlib inline
EXPERIMENT_NAME ... | github_jupyter |
# Overview
This notebook introduces you MONAI's image transformation module.
```
# Copyright 2020 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/l... | github_jupyter |
Import Respository (Replace the Access Token)
```
!git clone https://username:<access_token>@github.com/ganeshh123/cloudGAN.git
%cd /content/cloudGAN/
!ls -a -l
```
Install Requirements
```
!pip3 install -r src/requirements.txt
```
Setup
```
#Create env file
import os
import zipfile
import sys
from pathlib import ... | github_jupyter |
# Experiments with word embeddings
In this notebook, we'll have some fun with **<font color="magenta">word embeddings</font>**: distributed representations of words.
We'll see how such an embedding can be constructed by applying principal component analysis to a suitably transformed matrix of word co-occurrence prob... | github_jupyter |
# Navigation MDP [1]
```
import numpy as np
from simple_rl.tasks import NavigationMDP
from simple_rl.agents import QLearningAgent
from simple_rl.planning import ValueIteration
from simple_rl.tasks.grid_world.GridWorldStateClass import GridWorldState
%matplotlib inline
%load_ext autoreload
%autoreload 2
np.random.seed... | github_jupyter |
## 1. Sex is included; we need to update in .py
## 2. How about changing CHILDREN_COUNT to NUM_CHILDREN?
## 3.
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
HOUSEHOLD_ID = 'hhid'
HOUSEHOLD_SIZE = 'hhsize'
VEHICLE_COUNT = 'vehicle_count'
CHILDREN_COUNT = 'numchildren'
... | github_jupyter |
<a href="https://colab.research.google.com/github/JoshStrong/MAML/blob/master/CIFAR_FS_maml.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!pip install learn2learn
import random
import numpy as np
import torch
from torch import nn, optim
impo... | github_jupyter |
<a href="https://colab.research.google.com/github/mrdbourke/pytorch-deep-learning/blob/main/08_pytorch_paper_replicating.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# (WIP) 08. PyTorch Paper Replicating
Want to recreate ViT paper: "An Image is ... | github_jupyter |
# Tutorial 1.4. Introduction to Statistical Quantities in Wind Engineering
## Part 2: Extreme Value analysis
### Description: Tools for extreme values statistics are addressed with computations demonstrated for the generated signal in Part 1. Some additional exercises are proposed for individual studies.
#### Studen... | github_jupyter |
Wayne Nixalo - 1 Jul 2017
RNN practice in Theano -- 4th attempt
Implementing dimensionality for context idea.
## Theano RNN
```
import theano
import os, sys
sys.path.insert(1, os.path.join('../utils'))
from utils import *
# from __future__ import division, print_functions
path = get_file('nietzsche.txt', origin="ht... | github_jupyter |
```
# This code is for adaptive GPU usage
import keras.backend as K
cfg = K.tf.ConfigProto()
cfg.gpu_options.allow_growth = True
K.set_session(K.tf.Session(config=cfg))
import datetime
import pandas as pd
import numpy as np
```
# Creating the Weekly DataFrame
```
def addWeek(df):
# Creating Last Date Column
l... | github_jupyter |
<a href="http://landlab.github.io"><img style="float: left" src="../../../landlab_header.png"></a>
# Quantifying river channel evolution with Landlab
These exercises are based on a project orginally designed by Kelin Whipple at Arizona State University. This notebook was created by Nicole Gasparini at Tulane Universit... | github_jupyter |
```
# -*- coding: utf-8 -*-
import urllib2
import re
import string
import operator
#剔除常用字函数
def isCommon(ngram):
commonWords = ["the", "be", "and", "of", "a", "in", "to", "have",
"it", "i", "that", "for", "you", "he", "with", "on", "do", "say",
"this", "they", "is", "an", "at... | github_jupyter |
# Chapter 04 -- Pandas, Part 1
<h2 id="Topics-covered:">Topics covered:</h2>
<ul>
<li><a href="http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2004%20--%20Pandas%2C%20Part%201.ipynb#Importing-Packages" target="_blank">Importing Packages</a></li>
<li><a href="http://nbviewer.... | github_jupyter |
```
%pylab inline
import os, time
DATAFOLDER = '/home/d/Dropbox/TRAKODATA/qfib-data/'
QFIB = '/home/d/Projects/qfib/qfib'
point1 = np.array([1,2,3])
point2 = np.array([2,3,4])
np.linalg.norm(point1[0:3]-point2[0:3])
np.linalg.norm(point1[0]-point2[0])
np.linalg.norm(point1[1]-point2[1])
np.linalg.norm(point1[2]-point2[... | github_jupyter |
```
import numpy as np
import pandas as pd
import time
from matplotlib import pyplot as plt
%matplotlib inline
# from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import StratifiedKFold
# from sklearn.model_selection import LeaveOneOut
from sklearn.linear_model import LogisticRegression
... | github_jupyter |
# Feature Selection
O objetivo da seleção de recursos é duplo: queremos melhorar a eficiência computacional e reduzir o erro de generalização do modelo removendo recursos irrelevantes ou ruído. Esse tipo de abordagem é especialmente importante quando não estamos utilizando uma regularização forte.
## Benchmark
```
f... | github_jupyter |
# The Image Classification Dataset
:label:`sec_fashion_mnist`
(~~The MNIST dataset is one of the widely used dataset for image classification, while it's too simple as a benchmark dataset. We will use the similar, but more complex Fashion-MNIST dataset~~)
One of the widely used dataset for image classification is the... | github_jupyter |
# Political Organizations Activity on Wikipedia aggregated by Party
The parameters in the cell below can be adjusted to explore other political parties and time frames.
### How to explore other political parties?
The ***organization*** parameter can be use to aggregate organizations by their party. The column `subcat... | github_jupyter |
```
import os
import sys
import glob
import pickle
import itertools
import random
from IPython.display import Image
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
from matplotlib.colors import ListedColormap
from sklearn.metrics import confusion_matrix
from sklearn.manifold import TS... | github_jupyter |
# Example: Regenerating Data from
# [J.T. Gostick et al. / JPS 173 (2007) 277–290](http://www.sciencedirect.com/science/article/pii/S0378775307009056)
## Getting Started
In this tutorial, we will regenerate data from J.T. Gostick's 2007 paper [[1]](http://www.sciencedirect.com/science/article/pii/S0378775307009056). ... | github_jupyter |
# Ground-State: Heisenberg model
Author: Giuseppe Carleo and Filippo Vicentini (EPFL-CQSL)
The goal of this tutorial is to review various neural network architectures available in NetKet, in order to learn the ground-state of a paradigmatic spin model: the spin-$1/2$ Heisenberg antiferromagnetic chain.
The Hamiltoni... | github_jupyter |
# 使用 Spotpy
根据[spotpy](https://github.com/thouska/spotpy)的介绍，它是一个支持模型率定及不确定性和灵敏度分析中优化技术的python框架，其简单性和灵活性使得无需复杂的代码就可以针对各类模型使用各种算法。
这里主要结合实例介绍其中水文领域经常用到的一个优化算法 SCE-UA的使用，后面再根据实际使用情况更新补充。
因为本文主要以SCE-UA为例，所以不必须贝叶斯推断相关知识，不过如果想要进一步使用 spotpy，必要的知识背景是不可少的，下面介绍框架也有一些术语涉及到了，可以参考以下资料：
- [CamDavidsonPilon/Probabilistic-Progra... | github_jupyter |
# Symmetric Encryption
**ToDo**:
- Add illustration for Symmetric Encryption - Similar to [this](https://miro.medium.com/max/1400/1*mnyITCNnRdeLfauh3Psmlw.png).
- Add illustration for authenticated vs non-autenticated encryption.
- Explain difference between EtM, E&M and MtE - See [this](https://en.wikipedia.org/wiki/... | github_jupyter |
# Evaluation DYMOST OI:
This notebook presents the evaluation of the SSH reconstructions based on the DYMOST OI ([Ubelmann et al., 2016](https://journals.ametsoc.org/view/journals/atot/33/8/jtech-d-15-0163_1.xml), [Ballarotta et al., 2020](https://journals.ametsoc.org/view/journals/atot/37/9/jtechD200030.xml)) and pe... | github_jupyter |
# Text Classification
Text classification is the process of assigning tags or categories to text according to its content. It’s one of the fundamental tasks in natural language processing.
The text we wanna classify is given as input to an algorithm, the algorithm will then analyze the text’s content, and then categ... | github_jupyter |
## Dependencies
```
import os
import cv2
import shutil
import random
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import imgaug as ia
from imgaug import augmenters as iaa
from albumentations import *
from sklearn.utils import class_weight, shuffle
from sk... | github_jupyter |
Numba 0.44.0 Release Demo
=======================
This notebook contains a demonstration of new features present in the 0.44.0 release of Numba. Whilst release notes are produced as part of the [`CHANGE_LOG`](https://github.com/numba/numba/blob/5bffb209e853dc21a44a5bd801c93672404f1fe8/CHANGE_LOG), there's nothing like... | github_jupyter |
<!--NAVIGATION-->
< [More IPython Resources](01.08-More-IPython-Resources.ipynb) | [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) >
# Introduction to NumPy
This chapter, along with chapter 3, outlines techniques for effectively loading, storing, and manipulating in-memory data in Python.
T... | github_jupyter |
# Spiral Problem
This document presents a fictitious problem of learning the length of a spiral;
The equation of the features $x_1$ and $x_2$ and target is given by:
\begin{eqnarray}
x_1 &=& \theta \cos(\theta) + \epsilon_1 ~~~~~~ x_2 = \theta \sin(\theta) + \epsilon_2 \\
y &=& \frac{1}{2}\left[ \theta \sqrt{... | github_jupyter |
```
import os
import codecs
from datetime import datetime
def join_lines(**kwargs):
"""
Le o arquivo de entrada, caso algum dos registros tenha quantidade de colunas diferente
do cabecalho, a linha seguinte e concatenada com a atual e escrita no arquivo de saida.
"""
# Log: Mensagem de inicio d... | github_jupyter |
```
import gdal, osr
import numpy as np
from skimage.graph import route_through_array
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats
import os
import math
from osgeo import ogr
import fiona
import jenkspy
```
## For working with rasters
The Raster files are converted to numpy array for fu... | github_jupyter |
```
from zipline.pipeline import Pipeline
from zipline.component.research import run_pipeline
from zipline.pipeline.data import USEquityPricing
from zipline.pipeline.factors import SimpleMovingAverage
```
## Filters
A Filter is a function from an asset and a moment in time to a boolean:
```
F(asset, timestamp) -> boo... | github_jupyter |
```
# This imports the OpenContextAPI from the api.py file in the
# opencontext directory.
%run '../opencontext/api.py'
import numpy as np
import pandas as pd
oc_api = OpenContextAPI()
# Clear old cached records.
oc_api.clear_api_cache()
# This is a search url for bovid tibias.
url = 'https://opencontext.org/subject... | github_jupyter |
IMPORTING IMPORTANT MODULES
```
import tensorflow as tf
from tensorflow.keras.datasets import fashion_mnist
import numpy as np
import matplotlib.pyplot as plt
import numpy as np
from tensorflow.keras import layers
import time
from tensorflow.keras.models import Sequential, load_model
```
LOADING DATASET
```
(x,_),(... | github_jupyter |
#Libraries
```
import pandas as pd
import os, sys, time, random
import numpy as np
from scipy import stats
sys.path.append('../')
from RASLseqTools import *
sys.path.append('../RASLseqTools')
import RASLseqAnalysis_STAR
import seaborn
%pylab inline
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
```
... | github_jupyter |
# Entity Extraction from old-style SciSpacy NER Models
These models identify the entity span in an input sentence, but don't attempt to separately link to an external taxonomy. The following variations are possible here. Replace the `MODEL_NAME, MODEL_ALIAS` line in the cell below and repeat run to extract named entit... | github_jupyter |
<a id='HOME'></a>
# CHAPTER 6 Oh Oh: Objects and Classes
## 物件與類別
* [6.1 什麼是物件](#Objects)
* [6.2 使用class定義類別](#Class)
* [6.3 繼承](#Inheritance)
* [6.4 覆蓋方法](#Override)
* [6.5 添加新方法](#Add)
* [6.6 使用super得到父類別支援](#super)
* [6.7 self](#self)
* [6.8 設置與呼叫特性的屬性](#Attribute)
* [6.9 使用名稱重整保持私有性](#Privacy)
* [6.10 方法的類別](#Type... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import numpy as np
import pandas as pd
from scipy import signal as sg
import sys
sys.path.append('..')
data_base = '../data/raw/dataIBM'
data_exts = '.csv'
targ_df = pd.read_csv(data_base+data_exts, header=None).astype('float64')
idx = 2
if idx==0:
from SpiCoder.Batch import T... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
import os
import pickle
OMNIGLOT_DATA = os.path.join(os.getcwd(), 'omniglot/')
DATASET_DIR = os.path.join(os.getcwd(), 'cluttered_omniglot/')
import time
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (12.0, 12.0)
import ... | github_jupyter |
```
import quantecon as qe
import matplotlib.pyplot as plt
import matplotlib
%matplotlib notebook
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
font = {'weight' : 'medium',
'size' : 13}
matplotlib.rc('font', **font)
from numba import njit, vectorize, float64
from numpy import sin, cos, sqr... | github_jupyter |
# Importing the libraries
```
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.io as pio
pio.renderers.default = "svg" # to make graphs visible on github
import datetime as dt
from sklearn.preprocessing import MinMaxScaler
import tensorflow as tf
import tensorflow.keras.layers a... | github_jupyter |
# Parse Swedish gigaword XML
> "Dataset"
- toc: false
- branch: master
- badges: false
- comments: true
- categories: [swedish, gigaword, xml]
```
example = """\
<corpus id="1960-0000">
<text date="1965-02-14" datefrom="19650214" dateto="19650214" genre="news" publisher="Stockholms Tidningen " timefrom="000000" time... | github_jupyter |
```
import os
import torch
import torch.nn as nn
from datasets import get_ds
from cfg import get_cfg
from methods import get_method
import numpy as np
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
import matplotlib
from eval.get_data import get_data
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.e... | github_jupyter |
# Fun with Funnels
### By Alex Frieder
## Introduction
Imagine you were just hired by a company that has runs an online marketplace. Their website allows users to sign up for an account, log in to their account, search for products, leave reviews for products, and purchase products. They've hired you because their sa... | github_jupyter |
# Movies
This notebook was originally authored by Abhijit Dasgupta and was adapted from [Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do) by Wes McKinney
## Objectives
* What are the highest rated movies?
* What is the best movie for date night?
* Which movies do men and women disagree on ... | github_jupyter |
# Non-Negative Matrix Factorization
##### Using NNMF to uncover spatial components of individual player contribution.
Heavily inspired by [Justin Jacobs](https://twitter.com/Squared2020) 2018 blog post [Understanding Trends in the NBA: How NNMF Works](https://squared2020.com/2018/10/04/understanding-trends-in-the-nba... | github_jupyter |
# Predicting Product Success When Review Data Is Available
_**Using XGBoost to Predict Whether Sales will Exceed the "Hit" Threshold**_
---
---
## Contents
1. [Background](#Background)
1. [Setup](#Setup)
1. [Data](#Data)
1. [Train](#Train)
1. [Host](#Host)
1. [Evaluation](#Evaluation)
1. [Extensions](#Extensions)
... | github_jupyter |
<font face="Calibri" size="2"> <i>Open SAR Toolkit - Tutorial 1, version 1.3, July 2020. Andreas Vollrath, ESA/ESRIN phi-lab</i>
</font>
--------
# OST Tutorial I
## Pre-processing your first Sentine... | github_jupyter |
# Conditional Probability Activity & Exercise
Below is some code to create some fake data on how much stuff people purchase given their age range.
It generates 100,000 random "people" and randomly assigns them as being in their 20's, 30's, 40's, 50's, 60's, or 70's.
It then assigns a lower probability for young peop... | github_jupyter |
# 2D Four-well potential
```
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
from pydiffmap import diffusion_map as dm
%matplotlib inline
```
Load sampled data: discretized Langevin dynamics at temperature T=1, friction 1, and time step size dt=0.01, with double-well poten... | github_jupyter |
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