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# Introduction to NUMPY
Numpy is a the ideal tool to work with datasets. It is much faster than using python on its own
# Contents
- [raw python vs numpy](#pythonvsnumpy)
- [vectorisation](#vectorisation)
- [create](#create)
- [size](#size)
- [resize](#resize)
- [indexing](#indexing)
- [multi axis indexing](#multiin... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import arviz as az
from statsmodels.tsa import stattools
import statsmodels.api as sm
import pymc3 as pm
import pymc
import sys
sys.path.insert(0, '..')
from utils.plot_lib import set_default
set_default(figsize=(6, 4))... | github_jupyter |
# DLProfile Example using Cosmic Tagger Application
## Set imports and neccessary environment variables
```
import pathlib
import os
import sys
import matplotlib.pyplot as plt
import warnings
import pprint
import pandas
VANIDL_DIR="{}".format(pathlib.Path(os.getcwd()).parent.parent.parent.absolute())
sys.path.insert(... | 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 |
# Project: Part of Speech Tagging with Hidden Markov Models
---
### Introduction
Part of speech tagging is the process of determining the syntactic category of a word from the words in its surrounding context. It is often used to help disambiguate natural language phrases because it can be done quickly with high accu... | github_jupyter |
```
import os
import pandas as pd
import glob
import tempfile
from pathlib import Path
```
#### Provide storage account parameters here
###### storage_conn_string "Storage account connection string"
###### src_container "Container where data is stored"
###### dst_container "Container where results should be uploaded"... | github_jupyter |
# Topic Modeling: Financial News
This notebook contains an example of LDA applied to financial news articles.
## Imports & Settings
```
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
from collections import Counter
from pathlib import Path
import logging
import numpy as np
import pandas as pd... | github_jupyter |
# Comic Book Cancellations Part I: Web Scraping
While some Marvel comic books run for decades, most series go through cycles. For example, [Charles Soule's *She-Hulk* (2014)](https://www.cbr.com/charles-soule-investigates-she-hulks-blue-file/) was a colorful and quirky crime serial that got cancelled on its 12th issue... | github_jupyter |
---
## 30. Integraciรณn Numรฉrica
Eduard Larraรฑaga (ealarranaga@unal.edu.co)
---
### Resumen
En este cuaderno se presentan algunas tรฉcnicas de integraciรณn numรฉrica.
---
Una de las tareas mรกs comunes en astrofรญsica es evaluar integrales como
\begin{equation}
I = \int_a^b... | github_jupyter |
# Step 1 - Downloading Fitbit data via the API
For this initial step, we are using [python-fitbit](https://github.com/orcasgit/python-fitbit), a Python client accessing the Fitbit API. We furthermore require an exisiting Fitbit OAuth 2.0 Client (Consumer) ID and Client (Consumer) Secret. These can be obtained by regis... | github_jupyter |
##### Copyright 2020 The TensorFlow Hub Authors.
Licensed under the Apache License, Version 2.0 (the "License");
```
#@title Copyright 2020 The TensorFlow Hub Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the Lic... | github_jupyter |
# Experiment 02: Study performance stability over time
Study how well models trained on images from early dates perform at test time on images from later dates. This is meant to investigate how stable model performance is over time, as news rooms' image publishing pipelines (possibly) evolve.
For each source, sort... | github_jupyter |
```
# Prepare environment
import os, sys
sys.path.insert(0, os.path.abspath('..'))
from IPython.display import display
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
```
## Example starts here
------
```
import asyncio
from ibstract import IB
from ibstract i... | github_jupyter |
# RNN and LSTM Assignment
<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/3/3c/Chimpanzee_seated_at_typewriter.jpg/603px-Chimpanzee_seated_at_typewriter.jpg" width=400px>
It is said that [infinite monkeys typing for an infinite amount of time](https://en.wikipedia.org/wiki/Infinite_monkey_theorem) will... | github_jupyter |
Deep Learning
=============
Assignment 3
------------
Previously in `2_fullyconnected.ipynb`, you trained a logistic regression and a neural network model.
The goal of this assignment is to explore regularization techniques.
```
# These are all the modules we'll be using later. Make sure you can import them
# befor... | github_jupyter |
# Data Science Bootcamp - The Bridge
## Precurso
En este notebook vamos a ver, uno a uno, los conceptos bรกsicos de Python. Constarรกn de ejercicios prรกcticos acompaรฑados de una explicaciรณn teรณrica dada por el profesor.
Los siguientes enlaces estรกn recomendados para el alumno para profundizar y reforzar conceptos a par... | github_jupyter |
# Maps
## 1. Introduction
Maps are a way to present information on a (roughly) spherical earth on a flat plane, like a page or a screen. Here are two examples of common map projections. The projection is only accurate in the region where the plane touches the sphere, and is less accurate as the distance between the p... | github_jupyter |
# Extra Trees Classifier
This Code template is for the Classification tasks using simple ExtraTreesClassifier based on the Extremely randomized trees algorithm.
### Required Packages
```
import numpy as np
import pandas as pd
import seaborn as se
import warnings
import matplotlib.pyplot as plt
from sklearn.ense... | github_jupyter |
# Contest rating change prediction for user using KNN algorithm.
We will try to predict rating change based on previous contests - duration, authors, contest beginning hour, previous performances of the user and ratings.
## Imports
```
from database import *
import numpy as np
from IPython.display import display, cl... | github_jupyter |
# Dynamic Content Personalization Using LinUCB
This is a reference implementation of a recommendation system that dynamically learns the mapping between users and items that maximizes the conversion rates.
### Data
Simulator, no external dependencies
### References
1. Li L., Chu W., Langford J., Schapire R. -- A Co... | github_jupyter |
```
import time
import datetime
import numpy as np
import pandas as pd
import datatable as dt
pd.set_option("display.max_columns", None, "display.max_rows", None)
import tensorflow as tf
from tensorflow import keras
import tensorflow_addons as tfa
import tensorflow_probability as tfp
from tensorflow.keras import layer... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
# For comparison
import csr2d.core2
```
# 3D CSR Potentials
```
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
%matplotlib notebook
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
#sigma_z = 40e-6
#sigma_x = 134e-6
#rho = 1538.
#gamma = ... | github_jupyter |
```
import baryonification as bfc
from scipy.interpolate import splrep, splev
from scipy.integrate import quad
import matplotlib.pyplot as plt
import numpy as np
def cvir_fct(mvir):
"""
Concentrations form Dutton+Maccio (2014)
c200 (200 times RHOC)
Assumes PLANCK coismology
"""
A = 1.025
B ... | github_jupyter |
# Machine Vision Applications
Examples of Machine Vision Requirements
* Visualize droplets ranging in size from 10 to 100 microns.
* Visualize a field with 1 million drops
* Classify 10 micron particles
Questions
* Are the particles in motion?
* How much time is available to capture the image?
* Do we need a CFA or... | github_jupyter |
## NSE-TATAGLOBAL DATASETS
## Stock Market Prediction And Forecasting Using Stacked LSTM
# LGMVIP Task-2|| Data Science
### To build the stock price prediction model, we will use the NSE TATA GLOBAL dataset. This is a dataset of Tata Beverages from Tata Global Beverages Limited, National Stock Exchange of India: Tat... | github_jupyter |
# Neural Networks - Part 2
2016-09-16, Josh Montague
## The Plan
- Quick review of [Part 1](https://github.com/DrSkippy/Data-Science-45min-Intros/tree/master/neural-networks-101)
- The library stack (Keras, Theano, Numpy, oh my!)
- Examples!
- Classification (Iris)
- Classification (MNIST)
- Regression ... | github_jupyter |
```
import matplotlib.pyplot as plt
import numpy as np
import os
from scipy import stats
import glob
from scipy.stats import ks_2samp, kstest
%matplotlib inline
def load_summary(filename):
dtype=[('minr', 'f8'),
('maxr', 'f8'),
('ca_ratio', 'f8'),
('ba_ratio', 'f8'),
('a... | github_jupyter |
<img src="figures/ampel_multi.png" width="600">
### AMPEL and the Vera Rubin Observatory
The Vera Rubin Observatory, and the LSST survey, will provide a legacy collection of real-time data. Considering the potential long term impact of any transient programs, the AMPEL analysis platform was developed to
host complex... | github_jupyter |
# Chapter 10: RNN(Recurrent Neural Network) Application in IMDB Reviews and Sarcasm Reviews Dataset
```
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import tensorflow_datasets as tfds
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence im... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import datetime
import os, sys
import numpy as np
import matplotlib.pyplot as plt
import casadi as cas
##### For viewing the videos in Jupyter Notebook
import io
import base64
from IPython.display import HTML
# from ..</src> import car_plotting
# from .import src.car_plotting
P... | github_jupyter |
# Extensionmethods
Not all operators are loaded at import of rx.
```
# Example: from_marbles
import rx
try:
rx.Observable.from_marbles('a-b|')
except Exception as ex:
print 'error:', ex # shown only after ipython notebook kernel restart
# -> to see whats there don't use e.g. `dir(Observable)` but find
# 'def ... | github_jupyter |
# CDSAPI request examples
## Python workshop, EMS2019
### A few tips before we begin
- Use CDS Web download to construct the base of your request and then build on it.
- Reanalysis ERA5 data is originally stored in GRIB format and when you download it as netCDF, conversion will fail if there is more than one **Pro... | github_jupyter |
## Exploration of GradientSHAP with binary MNIST
**Function : Exploration of GradientSHAP with binary MNIST**<br>
**Author : Team DIANNA**<br>
**Contributor :**<br>
**First Built : 2021.06.28**<br>
**Last Update : 2021.07.06**<br>
**Library : os, numpy, matplotlib, torch, captum**<b... | github_jupyter |
###### Content provided under a Creative Commons Attribution license, CC-BY 4.0; code under BSD 3-Clause license. (c)2014 Lorena A. Barba, Olivier Mesnard. Thanks: NSF for support via CAREER award #1149784.
# Lift on a cylinder
Remember when we computed uniform flow past a [doublet](03_Lesson03_doublet.ipynb)? The st... | github_jupyter |
```
import os
import numpy as np
data_folder = os.path.join(os.path.expanduser("~"), "Data", "websites", "textonly")
documents = [open(os.path.join(data_folder, filename)).read() for filename in os.listdir(data_folder)]
len(documents)
pprint([document[:100] for document in documents[:5]])
from sklearn.cluster import K... | github_jupyter |
```
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import torch.optim as optim
import pandas as pd
import numpy as np
# own Modules
from models_sub_net_ls import LstmMse_LatentSpace, LstmMle_LatentSpace, AnalysisLayer
from data_preperator import DataPreperatorPrediction
from data_set impor... | github_jupyter |
# 1. Inference on Synthetic data
Author: [Marc Lelarge](https://www.di.ens.fr/~lelarge/)
Date: 04/05
In this notebook, we test our approach on synthetic data.
The problem can be described as follows: we are given a familly of ODEs $y'=h_\theta(y,t)$, where the function $h$ is parametrized by the parameter $\theta$ ... | github_jupyter |
```
import pandas as pd, numpy as np
import pyspark
from pyspark.sql import SparkSession
from pyspark.sql.types import *
raw_data_df = pd.read_csv('IPS_payload_200000_df.csv')
raw_data_df.columns
import os
java11_location= '/opt/homebrew/opt/openjdk@11'
os.environ['JAVA_HOME'] = java11_location
conf = pyspark.SparkConf... | github_jupyter |
## SFC Meteorology Obs from:
** - 2017 C2 (CKITAEM-2A) **
*** - 2017 M2 (BSM-2A) ***
__pyversion__==3.6
__author__==S.Bell
```
%matplotlib inline
import datetime
print("Last run {0}".format(datetime.datetime.now()))
```
### connecting to erddap and retrieving and basic information
```
from erddapy import ERDD... | github_jupyter |
# 04 - Full Waveform Inversion with Devito and Dask
## Introduction
In this tutorial, we will build on the [previous](https://github.com/devitocodes/devito/blob/master/examples/seismic/tutorials/03_fwi.ipynb) FWI tutorial and implement parallel versions of both forward modeling and FWI objective functions. Furthermor... | github_jupyter |
#Fire up graphlab create
```
import graphlab
```
#Load some house sales data
Dataset is from house sales in King County, the region where the city of Seattle, WA is located.
```
sales = graphlab.SFrame('home_data.gl/')
sales
```
#Exploring the data for housing sales
The house price is correlated with the number o... | github_jupyter |
# ART decision tree classifier attack
This notebook shows how to compute adversarial examples on decision trees (as described in by Papernot et al. in https://arxiv.org/abs/1605.07277). Due to the structure of the decision tree, an adversarial example can be computed without any explicit gradients, only by traversing ... | github_jupyter |
<!-- </style><figure align = "left" style="page-break-inside: avoid;"><figcaption style="font-weight: bold; font-size:16pt; font-family:inherit;" align="center"></figcaption><br> -->
<img src= "images/APEX.png">
## Introduction: What's APEX?
APEX is a portfolio trade scheduler that optimizes execution with the latest... | github_jupyter |
# Welcome to AI for Science Bootcamp
The objective of this bootcamp is to give an introduction to application of Artificial Intelligence (AI) algorithms in Science ( High Performance Computing(HPC) Simulations ). This bootcamp will introduce participants to fundamentals of AI and how those can be applied to different ... | github_jupyter |
**Chapter 1 โ The Machine Learning landscape**
_This is the code used to generate some of the figures in chapter 1._
# Setup
First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:
```
# T... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#WaveSurfer" data-toc-modified-id="WaveSurfer-1"><span class="toc-item-num">1 </span>WaveSurfer</a></span></li><li><span><a href="#Waveform_playlist" data-toc-modified-id="Waveform_playlist-2"><sp... | github_jupyter |
```
#|hide
#|skip
! [ -e /content ] && pip install -Uqq fastai # upgrade fastai on colab
#|default_exp data.core
#|export
from __future__ import annotations
from fastai.torch_basics import *
from fastai.data.load import *
#|hide
from nbdev.showdoc import *
```
# Data core
> Core functionality for gathering data
The... | github_jupyter |
If you wish to use the `enhanced_pyspark_processor`, be sure that `from sagemaker.spark.processing import PySparkProcessor` is commented out and that you're using `from enhanced_pyspark_processor import PySparkProcessor` instead.
```
import sagemaker
from sagemaker.local import LocalSession
#from sagemaker.spark.proce... | github_jupyter |
```
import pandas as pd
import numpy as np
import re
pd.set_option('display.max_rows', 1500)
pd.set_option('display.max_columns', 42)
pd.set_option('display.max_colwidth', 100)
!dir
upload1 = pd.read_csv('G:\datasets\Rx_Claims\Rx_BenefitPlan_20161101.csv.csv', sep='|', na_values=['nan', ' ', ' '])
upload2 = pd.read_c... | github_jupyter |
# Is there a relationship between GDP per capita and PISA scores?
July 2015
Written by Susan Chen at NYU Stern with help from Professor David Backus
Contact: <jiachen2017@u.northwestern.edu>
##About PISA
Since 2000, the Programme for International Student Assessment (PISA) has been administered every three years ... | github_jupyter |
# Environment setup
```
# Connect to Google Drive
from google.colab import drive
drive.mount('/content/gdrive')
# Copy the dataset from Google Drive to local
!cp "/content/gdrive/My Drive/CBIS_DDSM.zip" .
!unzip -qq CBIS_DDSM.zip
!rm CBIS_DDSM.zip
cbis_path = 'CBIS_DDSM'
# Import libraries
%tensorflow_version 1.x
i... | github_jupyter |
<h1 align="center">EQE512 MATRIX METHODS IN STRUCTURAL ANALYSIS
<br>
<br>
Week 01
<br>
<br>
Defining the solution methods in engineering calculations using matrices and development of algorithms</h1>
<h3 align="center">Dr. Ahmet Anฤฑl Dindar (adindar@gtu.edu.tr)</h3>
<h4 align="center">2017 Fall </h4>
** What is "... | github_jupyter |
# Gaussian Processes
## Introduction
[Gaussian Processes](https://en.wikipedia.org/wiki/Gaussian_process) have been used in supervised, unsupervised, and even reinforcement learning problems and are described by an elegant mathematical theory (for an overview of the subject see [1, 4]). They are also very attractive ... | github_jupyter |
```
# from google.colab import drive
# drive.mount('/content/drive')
# path = "/content/drive/MyDrive/Research/cods_comad_plots/sdc_task/mnist/"
m = 5
desired_num = 500
import torch.nn as nn
import torch.nn.functional as F
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import torch
import torc... | github_jupyter |
<img src='https://assets.leetcode-cn.com/aliyun-lc-upload/uploads/2019/08/17/1336_ex1.jpeg'>
<img src='https://assets.leetcode-cn.com/aliyun-lc-upload/uploads/2019/08/17/1336_ex2.jpeg'>
```
from collections import deque
class Solution:
def maxDistance(self, grid) -> int:
N, q = len(grid), deque()... | github_jupyter |
# Import statements
```
from google.colab import drive
drive.mount('/content/drive')
from my_ml_lib import MetricTools, PlotTools
import os
import numpy as np
import matplotlib.pyplot as plt
import pickle
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
import json
import dat... | github_jupyter |
# Deep Learning with Google Earth Engine, Cloud Storage and AI Platform
This notebook is inspired by the following tutorials:
- [Getting started: Training and prediction with Keras](https://cloud.google.com/ml-engine/docs/tensorflow/getting-started-keras)
- [Down to Earth with AI Platform](https://medium.com/google-e... | github_jupyter |
<b>The Relative Frequency</b> of any random variable is the number of occurance in the total number of observation.
The Relative Frequency is calculated as:<br>
\begin{equation}
Relative Frequency = \frac{Frequency}{Total\ number\ of\ observations}
\end{equation}<br>
E.g. We have a samples are like { 5,7,11,19,23,5,1... | github_jupyter |
# Data Wrangling with Spark SQL Quiz
This quiz uses the same dataset and most of the same questions from the earlier "Quiz - Data Wrangling with Data Frames Jupyter Notebook." For this quiz, however, use Spark SQL instead of Spark Data Frames.
```
from pyspark.sql import SparkSession
# TODOS:
# 1) import any other ... | github_jupyter |
```
from __future__ import division
import pandas as pd
import numpy as np
from sklearn import cluster, datasets, mixture
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from math import pi
from tqdm import tqdm
from torch.distributions.multivaria... | github_jupyter |
<h1 align="center">TensorFlow Neural Network Lab</h1>
์ด๋ฒ ์๊ฐ์๋ ํ
์ํ๋ก์ฐ์ ๊ธฐ๋ณธ์ ์ธ ๋ด์ฉ๋ค์ ์์ฉํ์ฌ ์ํ๋ฒณ์ ์ธ์ํ๋ ๋ชจ๋ธ์ ๋ง๋ค์ด๋ด
๋๋ค.
ํ
์ํ๋ก์ฐ๋ฅผ ์ด์ฉํ ์ฒซ ์ค์ต์ด๋ผ ๋งค์ฐ ๊ธฐ๋๊ฐใ
๋ฉ๋๋ค.
์ ๊ฐ ์ฌ์ฉํ๋ ๋ฐ์ดํฐ๋ <a href="http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html">notMNIST</a> ๋ผ๊ณ ํ๋ ๋ค์ํ ๋ชจ์์ ์ด๋ฏธ์ง๋ก ๊ตฌ์ฑ๋ A-J ์ํ๋ฒณ์
๋๋ค.
<img src="image/notmnist.png">
์ ์ด๋ฏธ์ง๋ ํ์ต์ํฌ ์ํ๋ฒณ์ ... | github_jupyter |
## Figure3_Geographical distribution of data in United States
```
import os
import pickle
import time
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
#importing basemap
import mpl_toolkits
mpl_toolkits.__path__.append('C:/Users/hp/Anaconda3/pkgs/basemap-1.2.0-py37h0354... | github_jupyter |
# ----------ๅฃฐๆๅธธ้----------
```
# Hongjun Wu
# 20180723
# A script that recommends stock based on data and conditions given.
# Import Statement
from selenium import webdriver
from bs4 import BeautifulSoup
from decimal import Decimal
from selenium.common.exceptions import ElementNotVisibleException
import turicreate as... | github_jupyter |
This notebook was prepared by [Donne Martin](https://github.com/donnemartin). Source and license info is on [GitHub](https://github.com/donnemartin/system-design-primer-primer).
# Design a deck of cards
## Constraints and assumptions
* Is this a generic deck of cards for games like poker and black jack?
* Yes, d... | github_jupyter |
## Universidade Federal do Rio Grande do Sul (UFRGS)
Programa de Pรณs-Graduaรงรฃo em Engenharia Civil (PPGEC)
# PEC00025: Introduction to Vibration Theory
### Class 14 - Vibration of beams
[1. The vibrating beam equation](#section_1)
[2. Free vibration solution](#section_2)
[3. Vibration modes and fr... | github_jupyter |
#### New to Plotly?
Plotly's Python library is free and open source! [Get started](https://plotly.com/python/getting-started/) by downloading the client and [reading the primer](https://plotly.com/python/getting-started/).
<br>You can set up Plotly to work in [online](https://plotly.com/python/getting-started/#initiali... | github_jupyter |
# [Optional] Data Preparation
---
This section is **optional**. For the purpose of making this lab as efficient as possible, data sets have already been prepared for you in MXNet [RecordIO format](https://mxnet.incubator.apache.org/versions/master/faq/recordio.html), which has various benefits including performance en... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('ggplot')
# Eventually, for Anaconda warnings.
# Can be commented out.
import warnings
warnings.filterwarnings("ignore")
%matplotlib inline
%load_ext autoreload
%autoreload 2
# Load basic libraries
import seaborn; seaborn.set()
import pickle, copy, ... | github_jupyter |
```
import itertools
from pathlib import Path
import re
import sys
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import scipy.io
from tqdm import tqdm
sns.set_style("whitegrid")
%load_ext autoreload
%autoreload 2
sys.path.append("../src")
import neare... | github_jupyter |
Everything is a network. [Assortativity](http://arxiv.org/pdf/cond-mat/0205405v1.pdf) is an interesting property of networks. It is the tendency of nodes in a network to be attached to other nodes that are similar in some way. In social networks, this is sometimes called "homophily."
One kind of assortativity that is ... | github_jupyter |
# ้ๆฉ
## ๅธๅฐ็ฑปๅใๆฐๅผๅ่กจ่พพๅผ
- ๆณจๆ๏ผๆฏ่พ่ฟ็ฎ็ฌฆ็็ธ็ญๆฏไธคไธช็ญๅท๏ผไธไธช็ญๅฐไปฃ่กจ่ตๅผ
- ๅจPythonไธญๅฏไปฅ็จๆดๅ0ๆฅไปฃ่กจFalse๏ผๅ
ถไปๆฐๅญๆฅไปฃ่กจTrue
- ๅ้ข่ฟไผ่ฎฒๅฐ is ๅจๅคๆญ่ฏญๅฅไธญ็็จๅ
```
a = id(1)
b = id(1)
print(a,b)
# ๅ ไธบaๅbๅนถไธๆฏๅไธไธชๅฏน่ฑก
a is b
a = id(1)
b = a
a is b
a = True
b = False
id(True)
a == b
a is b
```
## ๅญ็ฌฆไธฒ็ๆฏ่พไฝฟ็จASCIIๅผ
```
a = "jokar"
b = "jokar"
a > b
```
## M... | github_jupyter |
# bioimageio.core usage examples
```
import os
import hashlib
import bioimageio.core
import imageio
# we use napari for visualising images, you can install it via `pip install napari` or`conda install napari`
import napari
import numpy as np
import xarray as xr
from bioimageio.core.prediction_pipeline import create_... | github_jupyter |
# Diseรฑo de software para cรณmputo cientรญfico
----
## Unidad 3: NO-SQL
### Agenda de la Unidad 3
---
#### Clase 1
- Lectura y escritura de archivos.
- Persistencia de binarios en Python (pickle).
- Archivos INI/CFG, CSV, JSON, XML y YAML
#### Clase 2
- Bases de datos relacionales y SQL.
### Clase 3
- **Breve rep... | github_jupyter |
# Getting started with Perceptual Adversarial Robustness
This notebook contains examples of how to load a pretrained model, measure LPIPS distance, and construct perceptual and non-perceptual attacks.
If you are running this notebook in Google Colab, it is recommended to use a GPU. You can enable GPU acceleration by ... | github_jupyter |
# Self study 1
In this self study you should work on the code examples below together with the associated questions. The notebook illustrates a basic neural network implementation, where we implement most of the relevant functions from scratch. Except the calculation of gradients, for which we rely on the functionalit... | github_jupyter |
#### New to Plotly?
Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
<br>You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-fo... | github_jupyter |
```
text = """เคฌเฅเคเคฏเคพเคฒเฅ เคฎเฅเค เคญเฅ เคคเฅเคฐเคพ เคนเฅ เคเคฏเคพเคฒ เคเค
"เคเฅเคฏเฅเค เคฌเคฟเคเฅเคจเคพ เคนเฅ เฅเคฐเฅเคฐเฅ?" เคฏเฅ เคธเคตเคพเคฒ เคเค
เคคเฅเคฐเฅ เคจเฅเคฆเฅเคเคฟเคฏเฅเค เคเฅ เฅเฅเคถเฅ เคฌเฅเคนเคฟเคธเคพเคฌ เคฅเฅ
เคนเคฟเคธเฅเคธเฅ เคฎเฅเค เคซเคผเคพเคธเคฒเฅ เคญเฅ เคคเฅเคฐเฅ เคฌเฅเคฎเคฟเคธเคพเคฒ เคเค
เคฎเฅเค เคเฅ เคคเฅเคฎเคธเฅ เคฆเฅเคฐ เคนเฅเค, เคเฅเคฏเฅเค เคฆเฅเคฐ เคฎเฅเค เคฐเคนเฅเค?
เคคเฅเคฐเคพ เคเฅเคฐเฅเคฐ เคนเฅเค
เค เคคเฅ เคซเคผเคพเคธเคฒเคพ เคฎเคฟเคเคพ, เคคเฅ เคเฅเคตเคพเคฌ เคธเคพ เคฎเคฟเคฒเคพ
เคเฅเคฏเฅเค เคเฅเคตเคพเคฌ เคคเฅเฅ เคฆเฅเค?
เคฌเฅเคเคฏเคพเคฒเฅ เคฎเฅเค เคญเฅ เคคเฅเคฐเคพ เคนเฅ เคเคฏเคพเคฒ เคเค
"เคเฅเคฏเฅเค เคเฅเคฆเคพเค เคฆเฅ เคเคฏเคพ เคคเฅ?"... | github_jupyter |
# Machine Learning Models for SCOPE: Passive Aggressive Classifier
Models will be coded here, but the official write up will be in the RMarkdown document.
```
# load the data files
import pandas as pd
import numpy as np
from pymodelutils import utils
logs = pd.read_csv("data/metis_logs.csv")
logs.head()
# filter down... | github_jupyter |
# LABXX: What-if Tool: Model Interpretability Using Mortgage Data
**Learning Objectives**
1. Create a What-if Tool visualization
2. What-if Tool exploration using the XGBoost Model
## Introduction
This notebook shows how to use the [What-if Tool (WIT)](https://pair-code.github.io/what-if-tool/) on a deployed [... | github_jupyter |
# Simulation Experiments
We simulate a greybox fuzing campaign to understand the behavior of discovery probability as more inputs are generated. In contrast to a blackbox fuzzer, a greybox fuzzer adds inputs to the corpus that discover a new species (e.g., that cover a new program branch).
We simulate ๐๐๐๐๐=30 ... | github_jupyter |
# Random Forest
The aim of this part of the workshop is to give you initial experience in using *random forests*, which is a popular ensemble method that was presented earlier in the lectures. A particular emphasis is given to the *out-of-bag* error (sometimes called out-of-sample error) that can be used to select ran... | github_jupyter |
# ์ผ์ฑ์ ์ ์ฒจ๊ธฐ์ฐ ์๊ฐ ์ฌํ
- **Instructor**: Jongwoo Lim / Jiun Bae
- **Email**: [jlim@hanyang.ac.kr](mailto:jlim@hanyang.ac.kr) / [jiun.maydev@gmail.com](mailto:jiun.maydev@gmail.com)
```
from pathlib import Path
import yaml
import numpy as np
import pandas as pd
import torch
from models.mdnet import MDNet, BCELoss, Precisi... | github_jupyter |
# MNIST using RNN
## ์์ํ๊ธฐ
* ์ฌ์ฉํ ๋ผ์ด๋ธ๋ฌ๋ฆฌ๋ฅผ import ํฉ๋๋ค.
* ๋ณธ ์์ ๋ tensorflow๋ฅผ ์ฌ์ฉํฉ๋๋ค.
* ๋ฐ์ดํฐ์
์ tensorflow์์ ์ ๊ณตํ๋ mnist ๋ฐ์ดํฐ์
์ ์ฌ์ฉํฉ๋๋ค.
```
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
import os
import time
tf.reset_default_graph()
%matpl... | github_jupyter |
<a href="https://colab.research.google.com/github/pyGuru123/Data-Analysis-and-Visualization/blob/main/Tracking%20Bird%20Migration/bird_migration.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
One fascinating area of research uses GPS to track movem... | github_jupyter |
```
# default_exp metrics
```
# metrics
> API details.
```
#export
#hide
import numpy as np
import scipy.stats
import torch
from scipy.stats import chi2 as Chi2Dist
import matplotlib.pyplot as plt
from sklearn.metrics import auc
from fastcore.test import *
from fastai.metrics import rmse
import dcor
#export
def crps... | github_jupyter |
# ็ตฑ่จ็ๆจๅฎ
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats
%precision 3
%matplotlib inline
df = pd.read_csv('../data/ch4_scores400.csv')
scores = np.array(df['็นๆฐ'])
p_mean = np.mean(scores)
p_var = np.var(scores)
p_mean, p_var
fig = plt.figure(figsize=(10, 6))
ax = fig... | github_jupyter |
```
import numpy as np
import pandas as pd
np.seterr(divide='ignore', invalid='ignore')
data = pd.io.parsers.read_csv('data/final-new-ratings.csv',
names=['user_id', 'movie_id', 'rating', 'time'],
engine='python', delimiter=';')
movie_data = pd.io.parsers.read... | github_jupyter |
# Regularization
Welcome to the second assignment of this week. Deep Learning models have so much flexibility and capacity that **overfitting can be a serious problem**, if the training dataset is not big enough. Sure it does well on the training set, but the learned network **doesn't generalize to new examples** that... | github_jupyter |
# Title
**Class imbalance: Random Forest vs SMOTE Classification**
# Description
The goal of this exercise is to investigate the performance of Random Forest with and without class balancing techniques on a dataset with class imbalance.
The comparison will look a little something like this:
<img src="../img/image2... | github_jupyter |
# API Demo: Search, Order, and Download Capella's Rotterdam Data Set
Capella has collected two AOIs - 10 x 5 km each. Each AOI will be covered from 8am to 8pm over two days,
6 times each day to simulate roughly two hour revisit at very high resolution.
The main features in the AOIs are: the port, ships (tankers and ... | github_jupyter |
# 19 - Exploring Well Log Data using the Welly Python Library
The welly library was developed by Agile Geoscience to help with loading, processing and analysing well log data from a single well or multiple wells. The library allows exploration of the meta data found within the headers of las files and also contains a ... | github_jupyter |
```
import pandas as pd
from bs4 import BeautifulSoup
df = pd.read_csv('train.csv', sep=';')
texts = df['text']
from pymystem3 import Mystem
morph = Mystem()
import re
import nltk
nltk.download('punkt')
def text_to_sent(t):
text = BeautifulSoup(t).text.lower()
tokenizer = nltk.data.load('tokenizers/punkt/russi... | github_jupyter |
```
# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writi... | github_jupyter |
# Exemplos
---
* Vamos realizar alguns exercรญcios legais com dicionรกrios.
**<font color="red"> Questรฃo 1 </font>**
---
Digamos que vocรช estรก construindo um programa para identificar nรญveis de $CO_{2}$ (gรกs carbรดnico) em determinados locais para evitar potenciais acidentes. Em cada um desses locais a sua empresa tem ... | github_jupyter |
# Identifying the diffusion equation from a random walk
Samuel Rudy, 2016
Here we take various lengths of a random walk where $x_{j+1} \sim \mathcal{N}(x_j, dt)$ and see if we can identify the diffusion equation. As expected, it works better for longer series.
```
%pylab inline
pylab.rcParams['figure.figsize'] = (1... | github_jupyter |
<div class="alert alert-block alert-info" style="margin-top: 20px">
<a href="https://cocl.us/topNotebooksPython101Coursera">
<img src="https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Ad/TopAd.png" width="750" align="center">
</a>
</div>
<a href="https://cognit... | github_jupyter |
## Topological Data Analysis - Part 5 - Persistent Homology
This is Part 5 in a series on topological data analysis.
See <a href="TDApart1.html">Part 1</a> | <a href="TDApart2.html">Part 2</a> | <a href="TDApart3.html">Part 3</a> | <a href="TDApart4.html">Part 4</a>
<a href="https://github.com/outlace/OpenTDA/Persist... | github_jupyter |
```
import numpy as np
import json
import pandas as pd
import matplotlib.pyplot as plt
import linear_regression as clf
from learning_rate import *
import model
from data_process import load_data
import seaborn as sns
import time
%matplotlib inline
%load_ext autoreload
%autoreload 1
```
# Task 1
```
with open("data/r... | github_jupyter |
<a href="https://colab.research.google.com/github/john-s-butler-dit/Basic-Introduction-to-Python/blob/master/W1T3%20The%20Psychometric%20Function.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# The Psychometric Function Week 1, Tutorial 3
In this... | github_jupyter |
Parallel Map on Files
------------------------
For each of a set of filenames, we parse JSON data contents, load that data into a Pandas DataFrame, and then output the result to another file with a nicer format, HDF5.
We find that parsing JSON is slow and so we parallelize the process using the [concurrent.futures](h... | github_jupyter |
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