text stringlengths 2.5k 6.39M | kind stringclasses 3
values |
|---|---|
```
from collections import Counter, defaultdict
class Solution:
def maxScoreWords(self, words, letters, score) -> int:
def get_words(num):
w_list = []
cnt = 0
while num:
if num & 1:
w_list.append(words[cnt])
num >>= 1
... | github_jupyter |
# Parameterize SageMaker Pipelines
Customers can use SageMaker Pipelines to build scalable machine learning pipelines that preprocess data and train machine learning models. With SageMaker Pipelines, customers have a toolkit for every part of the machine learning lifecycle that provides deep customizations and tuning ... | github_jupyter |
##### Copyright 2020 The Cirq Developers
```
#@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 agre... | github_jupyter |
## Data Extracting
### Get crime data from Analyze Boston website
reference like: https://data.boston.gov/dataset/crime-incident-reports-august-2015-to-date-source-new-system/resource/12cb3883-56f5-47de-afa5-3b1cf61b257b
- use a request to query the data from the api
- store data in a dataframe using pandas framework... | github_jupyter |
<h1>Testing the E2E simulations</h1>
## -- JWST aperture --
This script introduces the end-to-end (E2E) simulations that are used in **`calibration.py`**, for the influence calibration of each individual segment. The testing of the script itself is done in this next notebook.
```
import os
import time
import numpy a... | github_jupyter |
# Recurrent Neural Networks
Classical neural networks, including convolutional ones, suffer from two severe limitations:
+ They only accept a fixed-sized vector as input and produce a fixed-sized vector as output.
+ They do not consider the sequential nature of some data (language, video frames, time series, etc.)
R... | github_jupyter |
This examples shows how a classifier is optimized by cross-validation, which is done using the [sklearn.model_selection.GridSearchCV](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV) object on a development set that comprises only half of t... | github_jupyter |
# Documentation of Economic Analysis behind Simulation Engine - Part 3
In this notebook, we consider a dynamical system approach to analyze economic network response to demand shocks. Initially, an economy in a steady state is perturbed by means of an impulse shock. In a static view, where one assumes the output of ec... | github_jupyter |
```
import numpy as np
import random
from matplotlib import pyplot as plt
plt.rcParams['figure.figsize'] = (10.0, 8.0)
from sklearn.datasets import make_biclusters
from sklearn.datasets import samples_generator as sg
# from sklearn.cluster.bicluster import SpectralCoclustering
from sklearn.metrics import consensus_sco... | github_jupyter |
**Chapter 10 – Introduction to Artificial Neural Networks**
_This notebook contains all the sample code and solutions to the exercises in chapter 10._
# 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 func... | github_jupyter |
# TensorFlow Tutorial
Welcome to this week's programming assignment. Until now, you've always used numpy to build neural networks. Now we will step you through a deep learning framework that will allow you to build neural networks more easily. Machine learning frameworks like TensorFlow, PaddlePaddle, Torch, Caffe, Ke... | github_jupyter |
```
# trees.r
# MWL, Lecture 2
# Author(s): [Phil Snyder]
#install.packages("mlbench", repos="http://cran.rstudio.com/") # we can download new libraries right from the R terminal!
library("mlbench")
#help(package="mlbench")
data(BreastCancer)
#help(topic="BreastCancer", package="mlbench")
BreastCancer$Id <- NULL # Just... | github_jupyter |
```
'''
Step 1: from output of evaluate qualification.py: worker_id and int percentage value
Step 2: open worker csv file (downloaded from turk) and
stat_dict = {worker_id: eval_score}
for each cell in csv:
if cellworker_id in stat_dict:
update the qual column with stat_dict[cell_worker_id]
... | github_jupyter |
**Downlaod and extract data**
```
! wget -O A.zip http://epileptologie-bonn.de/cms/upload/workgroup/lehnertz/Z.zip
! wget -O B.zip http://epileptologie-bonn.de/cms/upload/workgroup/lehnertz/O.zip
! wget -O C.zip http://epileptologie-bonn.de/cms/upload/workgroup/lehnertz/N.zip
! wget -O D.zip http://epileptologie-bonn.... | github_jupyter |
# Estimating the Cost of Equity from Historical Price Data
We want to estimate the cost of equity for a company. We have historical data on its stock prices, as well as prices of a market portfolio. We will estimate the CAPM $\beta$, and then calculate the CAPM to determine the cost of equity.
:As a reminder, the CAP... | github_jupyter |
### Python API
HyperTS是DataCanvas Automatic Toolkit(DAT)工具链中，依托[Hypernets](https://github.com/DataCanvasIO/Hypernets)研发的关于时间序列的全Pipeline的自动化工具包。它遵循了make_expriment的使用习惯(类似于[HyperGBM](https://github.com/DataCanvasIO/HyperGBM)的API，一个针对于结构化表格数据的AutoML工具)，也符合```scikit-learn```中model API的使用规范。我们可以创造一个```make_expriment```，``... | github_jupyter |
```
%pylab inline
from ipyparallel import Client, error
cluster=Client(profile="mpi")
view=cluster[:]
view.block=True
try:
from openmdao.utils.notebook_utils import notebook_mode
except ImportError:
!python -m pip install openmdao[notebooks]
```
# Conversion Guide for the Auto-IVC (IndepVarComp) Feature
As of... | github_jupyter |
```
# reload packages
%load_ext autoreload
%autoreload 2
```
### Choose GPU
```
%env CUDA_DEVICE_ORDER=PCI_BUS_ID
%env CUDA_VISIBLE_DEVICES=3
import tensorflow as tf
gpu_devices = tf.config.experimental.list_physical_devices('GPU')
if len(gpu_devices)>0:
tf.config.experimental.set_memory_growth(gpu_devices[0], Tr... | github_jupyter |
Audit Grouping
===
- Load (calibrated) ORES scores
- Load revert probability scores
- Group in some way (caliper width?)
- Investigate groupings
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import os
from tqdm import tqdm
import bz2
import sqlite3
import difflib
imp... | github_jupyter |
```
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import random, numpy as np
import pandas as pd
import matplotlib.pyplot as plt
torch.manual_seed(1)
```
## Loading the datasets, i.e loading frames for few actions
```
#loading and pr... | github_jupyter |
# [LEGALST-123] Lab 07: Intro to Folium
```
#from datascience import *
%matplotlib inline
import matplotlib.pyplot as plt
from folium.plugins import HeatMap
import numpy as np
import folium
import json
import os
!pip install folium --upgrade
from folium.plugins import HeatMap
```
## Data
This lab will serve as an in... | github_jupyter |
## Send More Money cryptarithmetic puzzle
While not often spoken about as a classic data science technique,
constraint programming can be a very useful tool in numerous scenarios.
We'll look at solving a problem using brute force and then how
constraint programming provides a very declarative style
which saves us havi... | github_jupyter |
```
# Installs
!pip install --upgrade -q pip jax jaxlib
!pip install --upgrade -q git+https://github.com/google/flax.git
!pip install --upgrade -q git+https://github.com/rolandgvc/flaxvision.git
# General imports
import jax
import jax.numpy as jnp
import numpy as np
from flax import linen as nn
from flax import op... | github_jupyter |
# Random walk baseline
```
import numpy as np
import pandas as pd
from scipy.fftpack import dct, idct
import matplotlib
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.gridspec as gridspec
from music21 import converter
matplotlib.style.use('styles.mplstyle'... | github_jupyter |
```
import os
import sys
import base64
from io import BytesIO
import numpy as np
from PIL import Image
sys.path.append("..")
from dash_reusable_components import *
# Displays images smaller
def display(im, new_width=400):
ratio = new_width / im.size[0]
new_height = round(im.size[1] * ratio)
return im.res... | github_jupyter |
# Point Source Deconvolution
Deconvolution of a small, simulated point-source image demonstrating the simplest possible example. This is an idealized version of deconvolving subresolution bead images.
**NOTE**: This is definitely a CPU-friendly example (it is not computationally intensive at all).
```
%matplotlib i... | github_jupyter |
## Aula 01 - Entendendo Série Temporal
### Parte 1 - Coleta de Dados e Primeiras Análises
- Fonte dos dados: [Governo do Estado de São Paulo](https://www.seade.gov.br/coronavirus/)
```
src = "../../data/modulo_03/dados_covid_sp.zip"
import pandas as pd
dados = pd.read_csv(src, sep=";")
dados.head()
dados["datahora"]... | github_jupyter |
```
import os
import time
import math
import bisect
import numpy as np
from numpy import array
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from scipy.stats import t, ttest_ind
from collections import Counter
import warnings
from datetime import date
import matplotlib.py... | github_jupyter |
```
import os
import sys
import geopandas as gpd
import pandas as pd
import numpy as np
import scipy
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cf
from IPython.display import Markdown as md
from sklearn.preprocessing import PolynomialFeatures
from shapely import geometry
... | github_jupyter |
# Read in All Saildrone cruises downloaded from https://data.saildrone.com/data/sets
- 2017 onwards, note that earlier data is going to lack insruments and be poorer data quality in general
- For this code I want to develop a routine that reads in all the different datasets and creates a standardized set
- It may work ... | github_jupyter |
# Remark<div class='tocSkip'/>
The code in this notebook differs slightly from the printed book. For example we frequently use pretty print (`pp.pprint`) instead of `print` and `tqdm`'s `progress_apply` instead of Pandas' `apply`.
Moreover, several layout and formatting commands, like `figsize` to control figure siz... | github_jupyter |
# Analysis of French museums' collections (Joconde database)
#### <br> *Download the open data CSV file [here](https://www.data.gouv.fr/fr/datasets/5b435ff2c751df675059dde9/) named joconde-MUSEES-valid.csv*
#### <br> Load the table from the CSV file
##### *Initial fiels are named REF|INV|DOMN|DENO|TITR|AUTR|PERI|EPO... | github_jupyter |
```
import json
import pandas as pd
import numpy as np
with open("/home/ayush/Desktop/img_json/single_keypoints.json") as datafile:
data = json.load(datafile)
df = pd.DataFrame(data)
df.head(5)
df.info()
import json
import pandas as pd
from pandas.io.json import json_normalize
with open('/home/ayush/Deskt... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
from typing import Callable, Optional, Tuple
import numdifftools as nd
import sympy as sp
from iminuit import Minuit
from iminuit.cost import LeastSquares
import tabulate
def fodd(f, x, p):
return 0.5 * (f(x, p) - f(x, -p))
def central(f, x, p, h):
hinv =... | github_jupyter |
<a href="https://colab.research.google.com/github/papagorgio23/Python101/blob/master/Python_101.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
# Begining Libraries
import pandas as pd #Basic sorage Lib
import numpy as np #numpy additional Dat... | github_jupyter |
# Anomalia bouguer para o Havaí
## Importando bibliotecas
```
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import verde as vd
import pyproj
import boule as bl
import harmonica as hm
notebook_name = '6. Hawaii_bouguer_anomaly.ipynb'
```
### Plot style
```
plt.style.use('ggplot')
```
... | github_jupyter |
# Pandas统计分析入门（2）
- 转载注明转自：https://github.com/liupengyuan/
- ## 二维数据统计分析(DataFrame基础)
---
```
%matplotlib inline
from pandas import Series, DataFrame
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
```
## 二、二维数据统计分析(DataFrame基础)
- 数据的描述、分析、可视化展示、概括性度量、输入与输出
df：多维条形图，多维折线... | github_jupyter |
# Prepare Test Data
```
import pandas as pd
import numpy as np
pd.options.display.max_colwidth = 100000000
test_data = pd.read_csv("C:\\Users\\ricardo\\Github\\Kaggle\\1910_TMU_EnglishReviewClassification\\Data\\test_data.csv")
print(len(test_data))
print(test_data.columns)
# neg = "0", pos = "1"
import math
for i i... | github_jupyter |
This material is copied (possibily with some modifications) from the [Python for Text-Analysis course](https://github.com/cltl/python-for-text-analysis/tree/master/Chapters).
# Chapter 7 - Lists
*This notebook uses code snippets and explanations from [this course](https://github.com/kadarakos/python-course/blob/maste... | github_jupyter |
## CMA Diagram
Clemmow-Mullaly-Allis (CMA) Diagram
**Warning**: This notebook would store data (png images) under your jupyter working directory. To be accurate, that is `/the-path-to-your-jupyter-working-directroy/sinupy_data/dispersion/*.png`. Of course you can modify it (`data_path`) in the following block.
```
f... | github_jupyter |
Implementation of Infinite Mixture Models using Dirichlet Process taken from http://blog.echen.me/2012/03/20/infinite-mixture-models-with-nonparametric-bayes-and-the-dirichlet-process/
```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
import seaborn as sns
sns.set(c... | github_jupyter |
# Actividad de interpolación
## Integrantes
```
integrantes = {}
```
## Descripción
### Problema
La resistencia del concreto en una obra está dada en la siguiente tabla
de valores.
| Tiempo (días) | Resistencia (GPa) |
|:-------------:|:-----------------:|
| 0 | 1 |
| 2 | ... | github_jupyter |
## 1. Google Play Store apps and reviews
<p>Mobile apps are everywhere. They are easy to create and can be lucrative. Because of these two factors, more and more apps are being developed. In this notebook, we will do a comprehensive analysis of the Android app market by comparing over ten thousand apps in Google Play a... | github_jupyter |
<h1>Module Description</h1>
---
The current ipynb-module contains implementation of data preparing, especially it represents functions, which extracts faces from photos. For this purpose I've used the DNN module (specifically the network based on Single Shot MultiBox Detector, designed by [Aleksandr Rybnikov](https:/... | github_jupyter |
```
%autosave 0
import pandas as pd
import numpy as np
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import sys
sys.path.append('..')
##Custom Lib
import lib
from lib.data_clean import DataClean
from lib.classifier_trainer import ClassifierJob, MainAiJob
from lib.plot import roc
##
#... | github_jupyter |
<a href="https://colab.research.google.com/github/YIKUAN8/Transformers-VQA/blob/master/openI_VQA.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
**In this notebook, we will classify 15 thoracic findings from Chest X-ray images and associated reports... | github_jupyter |
# Detecting COVID-19 with Chest X Ray using PyTorch
Image classification of Chest X Rays in one of three classes: Normal, Viral Pneumonia, COVID-19
Dataset from [COVID-19 Radiography Dataset](https://www.kaggle.com/tawsifurrahman/covid19-radiography-database) on Kaggle
# Importing Libraries
```
from google.colab im... | github_jupyter |
# Building Rollup hierarchies in python with Treelib and atoti
This notebook is illustrating how to create a product catalog inside a BI application using Treelib and atoti. Full story is available on this link:
https://medium.com/atoti/building-rollup-hierarchies-in-python-with-treelib-and-atoti-ffc61fbac69c?source=... | github_jupyter |
<a href="https://colab.research.google.com/github/https-deeplearning-ai/tensorflow-1-public/blob/adding_C4/C4/W4/ungraded_labs/C4_W4_Lab_1_LSTM.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
#@title Licensed under the Apache License, Version 2.... | github_jupyter |
# FEATURE EXTRACTION
```
import pandas as pd
from textblob import TextBlob
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
d = pd.read_csv("Processed_tweets.csv")
d = d.drop(["Unnamed: 0"],axis=1)
d.head(10)
d.drop_duplicates(inplace=True)
d.isna... | github_jupyter |
<img src="images/logo.jpg" style="display: block; margin-left: auto; margin-right: auto;" alt="לוגו של מיזם לימוד הפייתון. נחש מצויר בצבעי צהוב וכחול, הנע בין האותיות של שם הקורס: לומדים פייתון. הסלוגן המופיע מעל לשם הקורס הוא מיזם חינמי ללימוד תכנות בעברית.">
# <span style="text-align: right; direction: rtl; float: r... | github_jupyter |
# Goals
### Learn how to use full potential of monk in it's expert mode
# Table of Contents
## [0. Install](#0)
## [1. Load data, setup model, select params, and Train](#1)
## [2. Run validation on trained classifier](#2)
## [3. Run inferencing on trained classifier](#3)
<a id='0'></a>
# Install Monk
- ... | github_jupyter |
___
___
# Choropleth Maps
## Offline Plotly Usage
Get imports and set everything up to be working offline.
```
import plotly.plotly as py
import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
```
Now set up everything so that the figures show up in the noteb... | github_jupyter |
# Managing ML workflows with AWS Step Functions and the Data Science SDK
<img align="left" width="130" src="https://raw.githubusercontent.com/PacktPublishing/Amazon-SageMaker-Cookbook/master/Extra/cover-small-padded.png"/>
This notebook contains the code to help readers work through one of the recipes of the book [Ma... | github_jupyter |
##### Copyright 2020 The TensorFlow IO 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 ... | github_jupyter |
##Explore FrozenLakeEnv
```
import numpy as np
import copy
import check_test
from frozenlake import FrozenLakeEnv
from plot_utils import plot_values
env=FrozenLakeEnv()
print(env.observation_space)
print(env.action_space)
print(env.nS)
print(env.nA)
env.P[1][3]
```
##Iterative Policy
```
def policy_evaluation(env,p... | github_jupyter |
##### Copyright 2020 Google
```
#@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 agreed to in writ... | github_jupyter |
```
library(hash)
library(xts)
library(lubridate)
library(forecast)
library(fpp)
# Constants used throughout the code
INPUT_FILE <- "../../../cocUptoDec2016.csv"
DATA_FOLDER <- "../data/topNComplaints"
```
# Base Vignette
Purpose:
- To provide a quick start code snippet to get the data, loaded into a useable format fo... | github_jupyter |
```
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
tf.logging.set_verbosity(tf.logging.INFO) #This way we can see the training information
import os
from google.colab import drive
drive.mount('/content/drive')
%matplotlib inline
filedir = './drive/My Drive/Final/CNN_data'
filelist = os.lis... | github_jupyter |
```
%config IPCompleter.greedy=True
```
# Neuron
Let's start with a simple neuron. From the biological point of view, the simplified view on neuron is following.
The dendrites are inputs of the neuron. Outputs of other neurons are ... | github_jupyter |
# Spiral-shaped distribution
```
Copyright (C) 2021
Code by Leopoldo Sarra and Florian Marquardt
Max Planck Institute for the Science of Light, Erlangen, Germany
http://www.mpl.mpg.de
This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://... | github_jupyter |
# Captcha Solver
```
import cv2
import keras
import numpy as np
from matplotlib import pyplot as plt
%%capture
!unzip generated_captcha_images.zip
```
## Data Processing
### Extracting Single letters from Captcha.
```
import os
import os.path, glob, imutils
captcha_image_folder = "generated_captcha_images"
output_... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import glob
import nibabel as nib
import os
import time
import pandas as pd
import numpy as np
from mricode.utils import log_textfile
from mricode.utils import return_csv
#from mricode.utils import return_iter
path_output = './'
path_tfrecords = '/data2/res64/down/'
path_csv = ... | github_jupyter |
# "Enabling Easy Zipapp Installs on Windows"
> "How to prepare a Windows system for a good PYZ experience."
- author: jhermann
- toc: false
- branch: master
- badges: true
- comments: true
- published: true
- categories: [python, deployment]
- image: images/copied_from_nb/img/python/python+windows.png
;
# 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 |
# Wafer map pattern classification using MultiNN
- Directory
/data/WMPC_CNN_0_0_softmax.pickle
...
/data/WMPC_MFE_0_0_softmax.pickle
...
```
import pickle
import os
import sys
import numpy as np
from tensorflow.keras.layers import Input, Dense, MaxPooling2D, Concatenate
from tensorflow.keras.applications.vgg16 impo... | github_jupyter |
```
import numpy as np
def sigmoid(x):
return 1.0 / (1.0 + np.exp(-x))
# return x * (x > 0)
def sigmoid_derivative(output):
return output * (1 - output)
# return 1.0 * (output > 0)
# 整数与二进制转化
int2binary = {}
binary_dim = 9
largest_number = pow(2, binary_dim)
def int2bin(int_num):
b_temp = bin(int_nu... | github_jupyter |
# Functions and Methods Homework
Complete the following questions:
____
**Write a function that computes the volume of a sphere given its radius.**
<p>The volume of a sphere is given as $$\frac{4}{3} πr^3$$</p>
```
def vol(rad):
return 4/3 * 22/7 * rad**3
# Check
vol(2)
```
___
**Write a function that checks wh... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content-dl/blob/main/projects/NaturalLanguageProcessing/machine_translation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> <a href="https://kaggle.com/kernels/welcome?... | github_jupyter |
```
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from trackml.dataset import load_event
from trackml.randomize import shuffle_hits
from trackml.score import score_event
import os
import numpy as np
import pandas as pd
import glob
im... | github_jupyter |
# Defect Detection Model
Here, we build a model to detect the presence/absence of defect (any kind) in a submersible pump impeller using Transfer Learning (with VGG16 base model)
**Dataset**: [Submersible Pump Impeller Defect Dataset](https://www.kaggle.com/ravirajsinh45/real-life-industrial-dataset-of-casting-produc... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Automated ML on Azure Databricks
In this example we use the scikit-learn's <a href="http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset" target="_blank">digit dataset</a> to sh... | github_jupyter |
# Random Agent in Malmo
This guide shows how to setup a single-player Malmo mission. This example may serve as a basis to use Malmo in your RL experiments.
## Malmo launcher
In earlier versions of ```malmoenv``` each Minecraft instance had to be started manually from command line. The launcher handles these processes ... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import pandas as pd
from sklearn.linear_model import LogisticRegression
import seaborn as sns
from sklearn.pipeline import Pipeline
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from skl... | github_jupyter |
# Building a Model in Helipad
In this walkthrough, we’ll build a very simple model of two goods where decentralized trading results in agents converging on an equilibrium price. Each period, agents pair off randomly and see if they can become better off by trading. If so, they trade. If not, they do nothing. In just a... | github_jupyter |
# Data Science Projects with SQL Server Machine Learning Services
## 06 Customer Acceptance and Model Retraining
<p style="border-bottom: 1px solid lightgrey;"></p>
<dl>
<dt>Course Outline</dt>
<dt>1 Overview and Course Setup</dt>
<dt>2 Business Understandin... | github_jupyter |
<a href="https://colab.research.google.com/github/Data-Science-and-Data-Analytics-Courses/UCSanDiegoX---Machine-Learning-Fundamentals-03-Jan-2019-audit/blob/master/Week%2006%20Linear%20Classification/perceptron_at_work/perceptron_at_work.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-b... | 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 pr... | github_jupyter |
# Friendship Paradox
#### Author: [Erika Fille Legara](https://erikalegara.site)
[](https://github.com/eflegara/Network-Science-Lectures/blob/master/LICENSE.md)
---
<table align="left" border=0>
<!-- <table class="tfo-notebook-buttons" align="left"... | github_jupyter |
### The purpose of this notebook is to load the referential data of `chairs-in-context` and package them in a pandas dataframe along with other simple datastructures like dictionaries that map integers to tokens etc. Having access to these pre-processed data, is the first step before you start training neural listener ... | github_jupyter |
# VMEC Python Interface
This notebook introduces the user to the VMEC Python interface. This is accomplished by using the CTYPES Python library interface to directly access a statically linked version of libstell as compiled with the VMEC distribution.
First we Test if we can load the library.
```
from libstell impo... | github_jupyter |
[](https://github.com/awslabs/aws-data-wrangler)
# 11 - CSV Datasets
Wrangler has 3 different write modes to store CSV Datasets on Amazon S3.
- **append** (Default)
Only adds new files without any delete.
- **overwrite**
Deletes everything in t... | github_jupyter |
# 3.1.3. Sensor-to-sample distance
This code generates all the results presented in the subsubsection 3.1.3 Sensor-to-sample distance.
### License
This code is licensed under under the [BSD 3-clause](http://choosealicense.com/licenses/bsd-3-clause/) license. See the file `LICENSE.md`
### Import the required depende... | github_jupyter |
# Sankey Diagram
```
#Simple Sankey Diagram
fig = go.Figure(
go.Sankey(
node = {
"label": ["India", "USA", "China", "Pakistan", "Bangladesh", "Mexico"],
},
link = {
... | github_jupyter |
# Binary Classification
This is a basic example in which we learn to ground unary predicate $A$ that is defined in the space of $[0,1]^2$.
We define the predicate $A$ to apply to points that are close to the middle point $c=(.5,.5)$.In order to get training data, we randomly sample data from the domain. We split the ... | github_jupyter |
## Datasets
```
# Visualization
%pylab inline
from IPython.display import display, Math, Latex
import matplotlib.pyplot as plt
# handling data
import csv
import json
import pandas as pd
# Math
from random import random
import scipy.stats as ss
import numpy as np
import itertools
from collections import Counter
```
... | github_jupyter |
# cuML Preprocessing
Users of cuML are certainly familiar with its ability to run machine learning models on GPUs and the significant training and inference speedup that can entail, but the models themselves are only part of the story. In this notebook, we will demonstrate how cuML allows you to develop an entire machi... | github_jupyter |
```
#Introduction
#.....
```
Check to see if jupyter lab uses the correct python interpreter with '!which python'.
It should be something like '/opt/anaconda3/envs/[environment name]/bin/python' (on Mac).
If not, try this: https://github.com/jupyter/notebook/issues/3146#issuecomment-352718675
```
import sys
sys.exec... | github_jupyter |
# iAR package Demo - BIAR Model
```
import iar
import numpy as np
import matplotlib.pyplot as plt
print("iAR version:")
print(iar.__version__)
```
# Simulates from a BIAR Model
```
from iar import BIAR_sample,gentime
np.random.seed(6713)
n=300
phi1=0.9
phi2=0.4
sT=gentime(n=n,lambda1=15,lambda2=2)
y,sT,Sigma =BIAR_s... | github_jupyter |
# Using the NDBC Buoy Data Scraper
The Buoy class is used to get realtime and historical data from [NDBC Buoys](https://www.ndbc.noaa.gov/)
[Realtime Buoy Data](#Realtime-data-from-the-Neah-Bay-buoy)
[Historical Buoy Data](#Historical-data)
```
from buoyscraper import Buoy
```
## Realtime data from the Neah Bay b... | github_jupyter |
Before we begin, let's execute the cell below to display information about the CUDA driver and GPUs running on the server by running the `nvidia-smi` command. To do this, execute the cell block below by giving it focus (clicking on it with your mouse), and hitting Ctrl-Enter, or pressing the play button in the toolbar ... | github_jupyter |
```
from openpiv import tools, pyprocess, scaling, filters, \
validation, preprocess
import numpy as np
from skimage import io
import matplotlib.pyplot as plt
%matplotlib inline
file_a = '../test4/Camera1-0101.tif'
file_b = '../test4/Camera1-0102.tif'
im_a = tools.imread( file_a )
im_b = tools.im... | github_jupyter |
# Text Preprocessing
For any NLP tasks in Deep Learning the first step would be preprocessing the text data into numbers!
In the recent years almost all the DL packages have started to provide their own APIs to do the text preprocessing, however each one has its own subtle differences, which if not understood correct... | github_jupyter |
```
import pickle
import numpy as np
class SeqDataset(object):
def __init__(self, ids, features, labels, groups, wordRanges, truePos):
'''
ids are ids of candidate sequences
each row of features is 13 features corresponding to the following:
feature_0: pred_end - pred_start so len... | github_jupyter |
```
# importing libraries
import argparse
import os
import pickle
import logging
import boto3
import faiss
import pandas as pd
from tqdm import tqdm
from random import sample
########################################
# 从s3同步数据
########################################
def sync_s3(file_name_list, s3_folder, local_folder... | github_jupyter |
# Utilizing existing FAQs for Question Answering
[](https://colab.research.google.com/github/deepset-ai/haystack/blob/master/tutorials/Tutorial4_FAQ_style_QA.ipynb)
While *extractive Question Answering* works on pure texts and is therefore more... | github_jupyter |
```
from google.colab import drive
import os
import shutil
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow import keras
from keras import layers
from keras import models
from keras import optimizers
from keras.layers import Input, Dense, Activation, Flatten... | github_jupyter |
# Laboratory 03 - Introduction to Digital Data Acquisition, FFT, and Spectrum Analysis 2
## MAE 3120, Spring 2020
## Grading Rubric
Procedures, Results, Plots, Tables - 60%
Discussion Questions - 25%
Neatness - 15%
## Introduction and Background
Prior to the 1980s, the oscilloscope and strip-chart recorder repr... | github_jupyter |
# Use Amazon Sagemaker Distributed Model Parallel to Launch a BERT Training Job with Model Parallelization
Sagemaker distributed model parallel (SMP) is a model parallelism library for training large deep learning models that were previously difficult to train due to GPU memory limitations. SMP automatically and effic... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Model Development with Custom Weights
This example shows how to retrain a model with custom weights and fine-tune the model with quantization, then deploy the model running on FPGA. Only Windows is supported. We use TensorFlo... | github_jupyter |
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