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# SAS ODA and Python Integration to Analyze COVID-19 Data
The purpose of this notebook is to illustrate how Python code can be integrated with calls to SAS ODA in order to solve a particular problem of interest. In the course of this document, we will load the NYT COVID-19 data set. As the NYT data set contains raw cu... | github_jupyter |
# ML for Trading: How to run an ML algorithm on Quantopian
The code in this notebook is written for the Quantopian Research Platform and uses the 'Algorithms' rather than the 'Research' option we used before.
To run it, you need to have a free Quantopian account, create a new algorithm and copy the content to the onl... | github_jupyter |
# STUMPY Basics
[](https://mybinder.org/v2/gh/TDAmeritrade/stumpy/main?filepath=notebooks/Tutorial_STUMPY_Basics.ipynb)
## Analyzing Motifs and Anomalies with STUMP
This tutorial utilizes the main takeaways from the research papers: [Matrix Profile I](http://www.cs.ucr.e... | github_jupyter |
# Autoencoders
[](https://colab.research.google.com/github/m12sl/dl-hse-2021/blob/master/12-xae/seminar.ipynb)
Пора заняться автоэнкодерами.
<img src="https://github.com/m12sl/dl-hse-2021/raw/master/12-xae/img/encoder.png" crossorigin="anonymo... | github_jupyter |
# Generating Features from GeoTiff Files
From GeoTiff Files available for India over a period of more than 20 years, we want to generate features from those files for the problem of prediction of district wise crop yield in India.
Due to gdal package, had to make a separate environment using conda. So install packages... | github_jupyter |
```
{-# LANGUAGE InstanceSigs #-}
import Control.Applicative (Alternative(..))
import Data.Char
newtype Parser s r = Parser { unParser :: [s] -> ParseResult s r }
type ParseResult s r = [([s], r)]
symbol :: Eq s => s -> Parser s s
symbol sym = Parser p
where p (s:ss) | s == sym = [(ss, sym)]
p _ ... | github_jupyter |
#Traditional Value Factor Algorithm
By Gil Wassermann
Strategy taken from "130/30: The New Long-Only" by Andrew Lo and Pankaj Patel
Part of the Quantopian Lecture Series:
* www.quantopian.com/lectures
* github.com/quantopian/research_public
Notebook released under the Creative Commons Attribution 4.0 License. Pleas... | github_jupyter |
# 1. File I/O Settings
```
hindcast_data_file = 'test_data/NMME_data_BD.csv' #data used for cross-validated hindcast skill analysis, and to train forecast model
hindcast_has_years = True
hindcast_has_header = False
hindcast_has_obs = True #NOTE: This is mandatory
hindcast_export_file = 'bd.csv' #'None' or the name of... | github_jupyter |
```
import os.path
import re
import sys
import numpy as np
import json
import time
from six.moves import urllib
import matplotlib as mpl
from pprint import pprint
import pandas as pd
%matplotlib inline
%load_ext autoreload
%autoreload 2
```
## Preprocessing - BM Caption dataset
- Remove duplication by image url - D... | github_jupyter |
# Marching Wagon of useful Modules
#### Changing you life one package at a time....
```
```
### Retrying your functions
```
# retrying
# https://pypi.python.org/pypi/retrying
import time
import random
from retrying import retry
@retry(stop_max_delay=1000)
def do_something_unreliable():
if random.randint(0, 1... | github_jupyter |
```
!conda upgrade scikit-learn -y
from azureml import services
from azureml import Workspace
from azure.servicebus import ServiceBusService
import warnings; warnings.filterwarnings('ignore')
import datetime
from dateutil import parser
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklea... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/GetStarted/04_band_math.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_blank" href... | github_jupyter |
```
import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import StandardScaler
class MyTransformer(BaseEstimator, TransformerMixin):
def __init__(self):
self._mean_X = None
self._std_X = None
def fit(self, X: np.array, y... | github_jupyter |
# Introduction
One of the most basic questions we might ask of a model is: What features have the biggest impact on predictions?
This concept is called **feature importance**.
There are multiple ways to measure feature importance. Some approaches answer subtly different versions of the question above. Other appro... | github_jupyter |
```
""" Load some libs """
""" python 2 lib using networkx """
import matplotlib.pyplot as plt
import networkx as nx
import random
import math
import pandas as pd
import statsmodels.api as sm
import glob
import os
import numpy as np
from PIL import Image
from helpers import *
import pickle
import time
#random.seed(100)... | github_jupyter |
# Compare Models
This notebook compares various GFW models based on the `measure_speed` and `measure_course` with each other
and with the models from Dalhousie University. Note that the distance-to-shore cutoff was disabled in the
Dalhousie models, so none of the models compared here are using distance-to-shore as a ... | github_jupyter |
# Elementary greenhouse models
____________
<a id='section1'></a>
## 1. A single layer atmosphere
____________
We will make our first attempt at quantifying the greenhouse effect in the simplest possible greenhouse model: a single layer of atmosphere that is able to absorb and emit longwave radiation.
<img src="../... | github_jupyter |
<a href="https://colab.research.google.com/github/hendradarwin/covid-19-prediction/blob/master/series-dnn_and_rnn/Forecast_2_dnn.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## Pediction New Death Cases Global Covid-19 Cases
## Load Data and Imp... | github_jupyter |
# Optimising Returns in Portfolio Management
### Exploring Numerical Optimisation Techniques to solve Quadratic Problems in Python
##### Zac Keskin - Numerical Optimisation - UCL 2018
## Part 0: Define functions, Import Data
### Pre-import required packages
```
import numpy as np
import pandas as pd
import m... | github_jupyter |
### Test web application locally
This notebook pulls some images and tests them against the local web app running inside the Docker container we made previously.
```
import matplotlib.pyplot as plt
import numpy as np
from testing_utilities import *
import requests
%matplotlib inline
%load_ext autoreload
%autoreload 2
... | github_jupyter |
# Session 3: Data Structuring 2
*Nicklas Johansen*
## Agenda
In this session, we will work with different types of data:
- Boolean Data
- Numeric Operations and Methods
- String Operations
- Categorical Data
- Time Series Data
### Recap
- Loading Packages
- Pandas Series
- Pandas Data Frames
- Series vs DataFram... | github_jupyter |
```
from __future__ import print_function
from parser import *
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout ,LSTM
from keras.optimizers import RMSprop
import numpy as np
import random
text = parse_folder('TheVGLC-master/Super Mario Bros/Processed/')
print('corpus length:', le... | github_jupyter |
## Dependencies
```
from tweet_utility_scripts import *
from transformers import TFDistilBertModel, DistilBertConfig
from tokenizers import BertWordPieceTokenizer
from tensorflow.keras.models import Model
from tensorflow.keras import optimizers, metrics, losses
from tensorflow.keras.callbacks import EarlyStopping, Ten... | github_jupyter |
# Using Interact
The `interact` function (`ipywidgets.interact`) automatically creates user interface (UI) controls for exploring code and data interactively. It is the easiest way to get started using IPython's widgets.
```
from __future__ import print_function
from ipywidgets import interact, interactive, fixed, in... | github_jupyter |
<a href="https://qworld.net" target="_blank" align="left"><img src="../qworld/images/header.jpg" align="left"></a>
$$
\newcommand{\set}[1]{\left\{#1\right\}}
\newcommand{\abs}[1]{\left\lvert#1\right\rvert}
\newcommand{\norm}[1]{\left\lVert#1\right\rVert}
\newcommand{\inner}[2]{\left\langle#1,#2\right\rangle}
\newcomma... | github_jupyter |
<a href="https://githubtocolab.com/giswqs/geemap/blob/master/examples/notebooks/49_colorbar.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open in Colab"/></a>
Uncomment the following line to install [geemap](https://geemap.org) if needed.
```
# !pip install geemap
``... | github_jupyter |
<a href="https://colab.research.google.com/github/Sujangyawali/Fraud_Detection/blob/master/pyspark_for_classification.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
! pip install pyspark
! pip install -q kaggle
! mkdir ~/.kaggle
! cp kaggle.jso... | github_jupyter |
```
from pyspark.sql import SparkSession
from pyspark.ml import Pipeline
from pyspark.sql.functions import mean,col,split, col, regexp_extract, when, lit
from pyspark.ml.feature import StringIndexer
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from p... | github_jupyter |
# Tutorial Part 17: Training a Generative Adversarial Network on MNIST
In this tutorial, we will train a Generative Adversarial Network (GAN) on the MNIST dataset. This is a large collection of 28x28 pixel images of handwritten digits. We will try to train a network to produce new images of handwritten digits.
##... | github_jupyter |
```
#export
from fastai2.basics import *
from nbdev.showdoc import *
#default_exp callback.schedule
```
# Hyperparam schedule
> Callback and helper functions to schedule any hyper-parameter
```
from fastai2.test_utils import *
```
## Annealing
```
#export
class _Annealer:
def __init__(self, f, start, end): sto... | github_jupyter |
# Healthcare insurance fraud identification using PCA anomaly detection
1. [Background](#background)
1. [Setup](#setup)
1. [Data](#data)
1. [Obtain data](#datasetfiles)
1. [Feature Engineering](#feateng)
1. [Missing values](#missing)
1. [Categorical features](#catfeat)
1. [Gender](#gender)
... | github_jupyter |
<small><small><i>
All the IPython Notebooks in **Python Introduction** lecture series by Dr. Milaan Parmar are available @ **[GitHub](https://github.com/milaan9/01_Python_Introduction)**
</i></small></small>
# Python Programming
Python is a powerful multipurpose programming language created by *Guido van Rossum*.
It... | github_jupyter |
A very wide range of physical processes lead to wave motion, where
signals are propagated through a medium in space and time, normally
with little or no permanent movement of the medium itself.
The shape of the signals may undergo changes as they travel through
matter, but usually not so much that the signals cannot be... | github_jupyter |
# Non-linear dependencies amongst the SDGs and climate change by distance correlation
We start with investigating dependencies amongst the SDGs on different levels. The method how we investigate these dependencies should take as few assumptions as possible. So, a Pearson linear correlation coefficient or a rank correl... | github_jupyter |
# 7 - Functions
```
from scipy import *
from matplotlib.pyplot import *
%matplotlib inline
```
## Basics
```
def subtract(x1, x2):
return x1 - x2
r = subtract(5.0, 4.3)
r
```
## Parameters and Arguments
```
z = 3
e = subtract(5,z)
e
z = 3
e = subtract(x2 = z, x1 = 5)
e
```
### Changing Arguments
```
def subt... | github_jupyter |
# Introduction to Random Forests
## Resources
This notebook is designed around the theory from the fast.ai lectures (course18) with added comments and details found in the lectures and online. The entire course can be found here: http://course18.fast.ai/ml.html.
### Links
- Lecture notebook: https://github.com/fasta... | github_jupyter |
# Homework 1: Preprocessing and Text Classification
Student Name: Jun Luo
Student ID: 792597
Python version used: Python2.7
## General info
<b>Due date</b>: 11pm, Sunday March 18th
<b>Submission method</b>: see LMS
<b>Submission materials</b>: completed copy of this iPython notebook
<b>Late submissions</b>: -20... | github_jupyter |
# Overview
- nb015を改良
- nb020のfoldを使う
- top8は均等に振り分ける
```
# gitのhash
import subprocess
cmd = "git rev-parse --short HEAD"
hash = subprocess.check_output(cmd.split()).strip().decode('utf-8')
print(hash)
```
# Const
```
# basic
NB = '021'
DEBUG = False
isPI = False
isShowLog = True
PATH_TRAIN = '../data_ignore/input... | github_jupyter |
```
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
weights = []
for alpha in [.5]:
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf... | github_jupyter |
PPO Using VAE
# VAE classes
https://github.com/AntixK/PyTorch-VAE/blob/master/models/vanilla_vae.py
```
import torch
from torch import nn
from torch.nn import functional as F
import torch.optim as optim
class VAE(nn.Module):
# Use Linear instead of convs
def __init__(self,
in_channels: int,
... | github_jupyter |
# Split Dataframe using Panda's Groupby
For this tutorial, I will asume you have a basic understanding of Python, and know how to load a dataframe using the Panda's library.
I will use the GL_Detail example file from the AICPA's AuditDataAnalytic's GitHub.
```
import pandas as pd
import numpy as np
# Displays number... | github_jupyter |
# Model selection using hyperopt
```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.datasets import make_moons
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
f... | github_jupyter |
## Importing libraries
```
import warnings
warnings.filterwarnings("ignore")
# data manipulation and numeric operations
import pandas as pd
import numpy as np
# save and load serialized objects
import pickle
# track progress of function execution
from tqdm import tqdm
import os
# metrics
from sklearn.metrics impor... | github_jupyter |
```
import json
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import matplotlib
import pandas as pd
import pyprojroot
import seaborn as sns
def convert_seg_error_rate_pct(df):
df.avg_segment_error_rate = df.avg_segment_error_rate * 100
return df
RESULTS_ROOT = pyprojroot.here() / ... | github_jupyter |
# Train VAE for task2...
Then what if reconstruction is lower weighted?
Loss function is weighted as: $loss = 0.01 L_{Reconstruction} + L_{KLD}$
```
# public modules
from dlcliche.notebook import *
from dlcliche.utils import (
sys, random, Path, np, plt, EasyDict,
ensure_folder, deterministic_everything,
)
f... | github_jupyter |
```
# from google.colab import drive
# drive.mount('/content/drive')
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 torchvision
import torchvision.transforms as transforms
from torch.utils.data import Dataset, DataLoader... | github_jupyter |
# Explaining Answer Set Solving
This is a short guide that shows how Answer Set Programming works. We will use [clingo](https://potassco.org/clingo/) in Python for this, and throughout this document we will use the syntax that clingo uses for answer set programming.
<!-- [guide](https://github.com/potassco/guide/rele... | github_jupyter |
```
bonus_root = '/network/group/aopp/predict/TIP016_PAXTON_RPSPEEDY/ML4L/ECMWF_files/raw/BonusClimate/'
#Wetlands
wetlands = ['COPERNICUS', 'CAMA','ORCHIDEE','monthlyWetlandAndSeasonalWater_minusRiceAllCorrected_waterConsistent']
lakes = ['CL_ECMWFAndJRChistory','yearlyCL']
import glob
import pandas as pd
import xarr... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W1D1_ModelTypes/student/W1D1_Tutorial2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Neuromatch Academy: Week 1, Day 1, Tutorial 2
# Model ... | github_jupyter |
## Introduction to matplotlib
`matplotlib` is the Python plotting package to rule them all. Not because it's the best. Or the easiest to use. Or the fastest. Or... wait, why is it the number 1 plotting package? Nobody knows! But it's everywhere, and making basic plots is... fine. It's really fine.
```
import numpy as... | github_jupyter |
```
import pandas as pd
from google.colab import drive
drive.mount('/content/gdrive', force_remount=True)
import pandas as pd
import numpy as np
import pickle
from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score
testData = pd.read_csv('/content/gdrive/MyDrive/ML_Project/Dataset/pre_stand... | github_jupyter |
# Introduction to Deep Learning with PyTorch
In this notebook, you'll get introduced to [PyTorch](http://pytorch.org/), a framework for building and training neural networks. PyTorch in a lot of ways behaves like the arrays you love from Numpy. These Numpy arrays, after all, are just tensors. PyTorch takes these tenso... | github_jupyter |
<a href="https://colab.research.google.com/gist/HerkTG/5f255e18611170ac204fcedb3f9d81e2/algoloader_v1-1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
#User definied parameters
host = "https://covidv3.i.tgcloud.io" #@param {type:"string"}
graph... | github_jupyter |
```
from __future__ import print_function, division
from keras.datasets import mnist
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import UpS... | github_jupyter |
# Capital Allocation Problem
## Author: Snigdhayan Mahanta
In a large corporation the `capital allocation problem` is one of the biggest challenges for the corporate decision-makers. A `corporation` consists of several `business units`. From a high level perspective a corporation can choose to deploy its financial res... | github_jupyter |
```
import sys
import numpy as np
import scipy as sp
import pandas as pd
from scipy import ndimage
import matplotlib.pyplot as plt
from scipy import interpolate
from scipy.interpolate import griddata
from scipy.interpolate import RectBivariateSpline,bisplrep,CloughTocher2DInterpolator,interp2d
N=64
M=64
L=3
vs1=3
vs2=-... | github_jupyter |
# Integration
```
import matplotlib.pyplot as plt
import numpy as np
```
## Contents
1.[Integral Calculus](#Integral_Calculus)
2.[Fundamental Theorem of Calculus](#Fundamental_Theorem_of_Calculus)
3.[Basic Integration](#Basic_Integration)
- [Integrating powers of x](#Integrating_powers_of_x)
- [Integrating other b... | github_jupyter |
```
import numpy as np
from scipy import ndimage
from scipy import spatial
from scipy import io
from scipy import sparse
from scipy.sparse import csgraph
from scipy import linalg
from matplotlib import pyplot as plt
import seaborn as sns
from skimage import data
from skimage import color
from skimage import img_as_floa... | github_jupyter |
```
'''
Import packages and modules from Python Standard Library and Third party libraries.
'''
#Import from python standard library
import os
#Import from third party libraries
import cv2
import glob
import numpy as np
import pandas as pd
from sklearn.utils import shuffle
from skimage.color import gray2rgb, rgb2... | github_jupyter |
# Grove Temperature sensor module
---
## Aim
* This notebook illustrates how to use available APIs for the Grove Temperature sensor module on PYNQ-Z2 PMOD and Arduino interfaces.
## References
* [Grove Temperature sensor](https://www.seeedstudio.com/Grove-Temperature-Sensor.html)
* [Grove I2C ADC](https://www.seeed... | github_jupyter |
```
# Copyright 2022 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 |
# Estimating the effective reproduction number in Belgium with the RKI method
> Using the Robert Koch Institute method with serial interval of 4.
- toc:true
- branch: master
- badges: true
- comments: true
- author: Lode Nachtergaele
- categories: [cast42, covid19, Belgium]
Every day [Bart Mesuere](https://twitter.co... | github_jupyter |
```
import sys, os
sys.version, os.getcwd()
```
# Torch
```
torch.__version__
import pandas as pd
PREFIXES = ['WP','EU','CW','TT','RF']
def clean_source_data(directory):
data = pd.read_table(directory, header=None)
data['prefix'] = data[0].apply(lambda x: str(x).split(']')[0][1:].strip()).str.upper()
data... | github_jupyter |
# Chapter 1 Exercises
```
import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy import stats
az.style.use('arviz-darkgrid')
```
## Question 1
***
We do not know whether the brain really works in a Bayesian way, in an approximate Bayesian fashion, or maybe some evoluti... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Tutorial: Azure Machine Learning Quickstart
In this tutorial, you lear... | github_jupyter |
```
from IPython.core.debugger import set_trace
%run 'activation.ipynb'
import numpy as np
import pickle
%run "mnist.ipynb"
import matplotlib as mpl
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import Grid
```
### Define Model
```
class RBM:
def __init__(self, n_v, n_h, W=None, b=None, c=N... | github_jupyter |
```
# from dask.distributed import Client, LocalCluster
# import logging
# cluster = LocalCluster(
# n_workers=28,
# threads_per_worker=8,
# silence_logs=logging.DEBUG
# )
# client = Client(cluster, heartbeat_interval=10000)
# print(client.dashboard_link)
import afqinsight as afqi
import joblib
import mat... | github_jupyter |
```
import geopandas as gpd
import pandas as pd
import numpy as np
from copy import deepcopy
chirps_file = "../Data/vietnam/fluvial_defended/FD_1in5.csv"
chirps_ori = pd.read_csv(chirps_file)
chirps_ori.dropna(inplace=True)
chirps_ori.columns = ['Lon', 'Lat', 'flood_level']
chirps_data = deepcopy(chirps_ori)
chirps_dat... | github_jupyter |
## Различные графики
```
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
```
### Regular plot
```
n = 512
X = np.linspace(0, np.pi/2, n, endpoint=True)
Y = np.cos(20*X) * np.exp(-X)
plt.figure(figsize=(8,4), dpi=80)
# Plot upper sine wave
plt.plot(X, Y+2, color='green', alpha=1.00)
plt.fill_be... | github_jupyter |
```
import os
from datetime import datetime, timedelta
import ipywidgets as widgets
import plotly.graph_objs as go
import yfinance as yf
import pandas as pd
from IPython.display import display
interval_opts = [
"1m",
"2m",
"5m",
"15m",
"30m",
"60m",
"90m",
"1h",
"1d",
"5d",
"... | github_jupyter |
# Introduction to XArray
> This tutorial introduces XArray, a Python library for working with labeled multidimensional arrays.
- toc: false
- badges: true
- comments: true
- categories: [numpy]
#### DEA uses XArray as its data model. To better understand what it is, let's first do a simple experiment on how we could... | github_jupyter |
## Crop Analysis for English, Arabic, and Paired English+Arabic Memes
```
import logging
import shlex
import subprocess
import sys
import io
import pandas as pd
from collections import namedtuple
from pathlib import Path
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
from matplotl... | github_jupyter |
# Differential Privacy - DP
### What is DP?
Differential Privacy began with ensuring that different *'statistical analysis'* does not violate privacy, which in the early days of DP meant database queries that remained private. Now, any statistical analysis should not violate the privacy of any individual.
We want t... | github_jupyter |
One can create particle trajectories from a `DatasetSeries` object for a specified list of particles identified by their unique indices using the `particle_trajectories` method.
```
%matplotlib inline
import glob
from os.path import join
import yt
from yt.config import ytcfg
path = ytcfg.get("yt", "test_data_dir")
imp... | github_jupyter |
# Data Carpentry Reproducible Research Workshop - Data Exploration
## Learning objectives
Use the Python Pandas library in the Jupyter Notebook to:
* Assess the structure and cleanliness of a dataset, including the size and shape of the data, and the number of variables of each type.
* Describe findings, translate res... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LinearRegression, LogisticRegression, BayesianRidge
from sklearn.model_selection import train_test_split
survey_data = pd.read_csv('data/Questionnaire_July 31, 2019_10.47.csv')
maps_data = ... | github_jupyter |
```
from netCDF4 import Dataset
import netCDF4 as netcdf
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import matplotlib as mpl
#mapping
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from cartopy.io import shapereader
from cartopy.mpl.gridliner import LONGITUDE_... | github_jupyter |
# RNN Evaluation
From our paper on "Explainable Prediction of Acute Myocardial Infarction using Machine Learning and Shapley Values"
```
# Import libraries
from keras import optimizers, losses, activations, models
from keras.callbacks import ModelCheckpoint, EarlyStopping, LearningRateScheduler, ReduceLROnPlateau
fro... | github_jupyter |
```
from keras.datasets import mnist
(trainX, trainY), (testX, testY) = mnist.load_data()
from keras.models import Model
from keras.layers import Input, Reshape, Dense, Flatten, Dropout, LeakyReLU
class Autoencoder:
def __init__(self, img_shape=(28, 28), latent_dim=2, n_layers=2, n_units=128):
# encoder
h =... | github_jupyter |
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-59152712-8"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-59152712-8');
</script>
# BlackHoles@Home Tutorial: Creating `BOINC` native applica... | github_jupyter |
# Working with structured data in Python using Pandas
### What is data preprocessing?
Process of converting raw data into useful format.In order to better understand the data, we need to gather some statistical insights into our data. In this module of the course, we will use some of the libraries available with Pyt... | github_jupyter |
<center><font size="+4">Introduction to Programming and Data Processing 2020/2021</font></center>
<center><font size="+2">Sant'Anna School of Advanced Studies, Pisa, Italy</font></center><br/>
<center><font size="+2">Course responsible</font></center>
<center><font size="+2">Andrea Vandin a.vandin@santannapisa.it</fon... | github_jupyter |
# App4
* An App with 4 functions with different types of workload to validate the performance of the optimization algorithm
* There is 1 parallel and 1 cycle in App4
```
import os
from io import BytesIO
import time
import zipfile
import numpy as np
import boto3
from tqdm import tqdm
from datetime import datetime, t... | github_jupyter |
```
!curl -L https://raw.githubusercontent.com/facebookresearch/habitat-sim/main/examples/colab_utils/colab_install.sh | NIGHTLY=true bash -s
%cd /content/habitat-sim
## [setup]
import os
import random
import sys
import git
import magnum as mn
import numpy as np
import habitat_sim
from habitat_sim.utils import viz_ut... | github_jupyter |
# Neural Machine Translation with Attention: German to English
Here we implement a neural machine translator with attention using standard TensorFlow operations.
```
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
%matplotlib inline
from __future__ import p... | github_jupyter |
# AlexNet in Keras
In this notebook, we leverage an [AlexNet](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks)-like deep, convolutional neural network to classify flowers into the 17 categories of the [Oxford Flowers](http://www.robots.ox.ac.uk/~vgg/data/flowers/17/) d... | github_jupyter |
# T81-558: Applications of Deep Neural Networks
* 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 visit the [class website](https://sites.wust... | github_jupyter |
```
import pyvisa
from pylabnet.utils.logging.logger import LogClient
from pylabnet.network.core.generic_server import GenericServer
from pylabnet.hardware.power_meter.thorlabs_pm320e import Driver
from pylabnet.hardware.polarization.polarization_control import Driver as MPC320 , paddle1, paddle2, paddle3
import time
i... | github_jupyter |
# Face Detection in OpenCV
General Object Detection using Haar feature-based cascade classifiers is an effective object detection method proposed by Paul Viola and Michael Jones in their paper, "Rapid Object Detection using a Boosted Cascade of Simple Features" in 2001. It is a machine learning based approach where a c... | github_jupyter |
# CS5340 Lecture 8: HMMs #
Lecturer: Harold Soh (harold@comp.nus.edu.sg)
Graduate TAs: Abdul Fatir Ansari and Chen Kaiqi (AY19/20)
This notebook is a supplement to Lecture 8 of CS5340: Uncertainty Modeling in AI
The material uses the hmmlearn package and is based on the tutorial provided by the hmmlearn package (h... | github_jupyter |
# Fairness Metrics
This notebook implements the statistical fairness metrics from:
*Towards the Right Kind of Fairness in AI* by Boris Ruf and Marcin Detyniecki (2021)
https://arxiv.org/abs/2102.08453
Example with the `german-risk-scoring.csv` dataset.
Contributeurs : Xavier Lioneton & Francis Wolinski
## Imports
... | github_jupyter |
# Demonstrate the path of high probability and the orthogonal path on the pyloric rhythm for experimental data
```
# Note: this application requires a more recent version of dill.
# Other applications in this repository will require 0.2.7.1
# You might have to switch between versions to run all applications.
!pip inst... | github_jupyter |
```
#export
from fastai.basics import *
from fastai.text.core import *
from fastai.text.data import *
from fastai.text.models.core import *
from fastai.text.models.awdlstm import *
from fastai.callback.rnn import *
from fastai.callback.progress import *
#hide
from nbdev.showdoc import *
#default_exp text.learner
```
#... | github_jupyter |
## Stable Model Training
#### NOTES:
* This is "NoGAN" based training, described in the DeOldify readme.
* This model prioritizes stable and reliable renderings. It does particularly well on portraits and landscapes. It's not as colorful as the artistic model.
```
import os
os.environ['CUDA_VISIBLE_DEVICES']='0'
... | github_jupyter |
Air Quality Index
1)To identify the Most polluted City
2)Create a Model to Predict the quality of air
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df=pd.read_csv('https://raw.githubusercontent.com/tulseebisen/ML_Projects/main/AirQualityIndex/city_day.csv',parse_date... | github_jupyter |
```
# !pip install mediapipe opencv-python
import mediapipe as mp
import cv2
import numpy as np
import uuid
import os
mp_drawing = mp.solutions.drawing_utils
mp_hands = mp.solutions.hands
```
# getting realtime webcam feed
```
cap = cv2.VideoCapture(0)
while cap.isOpened():
ret, frame = cap.read()
cv2.imshow(... | github_jupyter |
# Entrenador de modelos
Este script contiene el código para entrenar un modelo de regresión lineal a partir de datos de ventas históricos. El modelo como tal es muy sencillo y probablemente no muy bueno, pero el objetivo del ejercicio es mostrar la arquitectura del sistema completo.
Una vez se ejecuta el comando `.fi... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import torch
from UnarySim.sw.kernel.div import CORDIV_kernel
from UnarySim.sw.stream.gen import RNG, SourceGen, BSGen
from UnarySim.sw.metric.metric import ProgressiveError
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import ticker, cm
f... | github_jupyter |
```
%matplotlib inline
%reload_ext autoreload
%autoreload 2
# 多行输出
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
import numpy as np
from kalman_estimation import Kalman4FROLS, Selector, get_mat_data
from tqdm import tqdm, trange
from utils import get_term_dic... | github_jupyter |
# Systems Identification Model Fitting
Fit a Systems Identification model off based off of this [specification](https://hackmd.io/w-vfdZIMTDKwdEupeS3qxQ) and [spec](https://hackmd.io/XVaejEw-QaCghV1Tkv3eVQ) with data obtained in [data_acquisition.ipynb](data/data_acquisition.ipynb).
#### Process changes and decision... | github_jupyter |
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