text stringlengths 2.5k 6.39M | kind stringclasses 3
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## Facial Filters
Using your trained facial keypoint detector, you can now do things like add filters to a person's face, automatically. In this optional notebook, you can play around with adding sunglasses to detected face's in an image by using the keypoints detected around a person's eyes. Checkout the `images/` di... | github_jupyter |
# Building a Supervised Machine Learning Model
The objective of this hands-on activity is to create and evaluate a Real-Bogus classifier using ZTF alert data. We will use the same data from the Day 2 clustering exercise (see [that notebook](https://github.com/LSSTC-DSFP/LSSTC-DSFP-Sessions/blob/master/Session7/Day2/C... | github_jupyter |
```
import numpy as np
from gridworld import GridworldEnv
env = GridworldEnv()
def policy_eval(policy, env, discount_factor=1.0, theta=0.00001):
"""
Evaluate a policy given an environment and a full description of the environment's dynamics.
Args:
policy: [S, A] shaped matrix representing the p... | github_jupyter |
# IoT Microdemos
## Indexing Strategy
A proper indexing strategy is key for efficient querying of data. The first index is mandatory for efficient time series queries in historical data. The second one is needed for efficient retreival of the current, i.e. open, bucket for each device. If all device types have the s... | github_jupyter |
<p style="font-family: Arial; font-size:3.75em;color:purple; font-style:bold"><br>
Introduction to numpy:
</p><br>
<p style="font-family: Arial; font-size:1.25em;color:#2462C0; font-style:bold"><br>
Package for scientific computing with Python
</p><br>
Numerical Python, or "Numpy" for short, is a foundational package... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import os
import cv2
data_dir= r'C:\Users\Benyamin\Downloads\DATASET\four-shapes\shapes'
cat=['circle','square','star','triangle']
for category in cat:
path=os.path.join(data_dir,category)
for img in os.listdir(path):... | github_jupyter |
# Explaining Tree Models with Interventional Feature Perturbation Tree SHAP
<div class="alert alert-info">
To enable SHAP support, you may need to run
```bash
pip install alibi[shap]
```
</div>
## Introduction
This example shows how to apply interventional Tree SHAP to compute shap values exactly for an `xgboo... | github_jupyter |
```
import torch
import torch.nn.functional as F
import torchsde
from torchvision import datasets, transforms
import math
import numpy as np
import pandas as pd
from tqdm import tqdm
import scipy.io
import os
from torchvision.transforms import ToTensor
from torch.utils.data import DataLoader, TensorDataset
import fu... | github_jupyter |
```
# 0110101
# 1011111
# suma=0;
# for(int i = 0; i < n; i++) {
# if(i % 3 == 0) {
# suma+=i;
# }
# }
# primitivne funkcije: +,-,*
# terminali: celi brojevi [-20, 20]
# X=5000
# (a.b).(c.d)
# izraz: (2+3)*(4-1)
# code: ['*' '+' '-' 2 3 4 1]
eval('2+3*4')
0 1 2 3 4 5 6
['*' '+' '-' 2 3 4 1] -> ... | github_jupyter |
# Learning to compute a product
Unlike the communication channel and the element-wise square,
the product is a nonlinear function on multiple inputs.
This represents a difficult case for learning rules
that aim to generalize a function given many
input-output example pairs.
However, using the same type of network stru... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive', force_remount = True)
%tensorflow_version 2.x
!pip install tiffile
!pip install gputools
!pip install imagecodecs
!pip install vollseg
%cd '/content/drive/My Drive/VollSeg/'
import os
import glob
import sys
import numpy as np
from tqdm import tqdm
from... | github_jupyter |
# Python modeling of the impact of social distancing and early termination of lockdown
### Dr. Tirthajyoti Sarkar, Fremont, CA 94536
---
## What is this demo about?
The greatest [global crisis since World War II](https://www.bloomberg.com/opinion/articles/2020-03-24/coronavirus-recession-it-will-be-a-lot-like-world-wa... | github_jupyter |
# Evaluate Modified Algorithms - part 1
## BalancedBaggingClassifier
```
from imblearn.ensemble import BalancedBaggingClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.datasets import make_classification
from imblearn.under_sampling import RandomUnderSampler
from sklearn.model_selection import St... | github_jupyter |
# Kalman-Stan
```
rm(list = ls())
library(rstan)
library(reshape2)
library(ggplot2)
rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())
```
In many settings, the permanent and transitory component of an aggregate time series (like labour income) is estimated by writing up a state space repres... | github_jupyter |
```
import astropy.units as u
from astroduet.duet_telescope import load_telescope_parameters
from astroduet.duet_sensitivity import src_rate, bgd_sky_qe_rate, bgd_electronics, calc_exposure
from astroduet.duet_neff import get_neff
from astroduet.bbmag import bb_abmag_fluence
import numpy as np
from matplotlib import py... | github_jupyter |
# Exercise 7
The result will be evaluated from a report in Jupyter, which must be found in a public GitHub repository. The project must be carried out in the groups assigned in class. Use clear and rigorous procedures. Due date: July 20, 2021, 11:59 pm, through Bloque Neón + (Upload repository link)
# Part 1 - DT
##... | github_jupyter |
# Burn Wound Classification by Transfer Learning
by Carsten Isert, Nov. 2017
This notebook is originally based on the dog-breed classification notebook from the Udacity AI Nanodegree.
## Motivation
The goal of this first step is to classify the burn degree on a given image.
There are two stages that will be covere... | github_jupyter |
# Data description & Problem statement:
The dataset is related to red vinho verde wine samples, from the north of Portugal. The goal is to model wine quality based on physicochemical tests. For more details, please check: https://archive.ics.uci.edu/ml/datasets/wine+quality
* Dataset is imbalanced. The data has 4898 r... | github_jupyter |
# Working with Images stored on the OMERO server using Cell Profiler
This notebook demonstrates how to retrieve Images stored in OMERO and process them using [CellProfiler](http://cellprofiler.org/). The output is saved back to OMERO as CSV attachments.
For this example, we use the pipeline [FruitFlyCells](http://cell... | github_jupyter |
# Logistic Regression with a Neural Network mindset
Welcome to your first (required) programming assignment! You will build a logistic regression classifier to recognize cats. This assignment will step you through how to do this with a Neural Network mindset, and so will also hone your intuitions about deep learning.... | github_jupyter |
```
%matplotlib inline
```
How to post-process simulation data
==================================================
In this example we compute the scattering through an orifice plate in a circular duct with flow. The data is extracted
from two Comsol Multiphysics simulations with a similar setup as in
`this study <htt... | github_jupyter |
# Assignment 3 Ungraded Sections - Part 1: BERT Loss Model
Welcome to the part 1 of testing the models for this week's assignment. We will perform decoding using the BERT Loss model. In this notebook we'll use an input, mask (hide) random word(s) in it and see how well we get the "Target" answer(s).
## Colab
Since... | github_jupyter |
```
import pandas as pd
import numpy as np
df_us_counties = pd.read_csv('data/us-counties.csv')
df_us_counties
df_us_states = pd.read_csv('data/us-states.csv')
df_us_states
df_us_states = df_us_states.sort_values(["state", "date"]).reset_index()
df_us_states
# loop to reverse cumulative death count and get daily number... | github_jupyter |
# Image features exercise
*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*
We have see... | github_jupyter |
```
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Model
from tensorflow.keras import applications
from tensorflow.keras.layers import BatchNormalization, Conv2D, AveragePooling2D, TimeDistributed, Dense, Dropout, Activa... | github_jupyter |
<a href="http://landlab.github.io"><img style="float: left"
src="https://raw.githubusercontent.com/landlab/tutorials/release/landlab_header.png"></a>
# Animate Landlab output
<hr>
<p><small>More Landlab tutorials:
<a href="https://landlab.readthedocs.io/en/latest/user_guide/tutorials.html">https://landlab.readthedocs... | github_jupyter |
# Multivariate Time Series Forecasting
Multivariate time series forecasting works similarly to univariate time series forecasting (covered [here](0_ForecastIntro.ipynb) and [here](1_ForecastFeatures.ipynb)). The main difference is that you must specify the index of a target univariate to forecast, e.g. for a 5-variabl... | github_jupyter |
```
# This cell is added by sphinx-gallery
!pip install mrsimulator --quiet
%matplotlib inline
import mrsimulator
print(f'You are using mrsimulator v{mrsimulator.__version__}')
```
# ⁸⁷Rb 2D 3QMAS NMR of RbNO₃
The following is a 3QMAS fitting example for $\text{RbNO}_3$. The dataset was
acquired and shared by Bre... | github_jupyter |
# Project 2: Inference and Capital Punishment
Welcome to Project 2! You will investigate the relationship between murder and capital punishment (the death penalty) in the United States. By the end of the project, you should know how to:
1. Test whether observed data appears to be a random sample from a distribution... | github_jupyter |
# Diagrams Overview
One piece generate document workflows allow for the creation of materials containing a wide variety of simple diagrams produced from simple text descriptions contained within the body of the document.
Making changes to the diagram simply requires a change to the original text description of it. Wh... | github_jupyter |
```
#Function to generate a 3-panel plot for input arrays
def plot_array(dem, clim=None, titles=None, cmap='inferno', label=None, overlay=None, fn=None, close_fig=True):
fig, ax = plt.subplots(1,1, sharex=True, sharey=True, figsize=(10,5))
alpha = 1.0
#Gray background
ax.set_facecolor('0.5')
#Force ... | github_jupyter |
# TTstar benchmarks
First, let's get formalities out of the way.
```
library(ggplot2)
library(reshape2)
library(repr)
library(dplyr)
options(repr.plot.width=4, repr.plot.height=3)
# load measured data
#xs <- read.csv('benchmark-1490607733.csv')
#xs <- read.csv('benchmark-1491380156.csv')
#xs <- read.csv('benchmark-1... | github_jupyter |
<a href="https://hub.callysto.ca/jupyter/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fcallysto%2Fcurriculum-notebooks&branch=master&subPath=SocialStudies/BubonicPlague/bubonic-pla... | github_jupyter |
### Attacks
Our implementation inludes three black-box patch attacks: Texture-based Patch Attack (TPA), MonoChrome Patch Attack (MPA) in our [paper](https://arxiv.org/abs/2004.05682); Metropolis-Hastings Attack (HPA) originally proposed in [paper](http://www.bmva.org/bmvc/2016/papers/paper137/index.html). Besides, we ... | github_jupyter |
# test note
* jupyterはコンテナ起動すること
* テストベッド一式起動済みであること
```
from pathlib import Path
# settings cell
# mounted dir
ait_dir = Path('/workdir/root')
ait_name='eval_bdd100k_aicc_tf2.3'
ait_full_name='eval_bdd100k_aicc_tf2.3_0.1'
# (dockerホスト側の)インベントリ登録用アセット格納ルートフォルダ
#invenotory_root_dir=r'F:\qai-testbed\dev\qai-testbe... | github_jupyter |
# TensorFlow 자습서 #03-B
# Layers API
원저자 [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)
/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ) / 번역 곽병권
## 개요
TensorFlow에서 신경망을 만들 때 빌더 API를 사용하는 것이 중요합니다. ... | github_jupyter |
This work implement the paper of "Preference fusion for community detection in social networks" F.Elarbi, T.Bouadi, A.Martin, B. Ben Yaghlane
Coded by Yiru Zhang <yiru.zhang@irisa.fr>
```
import numpy as np
import csv
import networkx as nx
import matplotlib.pyplot as plt
#define the class for belief fuction of one p... | github_jupyter |
### Dependencies
```
import os
import cv2
import math
import random
import shutil
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import multiprocessing as mp
import albumentations as albu
import matplotlib.pyplot as plt
from tensorflow import set_random_seed
from sklearn.model_selection i... | github_jupyter |
<a href="https://colab.research.google.com/github/mateusjunges/music-gender-detection/blob/master/music-gender-detection.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
# Imports
import cv2
import numpy as np
import pandas as pd
import re
import... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#RNN-(Recurrent-Neural-Network)" data-toc-modified-id="RNN-(Recurrent-Neural-Network)-1"><span class="toc-item-num">1 </span>RNN (Recurrent Neural Network)</a></span><ul class="toc-item"><li><span... | github_jupyter |
##### Copyright 2020 Google LLC.
Licensed under the Apache License, Version 2.0 (the "License");
```
#@title License
# 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.or... | github_jupyter |
```
%matplotlib inline
```
# Demo Agg Filter
```
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import matplotlib.transforms as mtransforms
from matplotlib.colors import LightSource
from matplotlib.artist import Artist
import numpy as np
def smooth1d(x, window_len):
# copied from http://www.scipy.o... | github_jupyter |
.. _nb_subset_selection:
## Subset Selection Problem
A genetic algorithm can be used to approach subset selection problems by defining custom operators. In general a metaheuristic algorithm might not be the ultimate goal to implement in a real-world scenario, however, it might be useful to investigate patterns or cha... | github_jupyter |
```
# importing prerequisites
import sys
import requests
import cv2
import random
import tarfile
import json
import numpy as np
import pdf2image
from os import path
from PIL import Image
from PIL import ImageFont, ImageDraw
from glob import glob
from matplotlib import pyplot as plt
from pdf2image import convert_from_pa... | github_jupyter |
```
from src.dataset_wrapper import *
from src.networks import *
import torch
from tqdm import tqdm
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import pickle
device = 'cuda:0'
net_params = {
"relation_dim": 23,
"object_dim": 8,
"hidden_dim": 256,
}
dataset_params = {
"d... | github_jupyter |
# Numerical solution to the 1-dimensional Time Independent Schroedinger Equation
Based on the paper "Matrix Numerov method for solving Schroedinger's equation" by Mohandas Pillai, Joshua Goglio, and Thad G. Walker, _American Journal of Physics_ **80** (11), 1017 (2012). [doi:10.1119/1.4748813](http://dx.doi.org/10.111... | github_jupyter |
```
try:
from openmdao.utils.notebook_utils import notebook_mode
except ImportError:
!python -m pip install openmdao[notebooks]
```
# ExplicitComponent
Explicit variables are those that are computed as an explicit function of other variables. For instance, $z$ would be an explicit variable, given $z=sin(y)$, ... | github_jupyter |
[](http://rpi.analyticsdojo.com)
<center><h1>Introduction to Feature Creation & Dummy Variables</h1></center>
<center><h3><a href = 'http://rpi.analyticsdojo.com'>rpi.analyticsdojo.com</a></h3></center>
... | github_jupyter |
# Q-Learning
* value learning : state + action
* learn to find a max Q(s, a) (Q function calculates the max discounted future value, Q value)
* Q value: the expected long-term rewards
$$Q^*(s_t, a_t) = max_\pi{E[\sum^T_{i=t}\gamma^ir^i]}$$
* the chicken-and-egg conundrum
## Bellman Function
* redefine Q-value as... | github_jupyter |
# Building a Controller
The following documents the development of a new controller.
In this case we are going to implement an arbitrary controllable storage unit. This
may be a battery, an electrically powered car or some sort of reservoir storage.
## Modelling a Battery
In order to simulate a storage system we use... | github_jupyter |
Note: The evals here have been run on GPU so they may not exactly match the results reported in the paper which were run on TPUs, however the difference in accuracy should not be more than 0.1%.
# Setup
```
import tensorflow as tf
import tensorflow_datasets as tfds
CROP_PROPORTION = 0.875 # Standard for ImageNet.
HE... | github_jupyter |
```
# reload packages
%load_ext autoreload
%autoreload 2
```
### Choose GPU (this may not be needed on your computer)
```
%env CUDA_DEVICE_ORDER=PCI_BUS_ID
%env CUDA_VISIBLE_DEVICES=0
import tensorflow as tf
gpu_devices = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(gpu... | github_jupyter |
# Notebook showing how to correctly calculate CPUE for CCFRP data
```
# Imports
import numpy as np
import pandas as pd
# Load data
occ = pd.read_csv('CCFRP_grid-level_occurrence.csv')
mof = pd.read_csv('CCFRP_grid-level_mof.csv', sep=',')
```
**Note** that I've now included an `organismQuantity` column in occ that ... | github_jupyter |
# Windowed-Sinc Filters
Windowed-sinc filters are used to separate one band of frequencies from another. They are very stable, produce few surprises, and can be pushed to incredible performance levels. These exceptional frequency domain characteristics are obtained at the expense of poor performance in the time domain,... | github_jupyter |
# pandas, linear regression
* регрессия - линию провести
* классификация - конечное чило ответов или бинарная калссификация
* много классовая классификация - от 1 до К классов
* классификация с пересекающими классами - (какая тема в статье? математика, биология, экономика)
* ранжирования - набор документов $d_1, ...... | github_jupyter |
<center> <h2> DS 3000 - Fall 2021</h2> </center>
<center> <h3> DS Report </h3> </center>
<center> <h3>E-Commerce Trends</h3> </center>
<center><h4>Armaan Pruthi, Angel Gong, Aritra Saharay</h4></center>
<hr style="height:2px; border:none; color:black; background-color:black;">
#### Executive Summary:
The e-commerce... | github_jupyter |
## Amazon SageMaker Processing jobs
*이 노트북은 [Amazon SageMaker Processing jobs (영문 원본)](https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker_processing/scikit_learn_data_processing_and_model_evaluation/scikit_learn_data_processing_and_model_evaluation.ipynb) 의 한국어 번역입니다.*
Amazon SageMaker Process... | 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 |
# Exporting BioCantor data models
BioCantor data models can be exported to any of:
1. GenBank
2. GFF3
3. JSON
4. BED (TranscriptInterval and FeatureInterval only).
The JSON representation can be read directly by the `marshmallow` data structures that build the data model.
```
from inscripta.biocantor.io.gff3.parser... | github_jupyter |
<a href="https://colab.research.google.com/github/raoyongming/DynamicViT/blob/master/colab_demo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!git clone https://github.com/raoyongming/DynamicViT.git
```
```
!pip install timm
import os
os.ch... | github_jupyter |
# Assignment 2: Naive Bayes
Welcome to week two of this specialization. You will learn about Naive Bayes. Concretely, you will be using Naive Bayes for sentiment analysis on tweets. Given a tweet, you will decide if it has a positive sentiment or a negative one. Specifically you will:
* Train a naive bayes model on a... | github_jupyter |
<i>Copyright (c) Microsoft Corporation. All rights reserved.</i>
<i>Licensed under the MIT License.</i>
# Standard Variational Autoencoders for Collaborative Filtering on MovieLens dataset.
This notebook accompanies the paper "*A Hybrid Variational Autoencoder for Collaborative Filtering*" by Kilol Gupta, Mukund Y. ... | github_jupyter |
# Character level language model - Dinosaurus land
Welcome to Dinosaurus Island! 65 million years ago, dinosaurs existed, and in this assignment they are back. You are in charge of a special task. Leading biology researchers are creating new breeds of dinosaurs and bringing them to life on earth, and your job is to gi... | github_jupyter |
```
from medpy.io import load
image_data, image_header = load('training/HGG/brats_tcia_pat447_0313/VSD.Brain.XX.O.MR_T1.41130.mha')
image_data.shape
image_data
image_header.get_voxel_spacing()
image_header.get_offset()
from medpy.filter import otsu
threshold = otsu(image_data)
output_data = image_data > threshold
thres... | github_jupyter |
# Plot.ly Charts
```
import pickle
from collections import Counter, OrderedDict
# load the pickled files from previous notebook
meat_potatoes_sent = pickle.load(open("aws_backup/meat_potatoes_sentiment.pkl", "rb"))
meat_potatoes_dict = pickle.load(open("aws_backup/meat_potatoes_dict.pkl", "rb"))
primanti_sent = pickl... | github_jupyter |
```
import keras
keras.__version__
```
# Predicting house prices: a regression example
This notebook contains the code samples found in Chapter 3, Section 6 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text features far more... | github_jupyter |
# Week 2 - Classical ML Models
## 2. Introduction to Scikit-learn
Started as a Google Summer of Code project in 2007, **scikit-learn** is one of the most popular machine learning libraries today. It provides efficient implementations of the ML models we will be taking a look at this week, as well as various other uti... | github_jupyter |
# Implementing the Gradient Descent Algorithm
In this lab, we'll implement the basic functions of the Gradient Descent algorithm to find the boundary in a small dataset. First, we'll start with some functions that will help us plot and visualize the data.
```
import matplotlib.pyplot as plt
import numpy as np
import ... | github_jupyter |
# 빅데이터 분석 기말고사
- toc:true
- branch: master
- badges: false
- comments: false
- author: 최서연
- categories: [Big Data Analysis]
## `#1`. 체인룰과 역전파기법
주어진 자료가 아래와 같다고 하자.
- ${\bf X} = \begin{bmatrix} 1 & 2.1 \\ 1 & 3.0 \end{bmatrix}$
- ${\bf y} = \begin{bmatrix} 3.0 \\ 5.0 \end{bmatrix}$
손실함수의 정의가 아래와 같다고 하자.
$$lo... | github_jupyter |
```
from quchem.Hamiltonian_Generator_Functions import *
from quchem.Graph import *
### HAMILTONIAN start
Molecule = 'H2'
geometry = [('H', (0., 0., 0.)), ('H', (0., 0., 0.74))]
basis = 'sto-3g'
### Get Hamiltonian
Hamilt = Hamiltonian_PySCF(Molecule,
run_scf=1, run_mp2=1, run_cisd=1, run_ccsd=1,... | github_jupyter |
```
import os
import tensorflow as tf
import tensorflow.python.platform
from tensorflow.python.platform import gfile
import numpy as np
import glob
classes = np.array(['ayam_bakar', 'ayam_crispy', 'bakso', 'gado2', 'ikan_bakar', 'mie_goreng', 'nasi_goreng', 'pecel_lele', 'pizza', 'rendang', 'sate', 'soto', 'sushi'])
n... | github_jupyter |
# K Nearest Neighbors Project
Welcome to the KNN Project! This will be a simple project very similar to the lecture, except you'll be given another data set. Go ahead and just follow the directions below.
## Import Libraries
**Import pandas,seaborn, and the usual libraries.**
```
import pandas as pd
import numpy as ... | github_jupyter |
# Publications markdown generator for academicpages
Takes a set of bibtex of publications and converts them for use with [academicpages.github.io](academicpages.github.io). This is an interactive Jupyter notebook ([see more info here](http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_is_jupyter.html... | github_jupyter |
```
# Copyright 2021 NVIDIA Corporation. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | github_jupyter |
<h1> OpenSees Examples Manual Examples for OpenSeesPy</h1>
<h2>OpenSees Example 1a. 2D Elastic Cantilever Column -- Earthquake Ground Motion</h2>
<p>
You can find the original Examples:<br>
https://opensees.berkeley.edu/wiki/index.php/Examples_Manual<br>
Original Examples by By Silvia Mazzoni & Frank McKenna, 2006, i... | github_jupyter |
# NumPy
```
# importação do pacote abaixo com abreviação
import numpy as np
```
# NumPy - criação de matrizes
```
# Matriz de uma dimensão
np.array([1,2,3])
# Matriz bidimensional
np.array([[1, 2], [3, 4]])
# Array 1D de comprimento 3 todos os valores 0
np.zeros(3)
# Matriz 3x4 com todos os valores em 1
np.ones((... | github_jupyter |
### AnADAMA2 Example: A workflow to download files in parallel
[AnADAMA2](http://huttenhower.sph.harvard.edu/anadama2) is the next generation of AnADAMA (Another Automated Data Analysis Management Application). AnADAMA is a tool to create reproducible workflows and execute them efficiently. Tasks can be run locally or... | github_jupyter |
```
import glob
import pandas as pd
import numpy as np
import networkx as nx
import seaborn as sns
print(nx.__version__)
from matplotlib import pyplot as plt
import sys
sys.path.append('../.')
from comap.mapper import CoMap
from comap.graph_utils import (compute_graph_deltas)
from comap.helper_utils import (get_red... | github_jupyter |
#1. Install Dependencies
First install the libraries needed to execute recipes, this only needs to be done once, then click play.
```
!pip install git+https://github.com/google/starthinker
```
#2. Get Cloud Project ID
To run this recipe [requires a Google Cloud Project](https://github.com/google/starthinker/blob/mast... | github_jupyter |
```
# Import Libraries
import numpy as np
import pandas as pd
import os
from torchsummary import summary
import sys
import torch
from time import time
import torch.nn as nn
import torch.optim as optim
from torch.utils import data
from torch.autograd import Variable
import transformers
import random
import pickle
from... | github_jupyter |
Azure ML & Azure Databricks notebooks by Parashar Shah.
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
We support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as y... | github_jupyter |
# Week 1 - Exercises
## Working with Numbers
```
## create a variable called num equal to 30 billion in scientific notation
## print it as "This number is $30,000,000,000.00" using f-string literal (2 decimal places)
## create a variable called num2 equal to 30 billion in scientific notation
## Print it like you did ... | github_jupyter |
# Сглаживание как способ быстрого решения негладких задач
$$
\min_x f(x)
$$
Основано на статье [Smooth minimization of non-smooth functions](https://www.math.ucdavis.edu/~sqma/MAT258A_Files/Nesterov-2005.pdf) by Y. Nesterov
## Текущие достижения для выпуклых функций
- Функция $f$ негладкая
$$
\epsilon \sim O\left... | github_jupyter |
```
# MODIFY!
# use Robust!
model_name = 'poi-baseline-no'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('./data/d-no-ns.csv')
# df.columns
# df.head()
df.shape
# df.info()
X = df.drop('throughput',axis=1)
X.shape
y = df['throughput']
y.shape
# Split the... | github_jupyter |
# RGI11 (Central Europe)
F. Roura-Adseiras & Fabien Maussion
Goal:
- Alps: updates of the Paul 2003 dataset
- Pytrenees: new inventory by Izagirre
```
import pandas as pd
import geopandas as gpd
import subprocess
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import seaborn as sns
import numpy... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelBinarizer
from sklearn.metrics import accuracy_score
from keras.datasets import cifar10
from keras.models import Model, Sequential
from keras.layers import Input, Dense, Concatenate, Reshape, Dropout, Conv... | github_jupyter |
The main package used in this notebook is **CasADi**. It has automatic differentiacion capabilities, focuses in optimal control. Its following integration are used:
- IDAS, for diferential algebraic equations
- IPOPT for non linear optimization
```
%matplotlib inline
from casadi import SX,DM,solve,substitute,kr... | github_jupyter |
```
#!pip install torch==1.0.1
## uploading files and put it in the right folder
#!mkdir data
#!mkdir checkpoint
#!mkdir models
#!mv abstract.py data
#!mv common.py data
#!mv iCIFAR.py data
#!mv idadataloader.py data
#!mv cifar_order.npy data
from torch import tensor
import torch
import torch.nn as nn
import torch.opti... | github_jupyter |
# Realization of Recursive Filters
*This jupyter notebook is part of a [collection of notebooks](../index.ipynb) on various topics of Digital Signal Processing. Please direct questions and suggestions to [Sascha.Spors@uni-rostock.de](mailto:Sascha.Spors@uni-rostock.de).*
## Cascaded Structures
The realization of rec... | github_jupyter |
```
import pickle
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import datetime
font = {'family': 'sans-serif', # Helvetica
'size' : 12}
matplotlib.rc('font', **font)
text = {'usetex': False}
matplotlib.rc('text', **text)
monospace_font = {'fontname':'monospace'}
... | github_jupyter |
# Profiling and Optimizing
* * *
By C Hummels (Caltech)
```
import random
import numpy as np
from matplotlib import pyplot as plt
```
It can be hard to guess which code is going to operate faster just by looking at it because the interactions between software and computers can be extremely complex. The best way ... | github_jupyter |
# Convolution
:label:`ch_conv_cpu`
In this section, we will optimize the convolution operator defined in :numref:`ch_conv` on CPUs. Specifically, this is a 2-D convolution operator.
## Setup
```
def set_env(num, current_path='.'):
import sys
from pathlib import Path
ROOT = Path(current_path).resolve().p... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
%aimport utils_1_1
import pandas as pd
import numpy as np
import altair as alt
from altair_saver import save
import datetime
import dateutil.parser
from os.path import join
from constants_1_1 import SITE_FILE_TYPES
from utils_1_1 import (
get_site_file_paths,
get_site_fi... | github_jupyter |
# Introduction: Using Watt Time to Find Energy Sources
The purpose of this notebook is to explore the Watt Time API to find what kind of electricity we are currently using. The Watt Time API allows us to see a breakdown of the energy generation for a given location.
```
# Standard Data Science Helpers
import numpy as... | github_jupyter |
```
import pandas as pd
from pandas import ExcelWriter
from pandas import ExcelFile
import datetime as dt
from datetime import datetime, timedelta
import numpy as np
import xarray as xr
import matplotlib.pyplot as plt
from copy import copy
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import ... | github_jupyter |
# Simple RNN
In ths notebook, we're going to train a simple RNN to do **time-series prediction**. Given some set of input data, it should be able to generate a prediction for the next time step!
<img src='assets/time_prediction.png' width=40% />
> * First, we'll create our data
* The... | github_jupyter |
```
# !wget https://f000.backblazeb2.com/file/malay-dataset/keyphrase/keyphrase-twitter-no-calon.json
# !wget https://raw.githubusercontent.com/huseinzol05/Malay-Dataset/master/keyphrase/twitter-bahasa/topics.json
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
import json
with open('topics.json') as fopen:
top... | github_jupyter |
# Python avançado - Parte 1
## Dados em cores
- [Sistemas de Cor PANTONE](www.pantone.com/color-systems/pantone-color-systems-explained)
- PANTONE MATCHING SYSTEM (PMS): adequado para impressão, artes gráficas e trabalhos digitais;
- FASHION, HOME + INTERIORS (FHI) SYSTEM: adequado para vestuário, tecidos, ma... | github_jupyter |
# Table of Contents
<p><div class="lev1 toc-item"><a href="#TP-6---Programmation-pour-la-préparation-à-l'agrégation-maths-option-info" data-toc-modified-id="TP-6---Programmation-pour-la-préparation-à-l'agrégation-maths-option-info-1"><span class="toc-item-num">1 </span>TP 6 - Programmation pour la préparati... | github_jupyter |
# Detecting active ranges
Here we examine, how a modestly deep network of dense layers is able to recognize the fact that a single feature influences the prediction only when it is within a certain range.
```
%matplotlib inline
import random
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
impor... | github_jupyter |
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