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# Taylor problem 3.23
last revised: 04-Jan-2020 by Dick Furnstahl [furnstahl.1@osu.edu]
**This notebook is almost ready to go, except that the initial conditions and $\Delta v$ are different from the problem statement and there is no statement to print the figure. Fix these and you're done!**
This is a conservatio... | github_jupyter |
```
import pandas as pd
import numpy as np
```
# Distances
```
activator = "sgmd"
mnist_sgmd = []
hands_sgmd = []
fashn_sgmd = []
for i in range(0, 10):
dataset = 'mnist'
df_cnn_relu0_1 = pd.read_csv(dataset + "/results/" + activator + "/cnn_K" + str(i) + ".csv")
dataset = 'handsign_mnist'
df_cnn_re... | github_jupyter |
```
import os
from pprint import pprint
import torch
import torch.nn as nn
from transformers import BertForTokenClassification, BertTokenizer
from transformers import AdamW
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from sklearn.model_selection import train_test_split
impo... | github_jupyter |
<a id='StartingPoint'></a>
# ONNX classification example
Sharing DL models between frameworks or programming languages is possible with Open Neural Network Exchange (ONNX for short).
This notebook starts from an onnx model exported from MATLAB and uses it in Python.
On MATLAB a GoogleNet model pre-trained on ImageNet... | github_jupyter |
# Flux.pl
The `Flux.pl` Perl script takes four input parameters:
`Flux.pl [input file] [output file] [bin width (s)] [geometry base directory]`
or, as invoked from the command line,
`$ perl ./perl/Flux.pl [input file] [output file] [bin width (s)] [geometry directory]`
## Input Parameters
* `[input file]`
`Flux.... | github_jupyter |
# Character-level recurrent sequence-to-sequence model
**Author:** [fchollet](https://twitter.com/fchollet)<br>
**Date created:** 2017/09/29<br>
**Last modified:** 2020/04/26<br>
**Description:** Character-level recurrent sequence-to-sequence model.
## Introduction
This example demonstrates how to implement a basic ... | github_jupyter |
```
import numpy as np
import tensorflow as tf
import os
import random
from collections import defaultdict
import pandas as pd
import time
def load_data_train():
user_movie = defaultdict(set)
data=pd.read_csv('BRP_datas\\BRP_common_user_book\\common_user_book_19_1VS2.csv')
num_user=len(pd.unique(data['user... | github_jupyter |
**Note**: There are multiple ways to solve these problems in SQL. Your solution may be quite different from mine and still be correct.
**1**. Connect to the SQLite3 database at `data/faculty.db` in the `notebooks` folder using the `sqlite` package or `ipython-sql` magic functions. Inspect the `sql` creation statement ... | github_jupyter |
# Frequent opiate prescriber
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import preprocessors as pp
sns.set(style="darkgrid")
data = pd.read_csv('../data/prescriber-info.csv')
data.head()
```
## Variable Separation
```
uniq_cols = ['NPI']
cat_cols = list(data.... | github_jupyter |
# Priprava okolja
```
!pip install transformers
!pip install sentencepiece
import csv
import torch
from torch import nn
from transformers import AutoTokenizer, AutoModel
import pandas as pd
from google.colab import drive
import transformers
import json
from tqdm import tqdm
from torch.utils.data import Dataset, DataL... | github_jupyter |
```
import os
import numpy as np
import pandas as pd
import gc
```
# To ensemble I used submissions from 8 public notebooks:
* LB: 0.0225 - https://www.kaggle.com/lunapandachan/h-m-trending-products-weekly-add-test/notebook
* LB: 0.0217 - https://www.kaggle.com/tarique7/hnm-exponential-decay-with-alternate-items/noteb... | github_jupyter |
# The Shared Library with GCC
When your program is linked against a shared library, only a small table is created in the executable. Before the executable starts running, **the operating system loads the machine code needed for the external functions** - a process known as **dynamic linking.**
* Dynamic linkin... | github_jupyter |
# Searching the UniProt database and saving fastas:
This notebook is really just to demonstrate how Andrew finds the sequences for the datasets. <br>
If you do call it from within our github repository, you'll probably want to add the fastas to the `.gitignore` file.
```
# Import bioservices module, to run remote U... | github_jupyter |
# Validation report for dmu26_XID+PACS_COSMOS_20170303
The data product dmu26_XID+PACS_COSMOS_20170303, contains three files:
1. dmu26_XID+PACS_COSMOS_20170303.fits: The catalogue file
2. dmu26_XID+PACS_COSMOS_20170303_Bayes_pval_PACS100.fits: The Bayesian pvalue map
3. dmu26_XID+PACS_COSMOS_20170303_Bayes_pval_PACS1... | github_jupyter |
# RNA World Hypothesis
RNA is a simpler cousin of DNA. As you may know, RNA is widely thought to be the first self replicating life-form to arise perhaps around 4 billion years ago. One of the strongest arguments for this theory is that RNA is able to carry information in its nucleotides like DNA, and like protein, i... | github_jupyter |
# Introduction
In this post,we will talk about some of the most important papers that have been published over the last 5 years and discuss why they’re so important.We will go through different CNN Architectures (LeNet to DenseNet) showcasing the advancements in general network architecture that made these architectu... | github_jupyter |
## Probalistic Confirmed COVID19 Cases- Denmark
**Jorge: remember to reexecute the cell with the photo.**
### Table of contents
[Initialization](#Initialization)
[Data Importing and Processing](#Data-Importing-and-Processing)
1. [Kalman Filter Modeling: Case of Denmark Data](#1.-Kalman-Filter-Modeling:-Case-of-Denm... | github_jupyter |
<a href="https://colab.research.google.com/github/Janani-harshu/Machine_Learning_Projects/blob/main/Covid19_death_prediction.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Covid-19 is one of the deadliest viruses you’ve ever heard. Mutations in cov... | github_jupyter |
## Dependencies
```
import json, warnings, shutil, glob
from jigsaw_utility_scripts import *
from scripts_step_lr_schedulers import *
from transformers import TFXLMRobertaModel, XLMRobertaConfig
from tensorflow.keras.models import Model
from tensorflow.keras import optimizers, metrics, losses, layers
SEED = 0
seed_ev... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib as plt
from shapely.geometry import Point, Polygon
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import KFold
import zipfile
import requests
import os
import shutil
... | github_jupyter |
# Important installation
This notebook requires unusual packages: `LightGBM`, `SHAP` and `LIME`.
For installation, do:
`conda install lightgbm lime shap`
## Initial classical imports
```
import os
import numpy as np
import pandas as pd
import warnings
warnings.simplefilter(action='ignore', category=Warning)
import ... | github_jupyter |
# Advanced RNNs
<img src="https://raw.githubusercontent.com/GokuMohandas/practicalAI/master/images/logo.png" width=150>
In this notebook we're going to cover some advanced topics related to RNNs.
1. Conditioned hidden state
2. Char-level embeddings
3. Encoder and decoder
4. Attentional mechanisms
5. Implementation
... | github_jupyter |
### Supervised Machine Learning Models for Cross Species comparison of supporting cells
```
import numpy as np
import pandas as pd
import scanpy as sc
import matplotlib.pyplot as plt
import os
import sys
import anndata
def MovePlots(plotpattern, subplotdir):
os.system('mkdir -p '+str(sc.settings.figdir)+'/'+subp... | github_jupyter |
# Nodejs MNIST model Deployment
* Wrap a nodejs tensorflow model for use as a prediction microservice in seldon-core
* Run locally on Docker to test
## Dependencies
* ```pip install seldon-core```
* [Helm](https://github.com/kubernetes/helm)
* [Minikube](https://github.com/kubernetes/minikube)
* [S2I](https... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W1D5_DimensionalityReduction/student/W1D5_Tutorial3.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Neuromatch Academy: Week 1, Day 5, Tutoria... | github_jupyter |
# Projection, Joining, and Sorting
## Setup
```
import ibis
import os
hdfs_port = os.environ.get('IBIS_WEBHDFS_PORT', 50070)
hdfs = ibis.hdfs_connect(host='quickstart.cloudera', port=hdfs_port)
con = ibis.impala.connect(host='quickstart.cloudera', database='ibis_testing',
hdfs_client=hdfs)
p... | github_jupyter |
# Predicting Boston Housing Prices
## Using XGBoost in SageMaker (Hyperparameter Tuning)
_Deep Learning Nanodegree Program | Deployment_
---
As an introduction to using SageMaker's High Level Python API for hyperparameter tuning, we will look again at the [Boston Housing Dataset](https://www.cs.toronto.edu/~delve/d... | github_jupyter |
**Instructions:**
1. **For all questions after 10th, Please only use the data specified in the note given just below the question**
2. **You need to add answers in the same file i.e. PDS_UberDriveProject_Questions.ipynb' and rename that file as 'Name_Date.ipynb'.You can mention the date on which you will be uploading... | github_jupyter |
```
import process_output
from PIL import Image, ImageEnhance, ImageFilter
import requests
from io import BytesIO
import imgkit
import json
def get_unsplash_url(client_id, query, orientation):
root = 'https://api.unsplash.com/'
path = 'photos/random/?client_id={}&query={}&orientation={}'
search_url = r... | github_jupyter |
# Robust Scaler - Experimento
Este é um componante que dimensiona atributos usando estatísticas robustas para outliers. Este Scaler remove a mediana e dimensiona os dados de acordo com o intervalo quantil (o padrão é Amplitude interquartil). Amplitude interquartil é o intervalo entre o 1º quartil (25º quantil) e o 3º ... | github_jupyter |
<a href="https://colab.research.google.com/github/ralsouza/python_fundamentos/blob/master/src/05_desafio/05_missao05.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## **Missão: Analisar o Comportamento de Compra de Consumidores.**
### Nível de Difi... | github_jupyter |
WINE CLASSIFIER
```
# Imports
from io import StringIO
import pandas as pd
import spacy
from cytoolz import *
import numpy as np
from IPython.display import display
import seaborn as sns
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import chi2
from sklearn.svm import LinearSVC
fr... | github_jupyter |
# Criminology in Portugal (2011)
## Introduction
> In this _study case_, it will be analysed the **_crimes occurred_** in **_Portugal_**, during the civil year of **_2011_**. It will analysed all the _categories_ or _natures_ of this **_crimes_**, _building some statistics and making some filtering of data related to... | github_jupyter |
```
!pip install torch torchvision
!pip install wavio
!pip install sounddevice
from google.colab import drive
drive.mount('/content/drive')
!ls "/content/drive/My Drive/IMT Atlantique/Projet 3A /master/kitchen20"
%cd /content/drive/My Drive/IMT Atlantique/Projet 3A /master/kitchen20
from envnet import EnvNet
from kitc... | github_jupyter |
```
from sklearn import *
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_iris
from sklearn.ensemble import (RandomForestClassifier, ExtraTreesClassifier,
AdaBoostClassifier)
from sklearn.tree import Decision... | github_jupyter |
##### Copyright 2021 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 |
<a href="https://colab.research.google.com/github/Shubham0Rajput/Feature-Detection-with-AKAZE/blob/master/AKAZE_code.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
#IMPORT FILES
import matplotlib.pyplot as plt
import cv2
#matplotlib inline
#MO... | github_jupyter |
## Programming for Data Analysis Project 2018
### Patrick McDonald G00281051
#### Problem statement
For this project you must create a data set by simulating a real-world phenomenon of your choosing. You may pick any phenomenon you wish – you might pick one that is of interest to you in your personal or professional... | github_jupyter |
# Перечислимые типы (enums)
## 1. Базовые возможности
```
enum Color
{
White, // 0
Red, // 1
Green, // 2
Blue, // 3
Orange, // 4
}
Color white = Color.White;
Console.WriteLine(white); // White
Color red = (Color)1; // Так можно приводить к типу перечисления
Console.WriteLine(red... | github_jupyter |
<center>
<img src="https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png" width="300" alt="cognitiveclass.ai logo" />
</center>
# **Space X Falcon 9 First Stage Landing Prediction**
## Web scraping Falcon 9 and Falcon Heavy Launches Records from Wikipedia
... | github_jupyter |
# Entities Recognition
<div class="alert alert-info">
This tutorial is available as an IPython notebook at [Malaya/example/entities](https://github.com/huseinzol05/Malaya/tree/master/example/entities).
</div>
<div class="alert alert-warning">
This module only trained on standard language structure, so it is no... | github_jupyter |
<a href="https://colab.research.google.com/github/Phantom-Ren/PR_TH/blob/master/FCM.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
<center>
# 模式识别·第七次作业·模糊聚类（Fussy C Means）
#### 纪泽西 17375338
#### Last Modified:26th,April,2020
</center>
<table ... | github_jupyter |
<img src="https://github.com/OpenMined/design-assets/raw/master/logos/OM/horizontal-primary-light.png" alt="he-black-box" width="600"/>
# Homomorphic Encryption using Duet: Data Owner
## Tutorial 2: Encrypted image evaluation
Welcome!
This tutorial will show you how to evaluate Encrypted images using Duet and TenSE... | github_jupyter |
# Shallow regression for vector data
This script reads zip code data produced by **vectorDataPreparations** and creates different machine learning models for
predicting the average zip code income from population and spatial variables.
It assesses the model accuracy with a test dataset but also predicts the number to... | github_jupyter |
# RNN Sentiment Classifier
In this notebook, we use an RNN to classify IMDB movie reviews by their sentiment.
[](https://colab.research.google.com/github/the-deep-learners/deep-learning-illustrated/blob/master/notebooks/rnn_sentiment_classifier... | github_jupyter |
# Intro to Jupyter Notebooks
### `Jupyter` is a project for developing open-source software
### `Jupyter Notebooks` is a `web` application to create scripts
### `Jupyter Lab` is the new generation of web user interface for Jypyter
### But it is more than that
#### It lets you insert and save text, equations & visuali... | github_jupyter |
# Qiskit Aqua: Vehicle Routing
## The Introduction
Logistics is a major industry, with some estimates valuing it at USD 8183 billion globally in 2015. Most service providers operate a number of vehicles (e.g., trucks and container ships), a number of depots, where t... | github_jupyter |
<a href="https://colab.research.google.com/github/JoseAugustoVital/Decision-Score-MarketPlace/blob/main/decision_score.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# ***UNIVERSIDADE FEDERAL DO MATO GROSSO DO SUL***
# Análise de dados para aumenta... | github_jupyter |
# Introduction to geospatial vector data in Python
```
%matplotlib inline
import pandas as pd
import geopandas
pd.options.display.max_rows = 10
```
## Importing geospatial data
Geospatial data is often available from specific GIS file formats or data stores, like ESRI shapefiles, GeoJSON files, geopackage files, P... | github_jupyter |
```
import os, json, sys, time, random
import numpy as np
import torch
from easydict import EasyDict
from math import floor
from easydict import EasyDict
from steves_utils.vanilla_train_eval_test_jig import Vanilla_Train_Eval_Test_Jig
from steves_utils.torch_utils import get_dataset_metrics, independent_accuracy_as... | github_jupyter |
## Computer Vision Learner
[`vision.learner`](/vision.learner.html#vision.learner) is the module that defines the [`cnn_learner`](/vision.learner.html#cnn_learner) method, to easily get a model suitable for transfer learning.
```
from fastai.gen_doc.nbdoc import *
from fastai.vision import *
```
## Transfer learning... | github_jupyter |
```
import pandas as pd
```
## Formas de Criar uma lista
### Usando espaço e a função split
```
data = ['1 2 3 4'.split(),
'5 6 7 8 '.split(),
'9 10 11 12'.split(),
'13 14 15 16'.split()]
data
```
## Passando int para cada elemento
### Com a função map
```
# map usa os parâmetros (function se... | github_jupyter |
# Neural networks with PyTorch
Deep learning networks tend to be massive with dozens or hundreds of layers, that's where the term "deep" comes from. You can build one of these deep networks using only weight matrices as we did in the previous notebook, but in general it's very cumbersome and difficult to implement. Py... | github_jupyter |
# GA4GH Variation Representation Schema
This notebook demonstrates the use of the VR schema to represent variation in APOE. Objects created in this notebook are saved at the end and used by other notebooks to demonstrate other features of the VR specification.
## APOE Variation
rs7... | github_jupyter |
```
import numpy as np
import cv2 as cv
import json
"""缩小图像,方便看效果
resize会损失像素，造成边缘像素模糊，不要再用于计算的原图上使用
"""
def resizeImg(src):
height, width = src.shape[:2]
size = (int(width * 0.3), int(height * 0.3))
img = cv.resize(src, size, interpolation=cv.INTER_AREA)
return img
"""找出ROI，用于分割原图
原图有四块区域，一个是地块区域，... | github_jupyter |
```
"""
Estimating the causal effect of sodium on blood pressure in a simulated example
adapted from Luque-Fernandez et al. (2018):
https://academic.oup.com/ije/article/48/2/640/5248195
"""
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
def generate_data(n=1000, seed=0, b... | github_jupyter |
# abc
```
doc_a = "Brocolli is good to eat. My brother likes to eat good brocolli, but not my mother."
doc_b = "My mother spends a lot of time driving my brother around to baseball practice."
doc_c = "Some health experts suggest that driving may cause increased tension and blood pressure."
doc_d = "I often feel pressu... | github_jupyter |
# McKinsey Data Scientist Hackathon
link: https://datahack.analyticsvidhya.com/contest/mckinsey-analytics-online-hackathon-recommendation/?utm_source=sendinblue&utm_campaign=Download_The_Dataset_McKinsey_Analytics_Online_Hackathon__Recommendation_Design_is_now_Live&utm_medium=email
slack:https://analyticsvidhya.slack... | github_jupyter |
# Trax : Ungraded Lecture Notebook
In this notebook you'll get to know about the Trax framework and learn about some of its basic building blocks.
## Background
### Why Trax and not TensorFlow or PyTorch?
TensorFlow and PyTorch are both extensive frameworks that can do almost anything in deep learning. They offer a... | github_jupyter |
<h1>Data Exploration</h1>
<p>In this notebook we will perform a broad data exploration on the <code>Hitters</code> data set. Note that the aim of this exploration is not to be completely thorough; instead we would like to gain quick insights to help develop a first prototype. Upon analyzing the output of the prototype,... | github_jupyter |
<a href="https://colab.research.google.com/github/choderalab/pinot/blob/master/scripts/adlala_mol_graph.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# import
```
! rm -rf pinot
! git clone https://github.com/choderalab/pinot.git
! pip install dg... | github_jupyter |
#### Libraries
```
%%javascript
utils.load_extension('collapsible_headings/main')
utils.load_extension('hide_input/main')
utils.load_extension('autosavetime/main')
utils.load_extension('execute_time/ExecuteTime')
utils.load_extension('code_prettify/code_prettify')
utils.load_extension('scroll_down/main')
utils.load_e... | github_jupyter |
<img src="images/Callysto_Notebook-Banner_Top_06.06.18.jpg">
```
%%html
<script src="https://cdn.geogebra.org/apps/deployggb.js"></script>
```
# Reflections of Graphs
<img src="images/cat_fight.jpg" width=960 height=640>
## Introduction
In the photo above, a kitten is looking at its reflection in a mirror.
There ... | github_jupyter |
```
from sklearn.datasets import load_iris, fetch_openml
from sklearn.preprocessing import MinMaxScaler, normalize
from sklearn.model_selection import train_test_split
from scipy.spatial.distance import minkowski, cosine
from sklearn.metrics import accuracy_score
from collections import Counter
import numpy as np
impor... | github_jupyter |
# Finding the Data
We need to install [newsapi-python](https://github.com/mattlisiv/newsapi-python) package. We can do this by entering ! in the beginning of a cell to directly access to the system terminal. Using exclamation mark is an easy way to access system terminal and install required packages as well undertake... | github_jupyter |
```
from sacred import Experiment
import tensorflow as tf
import threading
import numpy as np
import os
import Datasets
from Input import Input as Input
from Input import batchgenerators as batchgen
import Models.WGAN_Critic
import Models.Unet
import Utils
import cPickle as pickle
import Test
import pickle
dsd_train, ... | github_jupyter |
# Hyperparameter Tuning with Amazon SageMaker and MXNet
_**Creating a Hyperparameter Tuning Job for an MXNet Network**_
---
---
## Contents
1. [Background](#Background)
1. [Setup](#Setup)
1. [Data](#Data)
1. [Code](#Code)
1. [Tune](#Train)
1. [Wrap-up](#Wrap-up)
---
## Background
This example notebook focuses o... | github_jupyter |
# Data Loading Tutorial
```
cd ../..
save_path = 'data/'
from scvi.dataset import LoomDataset, CsvDataset, Dataset10X, AnnDataset
import urllib.request
import os
from scvi.dataset import BrainLargeDataset, CortexDataset, PbmcDataset, RetinaDataset, HematoDataset, CbmcDataset, BrainSmallDataset, SmfishDataset
```
## G... | github_jupyter |
<h1> Explore and create ML datasets </h1>
In this notebook, we will explore data corresponding to taxi rides in New York City to build a Machine Learning model in support of a fare-estimation tool. The idea is to suggest a likely fare to taxi riders so that they are not surprised, and so that they can protest if the c... | github_jupyter |
# Wind Statistics
### Introduction:
The data have been modified to contain some missing values, identified by NaN.
Using pandas should make this exercise
easier, in particular for the bonus question.
You should be able to perform all of these operations without using
a for loop or other looping construct.
1. The... | github_jupyter |
**[Pandas Micro-Course Home Page](https://www.kaggle.com/learn/pandas)**
---
# Introduction
Maps allow us to transform data in a `DataFrame` or `Series` one value at a time for an entire column. However, often we want to group our data, and then do something specific to the group the data is in. We do this with the `... | github_jupyter |
<a href="https://colab.research.google.com/github/jana0601/AA_Summer-school-LMMS/blob/main/Lab_Session_ToyModels.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import scipy.l... | 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>
# Indexed Expressions: Representing and manipulating tensor... | github_jupyter |
<a href="https://colab.research.google.com/github/arfild/dw_matrix/blob/master/Day5.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!pip install hyperopt
import pandas as pd
import numpy as np
import os
import datetime
import tensorflow as tf
f... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.backends.backend_tkagg
import matplotlib.pylab as plt
from astropy.io import fits
from astropy import units as units
import astropy.io.fits as pyfits
from astropy.convolution import Gaussian1DKernel, convolve
from extinction import calzetti00, apply, ccm89
fr... | github_jupyter |
Os treinamentos das redes MLP e Resnet foram feitos com um batch de 64 imagens por epoca e 10 epocas de treinamento com o dataset Cifar10
#CNN
```
'''For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. a... | github_jupyter |
```
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import Ridge
from sklearn.linear_model import Lasso
from sklearn.neighbors import KNeighborsRegressor
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
... | github_jupyter |
# Modeling and Simulation in Python
Chapter 6
Copyright 2017 Allen Downey
License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)
```
# Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an as... | github_jupyter |
```
%matplotlib inline
# Importing standard Qiskit libraries and configuring account
from qiskit import QuantumCircuit, execute, Aer, IBMQ
from qiskit.compiler import transpile, assemble
from qiskit.tools.jupyter import *
from qiskit.visualization import *
# Loading your IBM Q account(s)
provider = IBMQ.load_account()
... | github_jupyter |
# Availability Calculator
This tool estimates the average device availability over a period of time.
Double-click into the cells below, where it says `'here'`, and adjust the values as necessary.
After setting configuration values, select `Kernel` > `Restart & Run All` from the menu.
```
from datetime import dateti... | github_jupyter |
<a href="https://colab.research.google.com/github/TerrenceAm22/DS-Unit-2-Kaggle-Challenge/blob/master/LS_DS_223_assignment_checkpoint2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Lambda School Data Science
*Unit 2, Sprint 2, Module 3*
---
# C... | github_jupyter |
```
import wget, json, os, math
from pathlib import Path
from string import capwords
from pybtex.database import parse_string
import pybtex.errors
from mpcontribs.client import Client
from bravado.exception import HTTPNotFound
from pymatgen.core import Structure
from pymatgen.ext.matproj import MPRester
from tqdm.noteb... | github_jupyter |
# DeepDreaming with TensorFlow
>[Loading and displaying the model graph](#loading)
>[Naive feature visualization](#naive)
>[Multiscale image generation](#multiscale)
>[Laplacian Pyramid Gradient Normalization](#laplacian)
>[Playing with feature visualzations](#playing)
>[DeepDream](#deepdream)
This notebook demo... | 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 |
```
import sys
sys.path.append('../scripts/')
from robot import *
from scipy.stats import multivariate_normal
import random #追加
import copy
class Particle:
def __init__(self, init_pose, weight):
self.pose = init_pose
self.weight = weight
def motion_update(self, nu, omega, time, noise_... | github_jupyter |
```
# To add a new cell, type '# %%'
# To add a new markdown cell, type '# %% [markdown]'
# %% [markdown]
# ## Import necessary dependencies
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text imp... | github_jupyter |
# Sentiment Identification
## BACKGROUND
A large multinational corporation is seeking to automatically identify the sentiment that their customer base talks
about on social media. They would like to expand this capability into multiple languages. Many 3rd party tools exist for sentiment analysis, however, they need h... | github_jupyter |
# Feature Engineering
## Objective
Data preprocessing and engineering techniques generally refer to the addition, deletion, or transformation of data.
The time spent on identifying data engineering needs can be significant and requires you to spend substantial time understanding ... | github_jupyter |
```
from sklearn.cluster import KMeans
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
trueLables = pd.read_csv('bbcsport_classes.csv',delimiter=",", header=None).values
print(trueLables.shape)
terms = pd.read_csv('bbcsport_terms.csv',delimiter=",", header=None).values
print(terms.shape)
X =... | github_jupyter |
ERROR: type should be string, got "https://keras.io/examples/structured_data/structured_data_classification_from_scratch/\n\nmudar nome das coisas. Editar como quero // para de servir de exemplo pra o futuro..\n\n```\nimport tensorflow as tf\nimport numpy as np\nimport pandas as pd\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nimport pydot\nfile_url = \"http://storage.googleapis.com/download.tensorflow.org/data/heart.csv\"\ndataframe = pd.read_csv(file_url)\ndataframe.head()\nval_dataframe = dataframe.sample(frac=0.2, random_state=1337)\ntrain_dataframe = dataframe.drop(val_dataframe.index)\ndef dataframe_to_dataset(dataframe):\n dataframe = dataframe.copy()\n labels = dataframe.pop(\"target\")\n ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))\n ds = ds.shuffle(buffer_size=len(dataframe))\n return ds\n\n\ntrain_ds = dataframe_to_dataset(train_dataframe)\nval_ds = dataframe_to_dataset(val_dataframe)\n```\n\nfor x, y in train_ds.take(1):\n print(\"Input:\", x)\n print(\"Target:\", y)\n \n |||||| entender isto melhor\n\n```\ntrain_ds = train_ds.batch(32)\nval_ds = val_ds.batch(32)\nfrom tensorflow.keras.layers.experimental.preprocessing import Normalization\nfrom tensorflow.keras.layers.experimental.preprocessing import CategoryEncoding\nfrom tensorflow.keras.layers.experimental.preprocessing import StringLookup\n\n\ndef encode_numerical_feature(feature, name, dataset):\n # Create a Normalization layer for our feature\n normalizer = Normalization()\n\n # Prepare a Dataset that only yields our feature\n feature_ds = dataset.map(lambda x, y: x[name])\n feature_ds = feature_ds.map(lambda x: tf.expand_dims(x, -1))\n\n # Learn the statistics of the data\n normalizer.adapt(feature_ds)\n\n # Normalize the input feature\n encoded_feature = normalizer(feature)\n return encoded_feature\n\n\ndef encode_string_categorical_feature(feature, name, dataset):\n # Create a StringLookup layer which will turn strings into integer indices\n index = StringLookup()\n\n # Prepare a Dataset that only yields our feature\n feature_ds = dataset.map(lambda x, y: x[name])\n feature_ds = feature_ds.map(lambda x: tf.expand_dims(x, -1))\n\n # Learn the set of possible string values and assign them a fixed integer index\n index.adapt(feature_ds)\n\n # Turn the string input into integer indices\n encoded_feature = index(feature)\n\n # Create a CategoryEncoding for our integer indices\n encoder = CategoryEncoding(output_mode=\"binary\")\n\n # Prepare a dataset of indices\n feature_ds = feature_ds.map(index)\n\n # Learn the space of possible indices\n encoder.adapt(feature_ds)\n\n # Apply one-hot encoding to our indices\n encoded_feature = encoder(encoded_feature)\n return encoded_feature\n\n\ndef encode_integer_categorical_feature(feature, name, dataset):\n # Create a CategoryEncoding for our integer indices\n encoder = CategoryEncoding(output_mode=\"binary\")\n\n # Prepare a Dataset that only yields our feature\n feature_ds = dataset.map(lambda x, y: x[name])\n feature_ds = feature_ds.map(lambda x: tf.expand_dims(x, -1))\n\n # Learn the space of possible indices\n encoder.adapt(feature_ds)\n\n # Apply one-hot encoding to our indices\n encoded_feature = encoder(feature)\n return encoded_feature\n# Categorical features encoded as integers\nsex = keras.Input(shape=(1,), name=\"sex\", dtype=\"int64\")\ncp = keras.Input(shape=(1,), name=\"cp\", dtype=\"int64\")\nfbs = keras.Input(shape=(1,), name=\"fbs\", dtype=\"int64\")\nrestecg = keras.Input(shape=(1,), name=\"restecg\", dtype=\"int64\")\nexang = keras.Input(shape=(1,), name=\"exang\", dtype=\"int64\")\nca = keras.Input(shape=(1,), name=\"ca\", dtype=\"int64\")\n\n# Categorical feature encoded as string\nthal = keras.Input(shape=(1,), name=\"thal\", dtype=\"string\")\n\n# Numerical features\nage = keras.Input(shape=(1,), name=\"age\")\ntrestbps = keras.Input(shape=(1,), name=\"trestbps\")\nchol = keras.Input(shape=(1,), name=\"chol\")\nthalach = keras.Input(shape=(1,), name=\"thalach\")\noldpeak = keras.Input(shape=(1,), name=\"oldpeak\")\nslope = keras.Input(shape=(1,), name=\"slope\")\n\nall_inputs = [\n sex,\n cp,\n fbs,\n restecg,\n exang,\n ca,\n thal,\n age,\n trestbps,\n chol,\n thalach,\n oldpeak,\n slope,\n]\n\n# Integer categorical features\nsex_encoded = encode_integer_categorical_feature(sex, \"sex\", train_ds)\ncp_encoded = encode_integer_categorical_feature(cp, \"cp\", train_ds)\nfbs_encoded = encode_integer_categorical_feature(fbs, \"fbs\", train_ds)\nrestecg_encoded = encode_integer_categorical_feature(restecg, \"restecg\", train_ds)\nexang_encoded = encode_integer_categorical_feature(exang, \"exang\", train_ds)\nca_encoded = encode_integer_categorical_feature(ca, \"ca\", train_ds)\n\n# String categorical features\nthal_encoded = encode_string_categorical_feature(thal, \"thal\", train_ds)\n\n# Numerical features\nage_encoded = encode_numerical_feature(age, \"age\", train_ds)\ntrestbps_encoded = encode_numerical_feature(trestbps, \"trestbps\", train_ds)\nchol_encoded = encode_numerical_feature(chol, \"chol\", train_ds)\nthalach_encoded = encode_numerical_feature(thalach, \"thalach\", train_ds)\noldpeak_encoded = encode_numerical_feature(oldpeak, \"oldpeak\", train_ds)\nslope_encoded = encode_numerical_feature(slope, \"slope\", train_ds)\n\nall_features = layers.concatenate(\n [\n sex_encoded,\n cp_encoded,\n fbs_encoded,\n restecg_encoded,\n exang_encoded,\n slope_encoded,\n ca_encoded,\n thal_encoded,\n age_encoded,\n trestbps_encoded,\n chol_encoded,\n thalach_encoded,\n oldpeak_encoded,\n ]\n)\nx = layers.Dense(32, activation=\"relu\")(all_features)\nx = layers.Dropout(0.5)(x)\noutput = layers.Dense(1, activation=\"sigmoid\")(x)\nmodel = keras.Model(all_inputs, output)\nmodel.compile(\"adam\", \"binary_crossentropy\", metrics=[\"accuracy\"])\nmodel.fit(train_ds, epochs=50, validation_data=val_ds)\nsample = {\n \"age\": 60,\n \"sex\": 1,\n \"cp\": 1,\n \"trestbps\": 145,\n \"chol\": 233,\n \"fbs\": 1,\n \"restecg\": 2,\n \"thalach\": 150,\n \"exang\": 0,\n \"oldpeak\": 2.3,\n \"slope\": 3,\n \"ca\": 0,\n \"thal\": \"fixed\",\n}\n\ninput_dict = {name: tf.convert_to_tensor([value]) for name, value in sample.items()}\npredictions = model.predict(input_dict)\n\nprint(\n \"This particular patient had a %.1f percent probability \"\n \"of having a heart disease, as evaluated by our model.\" % (100 * predictions[0][0],)\n)\n```\n\n" | github_jupyter |
## Dependencies
```
import warnings, json, random, os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import KFold, StratifiedKFold
from sklearn.metrics import mean_squared_error
import tensorflow as tf
import tensorflow.keras.layers as L
impor... | github_jupyter |
## PyCity Schools Analysis
##### Top and Bottom Performing Schools
- The most immediate observation is that Charter schools populate the top 5 performing schools and District schools populate the bottom 5 performing schools for standardized test scores.
- Overall, District schools had lower standardized test scores t... | github_jupyter |
Greyscale ℓ1-TV Denoising
=========================
This example demonstrates the use of class [tvl1.TVL1Denoise](http://sporco.rtfd.org/en/latest/modules/sporco.admm.tvl1.html#sporco.admm.tvl1.TVL1Denoise) for removing salt & pepper noise from a greyscale image using Total Variation regularization with an ℓ1 data fid... | github_jupyter |
```
%matplotlib inline
```
# Out-of-core classification of text documents
This is an example showing how scikit-learn can be used for classification
using an out-of-core approach: learning from data that doesn't fit into main
memory. We make use of an online classifier, i.e., one that supports the
partial_fit metho... | github_jupyter |
```
import pandas as pd
import seaborn as sns
%matplotlib inline
```
Como hay gente voluntariosa, pero que no deja de ser radical, tenemos que limpiar formatos dispares de documentos estatales
```
# Carta Marina 2015
data2015 = pd.read_csv('../data/raw/escuelas-elecciones-2015-cordoba.csv')
data2015.head()
```
Sepa... | github_jupyter |
# Basic objects
A `striplog` depends on a hierarchy of objects. This notebook shows the objects and their basic functionality.
- [Lexicon](#Lexicon): A dictionary containing the words and word categories to use for rock descriptions.
- [Component](#Component): A set of attributes.
- [Interval](#Interval): One elemen... | github_jupyter |
# Import Modules
```
import warnings
warnings.filterwarnings('ignore')
from src import detect_faces, show_bboxes
from PIL import Image
import torch
from torchvision import transforms, datasets
import numpy as np
import os
```
# Path Definition
```
dataset_path = '../Dataset/emotiw/'
face_coordinates_directory = '.... | 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__}')
```
# Itraconazole, ¹³C (I=1/2) PASS
¹³C (I=1/2) 2D Phase-adjusted spinning sideband (PASS) simulation.
The following is a simulation of... | github_jupyter |
```
# !pip install plotly
import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout
from sklearn.metr... | github_jupyter |
# Lambda School Data Science - Logistic Regression
Logistic regression is the baseline for classification models, as well as a handy way to predict probabilities (since those too live in the unit interval). While relatively simple, it is also the foundation for more sophisticated classification techniques such as neur... | github_jupyter |
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