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# <font color='firebrick'><center>Idx Stats Report</center></font>
### This report provides information from the output of samtools idxstats tool. It outputs the number of mapped reads per chromosome/contig.
<br>
```
from IPython.display import display, Markdown
from IPython.display import HTML
import IPython.core.dis... | github_jupyter |
```
# Import lib
# ===========================================================
import csv
import pandas as pd
import numpy as np
import random
import time
import collections
import math
import sys
from tqdm import tqdm
from time import sleep
import matplotlib.pyplot as plt
# %matplotlib inline
plt.style.use('fivethirt... | github_jupyter |
# NumPy å
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æ¬ç« ã®ç®æšã¯ã[åååž°åæãšéååž°åæ](https://tutorials.chainer.org/ja/07_Regression_Analysis.html)ã®ç« ã§åŠãã éååž°åæãè¡ãã¢ã«ãŽãªãºã ã**NumPy ãçšããŠå®è£
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åïŒmultidimensional arrayïŒ**ã®æ±ãæ¹ãç¥ãããšã¯ãä»ã®æ§ã
ãªã©ã€ãã©ãªãå©çšããéã«åœ¹ç«ã¡ãŸãã
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ãªæ©æ¢°åŠç¿ææ³ãçµ±äžçãªã€ã³ã¿ãŒãã§ãŒã¹ã§å©çšã§ãã **s... | github_jupyter |
# HuberRegressorw with StandardScaler
This Code template is for the regression analysis using a Huber Regression and the feature rescaling technique StandardScaler in a pipeline.
### Required Packages
```
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as ... | github_jupyter |
# Signal Autoencoder
```
import numpy as np
import scipy as sp
import scipy.stats
import itertools
import logging
import matplotlib.pyplot as plt
import pandas as pd
import torch.utils.data as utils
import math
import time
import tqdm
import torch
import torch.optim as optim
import torch.nn.functional as F
from argpa... | github_jupyter |
# BetterReads: Optimizing GoodReads review data
This notebook explores how to achieve the best results with the BetterReads algorithm when using review data scraped from GoodReads. It is a short follow-up to the exploration performed in the `03_optimizing_reviews.ipynb` notebook.
We have two options when scraping rev... | github_jupyter |
# 2.18 Programming for Geoscientists class test 2016
# Test instructions
* This test contains **4** questions each of which should be answered.
* Write your program in a Python cell just under each question.
* You can write an explanation of your solution as comments in your code.
* In each case your solution program... | github_jupyter |
# OGGM flowlines: where are they?
In this notebook we show how to access the OGGM flowlines location before, during, and after a run.
Some of the code shown here will make it to the OGGM codebase [eventually](https://github.com/OGGM/oggm/issues/1111).
```
from oggm import cfg, utils, workflow, tasks, graphics
from o... | github_jupyter |
# Introduction to Machine Learning Nanodegree
## Project: Finding Donors for *CharityML*
In this project, we employ several supervised algorithms to accurately model individuals' income using data collected from the 1994 U.S. Census. The best candidate algorithm is then chosen from preliminary results and is further o... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import r2_score
from sklearn.linear_model import SGDRegressor
#
# load the data
#
df = pd.read_csv('../Datasets/synth_temp.csv')
#
# slice 1902 and forward
#
df = df.loc[df.Year > 1901]
#
# roll up by year
#
df_group_year = ... | github_jupyter |
[View in Colaboratory](https://colab.research.google.com/github/ArunkumarRamanan/Exercises-Machine-Learning-Crash-Course-Google-Developers/blob/master/validation.ipynb)
#### Copyright 2017 Google LLC.
```
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in complianc... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import os
import sys
from hawkes import hawkes, sampleHawkes, plotHawkes, iterative_sampling, extract_samples, sample_counterfactual_superposition, check_monotonicity_hawkes
sys.path.append(os.path.abspath('../'))
from sampling_utils import thinning_T
```
This not... | github_jupyter |
# Two Layer QG Model Example #
Here is a quick overview of how to use the two-layer model. See the
:py:class:`pyqg.QGModel` api documentation for further details.
First import numpy, matplotlib, and pyqg:
```
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
import pyqg
```
## Initialize an... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
```
This notebook provides a basic example of using the `blg_strain` package to calculate the magnetoelectric susceptibility for strained bilayer graphene.
# Strained Lattice
```
from blg_strain.lattice import StrainedLattice
sl = StrainedLattice(eps=0.01, thet... | github_jupyter |
# Transfer Learning
Most of the time you won't want to train a whole convolutional network yourself. Modern ConvNets training on huge datasets like ImageNet take weeks on multiple GPUs. Instead, most people use a pretrained network either as a fixed feature extractor, or as an initial network to fine tune. In this not... | github_jupyter |
```
%matplotlib inline
import sys
import os
import json
from glob import glob
from collections import defaultdict, OrderedDict
import dinopy
import yaml
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
import seaborn
import numpy
import pandas as pd
import networkx
from scipy.special impo... | github_jupyter |
# LAB 4c: Create Keras Wide and Deep model.
**Learning Objectives**
1. Set CSV Columns, label column, and column defaults
1. Make dataset of features and label from CSV files
1. Create input layers for raw features
1. Create feature columns for inputs
1. Create wide layer, deep dense hidden layers, and output layer
... | github_jupyter |
# Hive Command Note
**Outline**
* [Introduction](#intro)
* [Syntax](#syntax)
* [Reference](#refer)
---
Hive is a data warehouse infrastructure tool to process structured data in Hadoop. It resides on top of Hadoop to
summarize Big Data, and makes querying and analyzing easy.
* **Access Hive**: in cmd, type *`hive... | github_jupyter |
# Loading and working with data in sktime
Python provides a variety of useful ways to represent data, but NumPy arrays and pandas DataFrames are commonly used for data analysis. When using NumPy 2d-arrays or pandas DataFrames to analyze tabular data the rows are commony used to represent each instance (e.g. case or ob... | github_jupyter |
This notebook will show an example of text preprocessing applied to RTL-Wiki dataset.
This dataset was introduced in [1] and later recreated in [2]. You can download it in from http://139.18.2.164/mroeder/palmetto/datasets/rtl-wiki.tar.gz
--------
[1] "Reading Tea Leaves: How Humans Interpret Topic Models" (NIPS 200... | github_jupyter |
```
import pandas as pd
import numpy as np
from sklearn import metrics
from sklearn import preprocessing
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from bedrock_client.bedrock.analyzer.model_analyzer import ... | github_jupyter |
<table style="float:left; border:none">
<tr style="border:none">
<td style="border:none">
<a href="http://bokeh.pydata.org/">
<img
src="http://bokeh.pydata.org/en/latest/_static/bokeh-transparent.png"
style="width:70px"
>
</a> ... | github_jupyter |
```
import matplotlib.pyplot as plt
import numpy as np
import pymc3 as pm
import theano
from scipy.integrate import odeint
from theano import *
THEANO_FLAGS = "optimizer=fast_compile"
```
# Lotka-Volterra with manual gradients
by [Sanmitra Ghosh](https://www.mrc-bsu.cam.ac.uk/people/in-alphabetical-order/a-to-g/san... | github_jupyter |
# Quantum Teleportation
This notebook demonstrates quantum teleportation. We first use Qiskit's built-in simulators to test our quantum circuit, and then try it out on a real quantum computer.
## 1. Overview <a id='overview'></a>
Alice wants to send quantum information to Bob. Specifically, suppose she wants to send... | github_jupyter |
# Train convolutional network for sentiment analysis.
Based on
"Convolutional Neural Networks for Sentence Classification" by Yoon Kim
http://arxiv.org/pdf/1408.5882v2.pdf
For `CNN-non-static` gets to 82.1% after 61 epochs with following settings:
embedding_dim = 20
filter_sizes = (3, 4)
num_filters = 3
dr... | github_jupyter |
[](https://www.pythonista.io)
# Esquema de *OpenAPI*.
https://swagger.io/docs/specification/basic-structure/
## Estructura.
* Versión de *OpenAPI*.
* Información (```info```).
* Etiquetas (```tags```).
* Servidores (```servers```).
* Componentes (```components```).
* Esque... | github_jupyter |
# COVIDvu - US regions visualizer <img src='resources/American-flag.png' align = 'right'>
---
## Runtime prerequisites
```
%%capture --no-stderr requirementsOutput
displayRequirementsOutput = False
%pip install -r requirements.txt
from covidvu.utils import autoReloadCode; autoReloadCode()
if displayRequirementsOutp... | github_jupyter |
## Libraries
```
### Uncomment the next two lines to,
### install tensorflow_hub and tensorflow datasets
#!pip install tensorflow_hub
#!pip install tensorflow_datasets
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import tensorflow_hub as hub
import tensorflow_datasets as tfds
from tens... | github_jupyter |
```
import math
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from scipy.stats import bayes_mvs as bayesest
import os
import time
from szsimulator import Szsimulator
%matplotlib inline
mean_size = 3 # micron
doubling_time = 18 #min
tmax = 180 #min
sample_time = 2 #min
div_steps = 10
n... | github_jupyter |
# Text Data in scikit-learn
```
import matplotlib.pyplot as plt
import sklearn
sklearn.set_config(display='diagram')
from pathlib import Path
import tarfile
from urllib import request
data_path = Path("data")
extracted_path = Path("data") / "train"
imdb_path = data_path / "aclImdbmini.tar.gz"
def untar_imdb():
... | github_jupyter |
# Create a general MODFLOW model from the NHDPlus dataset
Project specific variables are imported in the model_spec.py and gen_mod_dict.py files that must be included in the notebook directory. The first first includes pathnames to data sources that will be different for each user. The second file includes a dictionar... | github_jupyter |
```
%matplotlib inline
```
Advanced: Making Dynamic Decisions and the Bi-LSTM CRF
======================================================
Dynamic versus Static Deep Learning Toolkits
--------------------------------------------
Pytorch is a *dynamic* neural network kit. Another example of a dynamic
kit is `Dynet <ht... | github_jupyter |
## Sparse logistic regression
$\newcommand{\n}[1]{\left\|#1 \right\|}$
$\newcommand{\R}{\mathbb R} $
$\newcommand{\N}{\mathbb N} $
$\newcommand{\Z}{\mathbb Z} $
$\newcommand{\lr}[1]{\left\langle #1\right\rangle}$
We want to minimize
$$\min_x J(x) := \sum_{i=1}^m \log\bigl(1+\exp (-... | github_jupyter |
# Ibis Integration (Experimental)
The [Ibis project](https://ibis-project.org/docs/) tries to bridge the gap between local Python and [various backends](https://ibis-project.org/docs/backends/index.html) including distributed systems such as Spark and Dask. The main idea is to create a pythonic interface to express SQ... | github_jupyter |
### Homework: going neural (6 pts)
We've checked out statistical approaches to language models in the last notebook. Now let's go find out what deep learning has to offer.
<img src='https://raw.githubusercontent.com/yandexdataschool/nlp_course/master/resources/expanding_mind_lm_kn_3.png' width=300px>
We're gonna use... | github_jupyter |
# One Shot Learning with Siamese Networks
This is the jupyter notebook that accompanies
## Imports
All the imports are defined here
```
%matplotlib inline
import torchvision
import torchvision.datasets as dset
import torchvision.transforms as transforms
from torch.utils.data import DataLoader,Dataset
import matplotl... | github_jupyter |
# Naive Bayes
$$ \begin{split} \mathop{argmax}_{c_k}p(y=c_k|x) &= \mathop{argmax}_{c_k}p(y=c_k)p(x|y=c_k) \\
& \left( due to: p(y=c_k|x) = \frac{p(y=c_k)p(x|y=c_k)}{p(x)} \right) \\
&= \mathop{argmax}_{c_k}p(y=c_k)\prod_jp(x^{(j)}|y=c_k) \end{split} $$
Use Maximum Likelihood Estimate(MLE) to evaluate $ p(y=c_k)$ and $ ... | github_jupyter |
# Working with Streaming Data
Learning Objectives
1. Learn how to process real-time data for ML models using Cloud Dataflow
2. Learn how to serve online predictions using real-time data
## Introduction
It can be useful to leverage real time data in a machine learning model when making a prediction. However, doing ... | github_jupyter |
# Build GAN (Generative Adversarial Networks) with PyTorch and SageMaker
### About GAN
Generative Adversarial Network (GAN) i is a generative machine learning model, which is widely used in advertising, games, entertainment, media, pharmaceuticals and other industries. It can be used to create fictional characters an... | github_jupyter |
# Quantization of Signals
*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).*
## Characteristic of a Linear Uniform Quantizer
The ch... | github_jupyter |
```
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.contrib.tensorboard.plugins import projector
# 蜜å
¥æ°æ®é
mnist = input_data.read_data_sets(r"C:\Users\zdwxx\Downloads\Compressed\MNIST_data", one_hot=True)
# è¿è¡æ¬¡æ°
max_steps = 550 * 21
# åŸçæ°é
image_num = 3000
# å®ä¹äŒè¯
ses... | github_jupyter |
# Diseño de software para cómputo cientÃfico
----
## Unidad 5: Integración con lenguajes de alto nivel con bajo nivel.
## Agenda de la Unidad 5
- JIT (Numba)
- Cython.
- Integración de Python con FORTRAN.
- **Integración de Python con C.**
## Recapitulando
- Escribimos el código Python.
- Pasamos todo a numpy.
-... | github_jupyter |
# An Introduction to Python using Jupyter Notebooks
<a id='toc'></a>
## Table of Contents:
### Introduction
* [Python programs are plain text files](#python-programs)
* [Use the Jupyter Notebook for editing and running Python](#jn-editing-python)
* [How are Jupyter Notebooks stored](#how-its-stored)
* [What you need t... | github_jupyter |
<a href="https://colab.research.google.com/github/vndee/pytorch-vi/blob/master/chatbot_tutorial.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## CHATBOT
**Tác giả**: [Matthew Inkawhich](https://github.com/MatthewInkawhich)
Trong hÆ°á»ng dẫn nà y chú... | github_jupyter |
# Tema 4.1 <a class="tocSkip">
# Imports
```
import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import graphviz
import sklearn.tree
import sklearn.neighbors
import sklearn.naive_bayes
import sklearn.svm
import sklearn.metrics
import sklearn.preprocessing
import sklearn.model_selectio... | github_jupyter |
# Super Resolution with PaddleGAN and OpenVINO
This notebook demonstrates converting the RealSR (real-world super-resolution) model from [PaddlePaddle/PaddleGAN](https://github.com/PaddlePaddle/PaddleGAN) to OpenVINO's Intermediate Representation (IR) format, and shows inference results on both the PaddleGAN and IR mo... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import importlib
import vsms
import torch
import torch.nn as nn
import clip
from vsms import *
from vsms import BoxFeedbackQuery
class StringEncoder(object):
def __init__(self):
variant ="ViT-B/32"
device='cpu'
jit = False
self.device = device
... | github_jupyter |
```
import sys, os
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import pandas_profiling as pp
sys.path.insert(0, os.path.abspath('..'))
from script.functions import *
```
#### First, we import the data and display it after passing it through the function.
```
df = load... | github_jupyter |
<a href="https://colab.research.google.com/github/wesleybeckner/technology_fundamentals/blob/main/C4%20Machine%20Learning%20II/SOLUTIONS/SOLUTION_Tech_Fun_C4_S2_Computer_Vision_Part_2_(Defect_Detection_Case_Study).ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In C... | github_jupyter |
# Data Manipulation and Plotting with `pandas`
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
```
## Learning Goals
- Load .csv files into `pandas` DataFr... | github_jupyter |
# KCWI_calcs.ipynb
functions from Busola Alabi, Apr 2018
```
from __future__ import division
import glob
import re
import os, sys
from astropy.io.fits import getheader, getdata
from astropy.wcs import WCS
import astropy.units as u
import numpy as np
from scipy import interpolate
import logging
from time import time
i... | github_jupyter |
## MIC Demo 1 - Basic steps for measurement
This simple demonstration of the MIC toolbox uses two simulated bivariate VAR(2) models from the ["Macroeconomic simulation comparison with a multivariate extension of the Markov Information Criterion"](https://www.kent.ac.uk/economics/documents/research/papers/2019/1908.pdf... | github_jupyter |
# Predicting Remaining Useful Life (advanced)
<p style="margin:30px">
<img style="display:inline; margin-right:50px" width=50% src="https://www.featuretools.com/wp-content/uploads/2017/12/FeatureLabs-Logo-Tangerine-800.png" alt="Featuretools" />
<img style="display:inline" width=15% src="https://upload.wikimedi... | github_jupyter |
```
#export
from local.torch_basics import *
from local.test import *
from local.layers import *
from local.data.all import *
from local.notebook.showdoc import show_doc
from local.optimizer import *
from local.learner import *
#default_exp callback.hook
```
# Model hooks
> Callback and helper function to add hooks i... | github_jupyter |
## Demo of 1D regression with an Attentive Neural Process with Recurrent Neural Network (ANP-RNN) model
This notebook will provide a simple and straightforward demonstration on how to utilize an Attentive Neural Process with a Recurrent Neural Network (ANP-RNN) to regress context and target points to a sine curve.
Fi... | github_jupyter |
<img src="images/usm.jpg" width="480" height="240" align="left"/>
# MAT281 - Laboratorio N°02
## Objetivos del laboratorio
* Reforzar conceptos básicos de clasificación.
## Contenidos
* [Problema 01](#p1)
<a id='p1'></a>
## I.- Problema 01
<img src="https://www.xenonstack.com/wp-content/uploads/xenonstack-credi... | github_jupyter |
# T1548.001 - Abuse Elevation Control Mechanism: Setuid and Setgid
An adversary may perform shell escapes or exploit vulnerabilities in an application with the setsuid or setgid bits to get code running in a different userâs context. On Linux or macOS, when the setuid or setgid bits are set for an application, the appl... | github_jupyter |
```
import matplotlib.pyplot as plt
x = [1, 2.1, 0.4, 8.9, 7.1, 0.1, 3, 5.1, 6.1, 3.4, 2.9, 9]
y = [1, 3.4, 0.7, 1.3, 9, 0.4, 4, 1.9, 9, 0.3, 4.0, 2.9]
plt.scatter(x,y, color='red')
w = [0.1, 0.2, 0.4, 0.8, 1.6, 2.1, 2.5, 4, 6.5, 8, 10]
z = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
plt.plot(z, w, color='lightblue', linewidt... | github_jupyter |
```
import pandas as pd
import sklearn as sk
import json
import ast
import pickle
import math
import matplotlib.pyplot as plt
df = pd.read_json('/data/accessible_POIs/great-britain-latest.json')
df.loc[:,'id'] = df['Node'].apply(lambda x: dict(x)['id'])
df.loc[:,'access'] = df['Node'].apply(lambda x: dict(x)['tags'].ge... | github_jupyter |
### Image Captioning
To perform image captioning we are going to apply an approach similar to the work described in references [1],[2], and [3]. The approach applied here uses a recurrent neural network (RNN) to train a network to generate image captions. The input to the RNN is comprised of a high-level representatio... | github_jupyter |
#### New to Plotly?
Plotly's Python library is free and open source! [Get started](https://plotly.com/python/getting-started/) by downloading the client and [reading the primer](https://plotly.com/python/getting-started/).
<br>You can set up Plotly to work in [online](https://plotly.com/python/getting-started/#initiali... | github_jupyter |
# Process an interferogram with ASF HyP3
https://hyp3-docs.asf.alaska.edu/using/sdk/
## Search for scenes
scenes over grand mesa, colorado using https://asf.alaska.edu/api/
```
import requests
import shapely.geometry
roi = shapely.geometry.box(-108.3,39.2,-107.8,38.8)
polygonWKT = roi.wkt
baseurl = "https://api.d... | github_jupyter |
# Activity #1: MarketMap
* another way to visualize mappable data
## 1.a : explore the dataset
```
# our usual stuff
%matplotlib inline
import pandas as pd
import numpy as np
#!pip install xlrd # JPN, might have to run this
# note: this is quering from the web! How neat is that??
df = pd.read_excel('https://query.d... | github_jupyter |
```
import sys
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0" #for training on gpu
from scipy import signal
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
import pickle
import time
from random import shuffle
from tensorflow import keras
from tensorflow.keras.utils import to_categorical
fr... | github_jupyter |
# 蜬眮å·ç§¯
:label:`sec_transposed_conv`
å°ç®å䞺æ¢ïŒæ们æè§å°çå·ç§¯ç¥ç»çœç»å±ïŒäŸåŠå·ç§¯å±ïŒ :numref:`sec_conv_layer`ïŒåæ±èå±ïŒ :numref:`sec_pooling`ïŒïŒéåžžäŒåå°äžéæ ·èŸå
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äŸåŠïŒèŸåºåçŽ æå€çéé绎å¯ä»¥ä¿æèŸå
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¶æ¯åšç©ºéŽç»ŽåºŠè¢«å·ç§¯ç¥ç»çœç»å±çŒ©å°åïŒæ们å¯ä»¥äœ¿çšåŠäžç§ç±»åçå·ç§¯ç¥ç»çœç»å±ïŒå®å¯ä»¥å¢å äžéæ ·äžéŽå±ç¹åŸåŸç空éŽç»ŽåºŠã
åšæ¬èäžïŒæ们å°ä»ç»
*蜬眮å·ç§¯*ïŒtransposed convol... | github_jupyter |
##### Copyright 2020 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 |
### Strings - Quotation Marks
```
# Quotation marks must be matching. Both of the following work.
good_string = "Hello, how are you?"
another_good_string = 'Hello, how are you?'
# These strings will not work
bad_string = 'Don't do that'
another_bad_string = "Don't do that'
# Notice you enclose the whole sentence in do... | github_jupyter |
```
import copy
if __name__ == '__main__':
%run Tests.ipynb
%run MoleculeGenerator2.ipynb
%run Discrim.ipynb
%run Rewards.ipynb
%run PPO_WITH_TRICKS.ipynb
%run ChemEnv.ipynb
%run SupervisedPreTraining.ipynb
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# wants: a... | github_jupyter |
Lambda School Data Science, Unit 2: Predictive Modeling
# Applied Modeling, Module 3
### Objective
- Visualize and interpret partial dependence plots
### Links
- [Kaggle / Dan Becker: Machine Learning Explainability â Partial Dependence Plots](https://www.kaggle.com/dansbecker/partial-plots)
- [Christoph Molnar: Int... | github_jupyter |
# Obtaining movie data, API-testing
```
# open questions:
# API only allows 1k requests per day..
# initial load (static database) or load on request, maybe another API required then?
# regular updates?
import requests
import pandas as pd
```
# get imdb ids
```
# uses links.csv, a list of random imdbIds from https:... | github_jupyter |
# MLP ORF to GenCode
Use GenCode 38 and length-restricted data.
Use model pre-trained on Simulated ORF.
```
import time
def show_time():
t = time.time()
print(time.strftime('%Y-%m-%d %H:%M:%S %Z', time.localtime(t)))
show_time()
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sk... | github_jupyter |
# Step 1: Data gathering
__Step goal__: Download and store the datasets used in this study.
__Step overview__:
1. London demographic data;
2. London shape files;
3. Counts data;
4. Metro stations and lines.
#### Introduction
All data is __open access__ and can be found on the official websites. Note, that the data ... | github_jupyter |
<h1 align="center">Exploratory Analysis : Game of Thrones</h1>
One of the most popular television series of all time, Game of Thrones is a fantasy drama set in fictional continents of Westeros and Essos filled with multi... | github_jupyter |
# HyperEuler on MNIST-trained Neural ODEs
```
import sys ; sys.path.append('..')
from torchdyn.models import *; from torchdyn import *
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
import pytorch_lightning as pl
from pytorch_lightning.loggers i... | github_jupyter |
```
import numpy as np
import tensorflow as tf
import random as rn
import os
import matplotlib.pyplot as plt
%matplotlib inline
os.environ['PYTHONHASHSEED'] = '0'
np.random.seed(1)
rn.seed(1)
from keras import backend as K
tf.compat.v1.set_random_seed(1)
#sess = tf.Session(graph=tf.get_default_graph())
#K.set_session(s... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Modeling" data-toc-modified-id="Modeling-1"><span class="toc-item-num">1 </span>Modeling</a></span><ul class="toc-item"><li><span><a href="#Victims" data-toc-modified-id="Victims-1.1"><span class... | github_jupyter |
```
# evaluate RFE for classification
import numpy as np
from sklearn.datasets import make_classification
from sklearn.model_selection import cross_val_score, RepeatedStratifiedKFold, RepeatedKFold
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeClassifier
from sklearn.pipeline import Pip... | github_jupyter |
# [Day 8](https://www.hackerrank.com/challenges/30-dictionaries-and-maps/problem)
```
{'1':'a'}.update({'2':'c'})
d = {}
for i in range(int(input())):
x = input().split()
d[x[0]] = x[1]
while True:
try:
name = input()
if name in d:
print(name, "=", d[name], sep="")
else:... | github_jupyter |
# Introduction to XGBoost Spark with GPU
The goal of this notebook is to show how to train a XGBoost Model with Spark RAPIDS XGBoost library on GPUs. The dataset used with this notebook is derived from Fannie Maeâs Single-Family Loan Performance Data with all rights reserved by Fannie Mae. This processed dataset is re... | github_jupyter |
# Exp 41 analysis
See `./informercial/Makefile` for experimental
details.
```
import os
import numpy as np
from IPython.display import Image
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import seaborn as sns
sns.set_style('ticks')
matplotlib.r... | github_jupyter |
# DOPPELGANGER #
## Ever wondered how your "doppelganger" dog would look like?
# EXPERIMENT LOCALLY
### Prepare Environment
Install and import needed modules.
```
import numpy as np
import pandas as pd
import os
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications.x... | github_jupyter |
## Exercise 3.10 Taxicab (tramcar) problem
Suppose you arrive in a new city and see a taxi numbered 100. How many taxis are there in this city? Let us assume taxis are numbered sequentially as integers starting from 0, up to some unknown upper bound $\theta$. (We number taxis from 0 for simplicity; we can also count fr... | github_jupyter |
## Nearest Neighbor item based Collaborative Filtering
Source: https://towardsdatascience.com
```
##Dataset url: https://grouplens.org/datasets/movielens/latest/
import pandas as pd
import numpy as np
r_cols = ['user_id','movie_id','rating'... | github_jupyter |
# PaddleOCR DJL example
In this tutorial, we will be using pretrained PaddlePaddle model from [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) to do Optical character recognition (OCR) from the given image. There are three models involved in this tutorial:
- Word detection model: used to detect the word block f... | github_jupyter |
# FÃsica de partÃculas ... com R e tidyverse
Esse tutorial utiliza os dados abertos do experimento CMS do LHC [CMS Open Data](http://opendata.cern.ch/about/cms) DisponÃveis no site [CERN Open Data portal](http://opendata.cern.ch).
Para rodar esse tutorial offline, vide o arquivo [README](https://github.com/cms-open... | github_jupyter |
æç« æ¥èª äœè
ïŒåå® æŽå€æºåšåŠä¹ ç¬è®°è®¿é®[è¿é](https://github.com/zlotus/notes-LSJU-machine-learning)
# 第åäºè®²ïŒPCAçå¥åŒåŒå解ãç¬ç«æååæ
åé¡Ÿäžäžäžäžè®²çå
容ââPCAç®æ³ïŒäž»èŠæäžäžªæ¥éª€ïŒ
1. å°æ°æ®æ£è§å䞺é¶ææ以ååäœåæ¹å·®ïŒ
2. 计ç®åæ¹å·®ç©éµ$\displaystyle\varSigma=\frac{1}{m}x^{(i)}\left(x^{(i)}\right)^T$ïŒ
3. æŸå°$\varSigma$çå$k$䞪ç¹åŸåéã
åšäžäžè®²çæåïŒæ们è¿ä»ç»äºPCAåšé¢éšè¯å«äžçåºçšãè¯æ³äžäžïŒåšé¢éšè¯å«äž$x^{(i)}\in\mathbb R... | github_jupyter |
<h1 align="center">SimpleITK Spatial Transformations</h1>
**Summary:**
1. Points are represented by vector-like data types: Tuple, Numpy array, List.
2. Matrices are represented by vector-like data types in row major order.
3. Default transformation initialization as the identity transform.
4. Angles specified in ra... | github_jupyter |
```
##### derived from https://github.com/bozhu/AES-Python
import copy
Sbox = (
0x63, 0x7C, 0x77, 0x7B, 0xF2, 0x6B, 0x6F, 0xC5, 0x30, 0x01, 0x67, 0x2B, 0xFE, 0xD7, 0xAB, 0x76,
0xCA, 0x82, 0xC9, 0x7D, 0xFA, 0x59, 0x47, 0xF0, 0xAD, 0xD4, 0xA2, 0xAF, 0x9C, 0xA4, 0x72, 0xC0,
0xB7, 0xFD, 0x93, 0x26, 0x36, 0x3F,... | github_jupyter |
# CME 193 - Lecture 8
Here's what you've seen over the past 7 lectures:
* Python Language Basics
* NumPy - Arrays/Linear Algebra
* SciPy - Sparse Linear Algebra/Optimization
* DataFrames - Reading & Maniputlating tabular data
* Scikit learn - Machine Learning Models & use with data
* Ortools - More Optimization
You'v... | github_jupyter |
```
import xarray as xr
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seawater as sw
import cartopy.crs as ccrs # import projections
import cartopy.feature as cf # import features
fig_dir='C:/Users/gentemann/Google Drive/f_drive/docs/projects/misst-arct... | github_jupyter |
# Understanding Data Actions
blocktorch streamlines the creation and implementation of machine learning models for tabular data. One of the many features it offers is [data checks](https://blocktorch.alteryx.com/en/stable/user_guide/data_checks.html), which are geared towards determining the health of the data before ... | github_jupyter |
```
import numpy as np
from bokeh.plotting import figure, show, output_notebook
from bokeh.layouts import gridplot
output_notebook()
N = 9
x = np.linspace(-2, 2, N)
y = x**2
sizes = np.linspace(10, 20, N)
xpts = np.array([-.09, -.12, .0, .12, .09])
ypts = np.array([-.1, .02, .1, .02, -.1])
figures = []
p = figure(title... | 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 |
<div class="alert alert-block alert-info">
<font size="5"><b><center> Section 5</font></center>
<br>
<font size="5"><b><center>Recurrent Neural Network in PyTorch with an Introduction to Natural Language Processing</font></center>
</div>
Credit: This example is obtained from the following book:
Subramanian, Vishnu. 2... | github_jupyter |
```
import numpy as np
import pandas as pd
import holoviews as hv
import networkx as nx
from holoviews import opts
hv.extension('bokeh')
defaults = dict(width=400, height=400)
hv.opts.defaults(
opts.EdgePaths(**defaults), opts.Graph(**defaults), opts.Nodes(**defaults))
```
Visualizing and working with network gr... | github_jupyter |
# Data Analysis of Bitcoin and Where it is Heading
# Graphing the whole Graph
```
#### Importing Pandas and others and Reading csv file
import os
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import pandas as pd
import plotly.express as px
##Remodified .CSV data to make managing data easie... | github_jupyter |
<a href="https://colab.research.google.com/github/google/jax-md/blob/main/notebooks/athermal_linear_elasticity.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
#@title Imports and utility code
!pip install jax-md
import numpy as onp
import jax.... | github_jupyter |
```
# noexport
import os
os.system('export_notebook identify_domain_training_data.ipynb')
from tmilib import *
import csv
import sys
num_prev_enabled = int(sys.argv[1])
num_labels_enabled = 2 + num_prev_enabled
data_version = 4 + num_prev_enabled
print 'num_prev_enabled', num_prev_enabled
print 'data_version', data_ve... | github_jupyter |
# Module 3 Required Coding Activity
Introduction to Python (Unit 2) Fundamentals
All course .ipynb Jupyter Notebooks are available from the project files download topic in Module 1, Section 1.
This is an activity from the Jupyter Notebook **`Practice_MOD03_IntroPy.ipynb`** which you may have already completed.
... | github_jupyter |
<a href="https://colab.research.google.com/github/Tessellate-Imaging/monk_v1/blob/master/study_roadmaps/1_getting_started_roadmap/2_elemental_features_of_monk/5)%20Feature%20-%20Switch%20modes%20without%20reloading%20experiment%20-%20train%2C%20eval%2C%20infer.ipynb" target="_parent"><img src="https://colab.research.go... | github_jupyter |
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