text stringlengths 2.5k 6.39M | kind stringclasses 3
values |
|---|---|
https://networkx.github.io/
sources https://github.com/networkx
## Creating a graph
Create an empty graph with no nodes and no edges.
```
import networkx as nx
G = nx.Graph()
```
By definition, a `Graph` is a collection of nodes (vertices) along with identified pairs of nodes (called edg... | github_jupyter |
# Stellar Initial Mass Function (IMF)
We are going to use a Salpeter IMF to generate stellar IMF data and then use MCMC to guess the slope.
The Salpeter IMF is given by:
$\frac{dN}{dM} \propto \frac{M}{M_\odot}^{-\alpha} ~~ or ~~ \frac{dN}{dlogM} \propto \frac{M}{M_\odot}^{1-\alpha}$
```
import numpy as np
import mat... | github_jupyter |
```
import scipy.io as sio
import numpy as np
import math
import matplotlib.pyplot as plt
from scipy.sparse import csc_matrix
data = sio.loadmat('XwindowsDocData.mat')
xtrain = data['xtrain']; xtest = data['xtest']
ytrain = data['ytrain']; ytest = data['ytest']
vocab = data['vocab']
```
### Naive Bayes classifiers
- T... | github_jupyter |
You can read an overview of this Numerical Linear Algebra course in [this blog post](http://www.fast.ai/2017/07/17/num-lin-alg/). The course was originally taught in the [University of San Francisco MS in Analytics](https://www.usfca.edu/arts-sciences/graduate-programs/analytics) graduate program. Course lecture vide... | github_jupyter |
<div style="width:1000 px">
<div style="float:right; width:98 px; height:98px;">
<img src="https://raw.githubusercontent.com/Unidata/MetPy/master/src/metpy/plots/_static/unidata_150x150.png" alt="Unidata Logo" style="height: 98px;">
</div>
<h1>XArray Introduction</h1>
<h3>Unidata Python Workshop</h3>
<div style="cle... | github_jupyter |
**Note**: Click on "*Kernel*" > "*Restart Kernel and Run All*" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img heigh... | github_jupyter |
# Analitiche con Pandas
## [Scarica zip esercizi](../_static/generated/pandas.zip)
[Naviga file online](https://github.com/DavidLeoni/softpython-it/tree/master/pandas)
## 1. Introduzione
Python mette a disposizione degli strumenti potenti per l'analisi dei dati - uno dei principali è [Pandas](https://pandas.pydata... | 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 |
# Parameter identification example
Here is a simple toy model that we use to demonstrate the working of the inference package
$\emptyset \xrightarrow[]{k_1} X \; \; \; \; X \xrightarrow[]{d_1} \emptyset$
```
%matplotlib inline
%config InlineBackend.figure_format = "retina"
from matplotlib import rcParams
rcParams["... | github_jupyter |
<a href="https://colab.research.google.com/github/Aman211409/HackFest-21/blob/ML/tflite_conversion.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
impor... | github_jupyter |
# Code Reference
https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html
https://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html
```
import nibabel as nib
import numpy as np
import pandas as pd
import os
import torch
import torch.nn as nn
import torch.nn.functiona... | github_jupyter |
```
#default_exp richext
```
# Extensions To rich
> Extensions to [rich](https://github.com/willmcgugan/rich) for ghtop.
```
#export
import time,random
from collections import defaultdict
from typing import List
from collections import deque, OrderedDict, namedtuple
from ghtop.all_rich import (Console, Color, FixedP... | github_jupyter |
```
# default_exp sources
from nbdev import *
%load_ext autoreload
%autoreload 2
from utilities.ipynb_docgen import *
from nbdev.showdoc import show_doc
```
# Sources and weights
> Define the PointSource class, code to load weight tables to combine with photon data
### Weight tables
We use a full-sky catalog analys... | github_jupyter |
Using a neural network to predict stock prices, using only basic data
```
%matplotlib inline
from matplotlib import pyplot as plt
import datetime
import pandas_datareader.data as web
import pandas as pd
import numpy as np
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.cross_valid... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Image/convolutions.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_blank" href="htt... | github_jupyter |
# NWB use-case pvc-7
--- Data courtesy of Aleena Garner, Allen Institute for Brain Sciences ---
Here we demonstrate how data from the NWB pvc-7 use-case can be stored in NIX files.
### Context:
- *In vivo* calcium imaging of layer 4 cells in mouse primary visual cortex.
- Two-photon images sampled @ 30 Hz
- Visual s... | github_jupyter |
# Processing Dirty Data
## Background
This is fake data generated to demonstrate the capabilities of `pyjanitor`. It contains a bunch of common problems that we regularly encounter when working with data. Let's go fix it!
### Load Packages
Importing `pyjanitor` is all that's needed to give Pandas Dataframes extr... | github_jupyter |
### Imports
```
import importlib
from AIBind.import_modules import *
from AIBind import AIBind
importlib.reload(AIBind)
```
### GPU Settings
```
str(subprocess.check_output('nvidia-smi', shell = True)).split('\\n')
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
```
### VAENet
#### Read In Test Datasets
```
targets_test... | github_jupyter |
<p align="center">
<img width="100%" src="../../../multimedia/mindstorms_51515_logo.png">
</p>
# `my_favorite_color`
Python equivalent of the `My favorite color` program. Makes Charlie react differently depending on the color we show him.
# Required robot
* Charlie (with color sensor and color palette)
<img src=".... | github_jupyter |
# Assignment 3: Question Answering
Welcome to this week's assignment of course 4. In this you will explore question answering. You will implement the "Text to Text Transfer from Transformers" (better known as T5). Since you implemented transformers from scratch last week you will now be able to use them.
<img src = ... | github_jupyter |
# Za enunt
use this ! https://inloop.github.io/sqlite-viewer/
---
## Za recapitulare
SOCKETS
```
#!/usr/bin/python # This is server.py file
import socket # Import socket module
s = socket.socket() # Create a socket object
host = socket.gethostname() # Get local machine name
port =... | github_jupyter |
# Setting a Meaningful Index
The index of a DataFrame provides a label for each of the rows. If not explicitly provided, pandas uses the sequence of consecutive integers beginning at 0 as the index. In this chapter, we learn how to set one of the columns of the DataFrame as the new index so that it provides a more mea... | github_jupyter |
```
import os
import pandas as pd
import random
import shutil
import librosa
import numpy as np
import soundfile as sf
# Freesound dataset
# negative data로 삼을 소리의 유형을 고릅니다.
# 여기서는 가정에서 날 법한 소리 위주로 선정했습니다.
OtherSound_class = ['Cat', 'Bell', 'Applause', 'Bark', 'Computer_keyboard', 'Clock', 'Cellphone_buzz_and_vibrating... | github_jupyter |
# Pytorch Example: Neural Network with Categorical Embeddings
In this example we will demonstrate conversion of a Pytorch model that takes numeric and categorical inputs separately, and estimate the treatment effect using some simulated historical data. The model input data is sourced from this kaggle competition: htt... | github_jupyter |
# Modeling and Simulation in Python
Chapter 23
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 a... | github_jupyter |
# Predicting Star Temperature with Elastic Net Linear Regression
Using the Open Exoplanet Catalogue database: https://github.com/OpenExoplanetCatalogue/open_exoplanet_catalogue/
## Data License
Copyright (C) 2012 Hanno Rein
Permission is hereby granted, free of charge, to any person obtaining a copy of this database ... | github_jupyter |
# Visualizing Catalytic Potentials of Glycolytic Regulatory Kinases
This notebook provides two examples on creating more sophisticated figures using MASSpy. Specifically, the two examples will reproduce the following from the publication by <cite data-cite="YAHP18">Yurkovich et al., 2018</cite>
- Example **A**: Reprod... | github_jupyter |
```
#export
from nbexp_personal import sendEmail
```
## utils
```
#export
def itemgetter(*args):
g = operator.itemgetter(*args)
def f(*args2):
return dict(zip(args, g(*args2)))
return f
#export
def write_json(filename, content):
with open(filename, 'w', encoding='UTF-8') as f:
json.du... | github_jupyter |
# Bayesian Parametric Survival Analysis with PyMC3
```
%matplotlib inline
from matplotlib import pyplot as plt
from matplotlib.ticker import StrMethodFormatter
import numpy as np
import pymc3 as pm
import scipy as sp
import seaborn as sns
from statsmodels import datasets
from theano import shared, tensor as tt
plt.s... | github_jupyter |
# Transposed Convolution
:label:`sec_transposed_conv`
The CNN layers we have seen so far,
such as convolutional layers (:numref:`sec_conv_layer`) and pooling layers (:numref:`sec_pooling`),
typically reduce (downsample) the spatial dimensions (height and width) of the input,
or keep them unchanged.
In semantic segment... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/FeatureCollection/select_by_location.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="... | github_jupyter |
# Calculate molecular descriptors by graph neural net
```
%matplotlib inline
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
from rdkit import Chem
import numpy as np
#load database
path="../database/small_db.csv"
df=pd.read_csv(path)
df
#s... | github_jupyter |
```
import cuml
import cudf
import nvcategory
import xgboost as xgb
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error, roc_auc_score
#Read in the data. Notice how it decompresses as it reads the data into memory.
gdf = cudf.read_csv('/rapids/Data/black-friday.zip')
#Taking a look a... | github_jupyter |
# COVID-19 and ACE inhibitors
**This example does not use actual COVID-19 data and does not offer medical advice.**
This notebook shows how to test whether ACI inhibitors explain higher mortality from COVID-19 among hypertensive patients.
Suppose we have a machine learning model which predicts COVID-19 mortality bas... | github_jupyter |
### Lyrics model.
This notebook contains code for training the lyrics models, which is a built using
a pretrained BERT model.
```
import pandas as pd
import numpy as np
import torch
from tqdm.notebook import tqdm
from transformers import BertTokenizer, BertForSequenceClassification, AdamW, get_linear_schedule_with_... | github_jupyter |
```
cd ..
# %matplotlib notebook
# %matplotlib inline
#import mpld3
#mpld3.enable_notebook()
import StateModeling as stm
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from Corona.LoadData import loadData, preprocessData
from Corona.CoronaModel import CoronaModel, plotTotalCases
from bokeh.... | github_jupyter |
# The Matrix Profile
## Laying the Foundation
At its core, the STUMPY library efficiently computes something called a <i><b>matrix profile</b>, a vector that stores the [z-normalized Euclidean distance](https://youtu.be/LnQneYvg84M?t=374) between any subsequence within a time series and its nearest neighbor</i>.
To ... | github_jupyter |
# Predict comparison
```
import vowpalwabbit
def my_predict(vw, ex):
pp = 0.0
for f, v in ex.iter_features():
pp += vw.get_weight(f) * v
return pp
def ensure_close(a, b, eps=1e-6):
if abs(a - b) > eps:
raise Exception(
"test failed: expected "
+ str(a)
... | github_jupyter |
# Analysis of model results
To do:
* write labels to geotiffs to dir data/test/predict_process or so
* implement masks for selecting no_img pixels
```
%load_ext autoreload
%autoreload 2
import sys
sys.path.append("/mnt/hd_internal/hh/projects_DS/road_detection/roaddetection/")
import numpy as np
from keras.models... | github_jupyter |
This Notebook creates the PNG surface figures for the sequential KL blueprint
```
import numpy as np
import nibabel as nib
import scipy.io as sio
from scipy import stats
import pandas as pd
import h5py
import nilearn
import plotly
from nilearn import plotting
import seaborn as sn
from math import pi
import matplotlib ... | github_jupyter |
[source](../../api/alibi_detect.od.llr.rst)
# Likelihood Ratios for Outlier Detection
## Overview
The outlier detector described by [Ren et al. (2019)](https://arxiv.org/abs/1906.02845) in [Likelihood Ratios for Out-of-Distribution Detection](https://arxiv.org/abs/1906.02845) uses the likelihood ratio (LLR) between ... | github_jupyter |
```
!pip --quiet install transformers
!pip --quiet install tokenizers
from google.colab import drive
drive.mount('/content/drive')
!cp -r '/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Scripts/.' .
COLAB_BASE_PATH = '/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/'
MODEL_BASE_PA... | github_jupyter |
## Requesting Data from Application Programming Interfaces (API's)
This notebook demonstrates the fundamentals of interacting with a web-hosted API for the sake of data retrieval. Much of this functionality is made available through the **requests** library which should have already been installed on your machine as pa... | github_jupyter |
# Value iteration
This assignment is taken from awesome [__CS294__](http://rll.berkeley.edu/deeprlcourse/) as is. All credit goes to them.
## Introduction
This assignment will review the two classic methods for solving Markov Decision Processes (MDPs) with finite state and action spaces.
We will implement value iter... | github_jupyter |
# MOSDBDiscrete
In this module, we will have a brief overview of the `MOSDBDiscrete` class, which manages a transistor characterization database and provide methods for designers to query transistor small signal parameters.
## MOSDBDiscrete example
To use the transistor characterization database, evaluate the followin... | github_jupyter |
# Initiation à la Programmation Orientée Objet
> Cours NSI Terminale - Thème 1.
- toc: true
- badges: true
- comments: false
- categories: [python, NSI, Terminale, Structure_donnees, POO, TP]
- image: images/nsi1.png
## Introduction
Objets et POO sont au centre de la manière Python fonctionne. Vous n'êtes pas obli... | github_jupyter |
##### Copyright 2018 The TF-Agents Authors.
### Get Started
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://colab.research.google.com/github/tensorflow/agents/blob/master/tf_agents/colabs/4_drivers_tutorial.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib
font = {'family': 'normal',
'weight': 'bold',
'size': 15}
matplotlib.rc('font', **font)
plt.rcParams["figure.figsize"] = [15, 7]
from os.path import expanduser
SRC_PATH = expanduser("~") + '/SageMaker/mastering-ml-on-aws/chapter... | github_jupyter |
# MMTL Basics Tutorial
The purpose of this tutorial is to introduce the basic classes and flow of the MMTL package within Snorkel MeTaL (not necessarily to motivate or explain multi-task learning at large; we assume prior experience with MTL). For a broader understanding of the general Snorkel pipeline and Snorkel MeT... | github_jupyter |
<div id="qe-notebook-header" align="right" style="text-align:right;">
<a href="https://quantecon.org/" title="quantecon.org">
<img style="width:250px;display:inline;" width="250px" src="https://assets.quantecon.org/img/qe-menubar-logo.svg" alt="QuantEcon">
</a>
</div>
# Wealth Distribut... | github_jupyter |
[](https://gishub.org/leafmap-pangeo)
Uncomment the following line to install [leafmap](https://leafmap.org) if needed.
```
# !pip install leafmap
import leafmap.kepler as leafmap
```
If you are using a recently implemented leafmap feature that has not yet been releas... | github_jupyter |
<a id='pd'></a>
<div id="qe-notebook-header" align="right" style="text-align:right;">
<a href="https://quantecon.org/" title="quantecon.org">
<img style="width:250px;display:inline;" width="250px" src="https://assets.quantecon.org/img/qe-menubar-logo.svg" alt="QuantEcon">
</a>
</div>
#... | github_jupyter |
# Assignment 1: Neural Machine Translation
Welcome to the first assignment of Course 4. Here, you will build an English-to-German neural machine translation (NMT) model using Long Short-Term Memory (LSTM) networks with attention. Machine translation is an important task in natural language processing and could be ... | github_jupyter |
# Now You Code 1: Data Analysis of Movie Goers
In this assignment you will perform a data analysis of people who go to the movies.
A movie theatre chain asked movie goers to fill out a quick survey in exchange for a 1/2 price ticket. The survey asked for basic demographics: age, gender, occupation and zip code. This... | github_jupyter |
# Using Astropy Quantities and Units for astrophysical calculations
## Authors
Ana Bonaca, Erik Tollerud, Jonathan Foster, Lia Corrales, Kris Stern, Stephanie T. Douglas
## Learning Goals
* Use `Quantity` objects to estimate a hypothetical galaxy's mass
* Take advantage of constants in the `astropy.constants` library... | github_jupyter |
# 函数
- 函数可以用来定义可重复代码，组织和简化
- 一般来说一个函数在实际开发中为一个小功能
- 一个类为一个大功能
- 同样函数的长度不要超过一屏
Python中的所有函数实际上都是有返回值(return None),
如果你没有设置return,那么Python将不显示None.
如果你设置return,那么将返回出return这个值.
```
def HJN():
print('Hello')
return 1000
b=HJN()
print(b)
HJN
def panduan(number):
if number % 2 == 0:
print('O')
e... | github_jupyter |
# Reading and writing files, JSON
## Contents:
* File Input/Output
* Reading and writing JSON
## File Input/Output
A huge portion of our input data will come from files that we have stored on our computer (on the file system). A lot of analysis of these files is done in memory in Python, when working with them. We ... | github_jupyter |
# Optimizing mining operations
This tutorial includes everything you need to set up IBM Decision Optimization CPLEX Modeling for Python (DOcplex), build a Mathematical Programming model, and get its solution by solving the model on Cloud with IBM ILOG CPLEX Optimizer.
When you finish this tutorial, you'll have a foun... | github_jupyter |
<a href="http://cocl.us/pytorch_link_top">
<img src="https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0110EN/notebook_images%20/Pytochtop.png" width="750" alt="IBM Product " />
</a>
<img src="https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0110EN... | github_jupyter |
# Multi-task recommenders
**Learning Objectives**
1. Training a model which focuses on ratings.
2. Training a model which focuses on retrieval.
3. Training a joint model that assigns positive weights to both ratings & retrieval models.
## Introduction
In the basic retrieval notebook we built a retrieval system ... | github_jupyter |
# Equilibrium analysis Chemical reaction
Number (code) of assignment: 2N4
Description of activity: H2 & H3
Report on behalf of:
name : Pieter van Halem
student number (4597591)
name : Dennis Dane
student number (4592239)
Data of student taking the role of contact person:
name : Pieter van Halem
email address... | github_jupyter |
# Gaussian Process Distribution of Relaxation Times
## In this tutorial we will reproduce Figure 7 of the article https://doi.org/10.1016/j.electacta.2019.135316
GP-DRT is our newly developed approach that can be used to obtain both the mean and covariance of the DRT from the EIS data by assuming that the DRT is a Ga... | github_jupyter |
```
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow import keras
from tensorflow.keras i... | github_jupyter |
```
#Load libraries
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import pandas as pd
from pandas import read_csv
from pandas import set_option
from matplotlib import pyplot
from pandas import read_csv
from pandas import set_option
from matplotlib import pyplot as plt
import seaborn
HOME_PATH = '... | github_jupyter |
```
import os
os.getcwd()
%cd ..
from pathlib import Path
from mimic.utils.experiment import MimicExperiment
from mimic.utils.filehandling import set_paths
from mimic.utils import plot
from mimic.utils.text import tensor_to_text
import json
import torch
from PIL import ImageFont
try:
font = ImageFont.truetype('Fr... | 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=Mathematics/StatisticsProject/statistic... | github_jupyter |
# 課程重點:
利用Keras 建立神經網路模型
查看優化器的結果
# 範例目標:
使用CIFAR-10圖庫, 看看完整神經網路
```
from __future__ import print_function
import keras
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Ac... | github_jupyter |
# The data block API
```
from fastai.gen_doc.nbdoc import *
from fastai.tabular import *
from fastai.text import *
from fastai.vision import *
np.random.seed(42)
```
The data block API lets you customize the creation of a [`DataBunch`](/basic_data.html#DataBunch) by isolating the underlying parts of that process in ... | github_jupyter |
<!--BOOK_INFORMATION-->
<img align="left" style="padding-right:10px;" src="figures/PDSH-cover-small.png">
*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/Pytho... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive')
DATA_DIR='/content/drive/MyDrive/BIR_Workshop/model_mesh'
!pip install livelossplot --quiet
from google.colab import drive
import os
import matplotlib.pyplot as plt
import pandas as pd
import torch
import numpy as np
from sklearn.model_selection import tr... | github_jupyter |
# Demo_Chris
> Pure markup, demonstrate clustering work for detecting convoys.
As we investigated the AIS ship tracking data, we became interested in automatically detecting emergent behavior from groups of ships. For instance: can we automatically detect container ships following a shipping lane? can we find groups o... | github_jupyter |
# College Data Clustering
> Learn about K-means clustering model.
- toc: true
- badges: true
- comments: true
- categories: [clustering]
- image: images/collegeData.png
___
When using the Kmeans algorithm under normal circumstances, it is because you don't have labels. In this case we will use the labels to try to... | github_jupyter |
```
from pyrep import PyRep
import numpy as np
from matplotlib import pyplot as plt
from pyrep.objects.shape import Shape
from pyrep.const import PrimitiveShape
from pyrep.objects.vision_sensor import VisionSensor
from IPython import display
f = open("soma_cube.txt", "r")
text = f.read()
split_sols = text.split('soluti... | github_jupyter |
# 10장. 회귀 분석으로 연속적 타깃 변수 예측하기
**아래 링크를 통해 이 노트북을 주피터 노트북 뷰어(nbviewer.jupyter.org)로 보거나 구글 코랩(colab.research.google.com)에서 실행할 수 있습니다.**
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://nbviewer.jupyter.org/github/rickiepark/python-machine-learning-book-2nd-edition/blob/mas... | github_jupyter |
# Asking the right questions
### Business Task: *How can we use trends in smart device usage to produce actionable insights that guide Bellabeat's marketing efforts?*
#### About BellaBeat
Bellabeat is a wearable smart device company co-founded by Urška Sršen and Sando Mur. Their aim is to create fashionable, fitness... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import torch
from UnarySim.sw.metric.metric import NormStability, NSbuilder, Stability, ProgressiveError
from UnarySim.sw.stream.gen import RNG, SourceGen, BSGen
from UnarySim.sw.kernel.relu import UnaryReLU
import random
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d i... | github_jupyter |
# Review of Day 1
## Fundamentals
### Data Types
Everything in Python is an object, and every object has a type.
Let's review the most important ones.
**Integers** – Whole Numbers
```
i = 3
i
```
**Floats** – Decimal Numbers
```
f = 3.4
f
```
**Strings** – Bits of Text
```
s = 'python'
s
```
**Lists** – Orde... | github_jupyter |
# データサイエンス100本ノック（構造化データ加工編） - SQL
## はじめに
- データベースはPostgreSQL13です
- 初めに以下のセルを実行してください
- セルに %%sql と記載することでSQLを発行することができます
- jupyterからはdescribeコマンドによるテーブル構造の確認ができないため、テーブル構造を確認する場合はlimitを指定したSELECTなどで代用してください
- 使い慣れたSQLクライアントを使っても問題ありません（接続情報は以下の通り）
- IPアドレス：Docker Desktopの場合はlocalhost、Docker toolboxの場合は192.168.99.1... | github_jupyter |
# The basics
Here we'll discuss how to instantiate spherical harmonic maps, manipulate them, plot them, and compute simple phase curves and occultation light curves.
```
%matplotlib inline
%run notebook_setup.py
import starry
import matplotlib.pyplot as plt
import numpy as np
starry.config.lazy = False
starry.config... | github_jupyter |
# süntaktiline analüüs [deplacy](https://koichiyasuoka.github.io/deplacy/) kaudu
## [Stanza](https://stanfordnlp.github.io/stanza)-ga
```
!pip install deplacy stanza
import stanza
stanza.download("et")
nlp=stanza.Pipeline("et")
doc=nlp("Suuga teeb suure linna, käega ei tee kärbse pesagi.")
import deplacy
deplacy.rend... | github_jupyter |
# BloodPressure example
This example assumes that PyShEx has been installed in jupyter environment
```
from pyshex import ShExEvaluator
from rdflib import Namespace
shex = """
BASE <http://example.org/ex/>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX ex: <http://ex.example/#>
PREFIX foaf: <http://xmlns.com/f... | github_jupyter |
# Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and... | github_jupyter |
# Вычислительный практикум
# Задание №2
### Итерационные методы (простой итерации, Зейделя, верхней релаксации) решения СЛАУ.
## Ковальчуков Александр
### 321 группа
### Вариант №6
```
import numpy as np
```
# Параметры задачи
```
A = np.array([[ 9.016024, 1.082197, -2.783575],
[ 1.082197, 6.846595, ... | github_jupyter |
# TensorFlow Basics
```
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
tf.__version__
```
## Constants
```
h = tf.constant('Hello World')
h
h.graph is tf.get_default_graph()
x = tf.constant(100)
x
# Create Session object in which we can run operations.
# A session obje... | github_jupyter |
## 1-3. 複数量子ビットの記述
ここまでは１量子ビットの状態とその操作（演算）の記述について学んできた。この章の締めくくりとして、$n$個の量子ビットがある場合の状態の記述について学んでいこう。テンソル積がたくさん出てきてややこしいが、コードをいじりながら身につけていってほしい。
$n$個の**古典**ビットの状態は$n$個の$0,1$の数字によって表現され、そのパターンの総数は$2^n$個ある。
量子力学では、これらすべてのパターンの重ね合わせ状態が許されているので、$n$個の**量子**ビットの状態$|\psi \rangle$はどのビット列がどのような重みで重ね合わせになっているかという$2^n$個の複素確率振幅で記... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Reinforcement Learning in Azur... | github_jupyter |
# Algorithm accuracy analysis
- In order to test whether Compas scores do an accurate job of deciding whether an offender is Low, Medium or High risk, we ran a Cox Proportional Hazards model. Northpointe, the company that created COMPAS and markets it to Law Enforcement, also ran a Cox model in [their validation study... | github_jupyter |
# cadCAD Template: Robot and the Marbles - Part 4
## Non-determinism
Non-deterministic systems exhibit different behaviors on different runs for the same input. The order of heads and tails in a series of 3 coin tosses, for example, is non deterministic.
Our robots ... | github_jupyter |
```
#imports and helper functions
import GPy
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
def plot_hist(bins,bin_ranges,errs,alpha):
"""Plots a histogram"""
assert len(bins)==(len(bin_ranges)-1)
yvals = bins/(np.diff(bin_ranges)[:,None])
evals = 1.96*errs/(np.diff(bin_ranges)[... | github_jupyter |
# Solving the Traveling Salesperson Problem with Azure Quantum QIO
Hello and welcome! In this notebook we will walk you through how you (can) solve the traveling salesperson problem (also known as the traveling salesman problem) with the Azure Quantum quantum inspired optimization (QIO) service.
## Introduction
The tr... | github_jupyter |
# Deep Q-Network implementation
This notebook shamelessly demands you to implement a DQN - an approximate q-learning algorithm with experience replay and target networks - and see if it works any better this way.
```
#XVFB will be launched if you run on a server
import os
if type(os.environ.get("DISPLAY")) is not str... | github_jupyter |
# *Unsupervised learning: Latent Dirichlet allocation (LDA) topic modeling*
```
## Install a Python package for LDA
# http://pythonhosted.org/lda/getting_started.html
!pip3 install lda
## Importing basic packages
import os
import numpy as np
## Downloading 'Essays' by Ralph Waldo Emerson
os.chdir('/sharedfolder/')
... | github_jupyter |
```
#export
from fastai2.basics import *
from fastai2.vision.core import *
from fastai2.vision.data import *
from fastai2.vision.augment import *
from fastai2.vision import models
#default_exp vision.learner
from nbdev.showdoc import *
```
# Learner for the vision applications
> All the functions necessary to build ... | github_jupyter |
# Marginal Likelihood Implementation
The `gp.Marginal` class implements the more common case of GP regression: the observed data are the sum of a GP and Gaussian noise. `gp.Marginal` has a `marginal_likelihood` method, a `conditional` method, and a `predict` method. Given a mean and covariance function, the functio... | github_jupyter |
```
import numpy as np
import pandas as pd
import plotly.graph_objects as go
df = pd.read_csv("/content/us_job_industry_data_2019.csv")
# TO DO: MAP CITIES TO MASTER LIST
def wrangle(X):
"""
Wrangles and cleans dataframe
"""
# Creating 2 copies to handle numeric and non-numeric data
numeric = X.copy()
no... | github_jupyter |
# Alignment & Operatrions
This notebook is more about *understanding* pandas, "going with the flow", than any particular method or operation.
Alignment is a key part of many parts of pandas, including
- binary operations (`+, -, *, /, **, ==, |, &`) between pandas objects
- merges / joins / concats
- constructors (`p... | github_jupyter |
# Tutorial XX: Template
This tutorial walks you through the process of *FILL IN*. The reason behind when and why this is important should be briefly described in the remainder of this paragraph. If possible, this should be further elucidated by a complementary figure, which can be placed in the folder *tutorials/img*.... | github_jupyter |
```
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer,CountVectorizer
import sklearn
import boto3
from s3 import get_file
from sklearn.decomposition import LatentDirichletAllocation
import pyLDAvis
import pyLDAvis.sklearn
pyLDAvis.enable_notebook()
from matplotlib impo... | github_jupyter |
# Sign & Speak ML Instructions
This notebook shows how to use Amazon SageMaker to run the training and inference scripts for the Sign & Speak project.
Use the `conda_pytorch_p36` kernel to run the cells in this notebook.
## Training
The following cell defines the training job to be run by Amazon SageMaker. It point... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Response-to-Thomas-Icard's-question-about-counterfactual-implementation-in-probability-trees." data-toc-modified-id="Response-to-Thomas-Icard's-question-about-counterfactual-implementation-in-probability-tr... | github_jupyter |
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