text stringlengths 2.5k 6.39M | kind stringclasses 3
values |
|---|---|
```
import os
store_dir = '/global/cfs/projectdirs/m3443/usr/caditi97/iml2020/misaligned/new_mis/'
og_evts = '/global/cfs/projectdirs/m3443/data/trackml-kaggle/train_all/'
os.environ['TRKXINPUTDIR']=f"{store_dir}shift_x/"
os.environ['TRKXOUTPUTDIR']= f"{store_dir}shift_x_pre/"
import pkg_resources
import yaml
import pp... | github_jupyter |
# Dictionaries and Sets
**CS1302 Introduction to Computer Programming**
___
```
%reload_ext mytutor
```
## Motivation for associative container
The following code simulates the outcomes from rolling a dice multiple times.
```
import random
dice_rolls = [random.randint(1,6) for i in range(10)]
print(*dice_rolls)
`... | github_jupyter |
```
# default_exp models.MINIROCKET
```
# MINIROCKET
> A Very Fast (Almost) Deterministic Transform for Time Series Classification.
```
#export
from tsai.imports import *
from tsai.utils import *
from tsai.data.external import *
from tsai.models.layers import *
#export
from sktime.transformations.panel.rocket import... | github_jupyter |
Conditional Generative Adversarial Network
----------------------------------------
A Generative Adversarial Network (GAN) is a type of generative model. It consists of two parts called the "generator" and the "discriminator". The generator takes random values as input and transforms them into an output that (hopefu... | github_jupyter |
# GATE Worker
The GATE Worker is a module that allows to run anything in a Java GATE process from Python and interchange documents between Python and Java.
One possible use of this is to run an existing GATE pipeline on a Python GateNLP document.
This is done by the python module communicating with a Java process ov... | github_jupyter |
```
#! /usr/bin/env python3
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
# File name: test.py
# First Edit: 2020-02-13
# Last Change: 13-Feb-2020.
"""
adb kill-server
adb start-server
adb device -l
adb shell dumpsys display
"""
import io
import os
import subprocess
import cv2
import numpy as np
import pytesseract
import ... | github_jupyter |
[Table of Contents](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb)
# Smoothing
```
#format the book
%matplotlib inline
from __future__ import division, print_function
from book_format import load_style
load_style()
```
## Introduction
The perform... | github_jupyter |
```
import os
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['mathtext.fontset'] = 'stix'
```
# Calculate $\kappa$ sampled from the first training
In the first training, we let 200 independent LSTMs predict 200 trajectories of 200$ns$. Since we are using LSTM as a generative model, we can also train ... | github_jupyter |
## Dependencies
```
!pip install --quiet /kaggle/input/kerasapplications
!pip install --quiet /kaggle/input/efficientnet-git
import warnings, glob
from tensorflow.keras import Sequential, Model
import efficientnet.tfkeras as efn
from cassava_scripts import *
seed = 0
seed_everything(seed)
warnings.filterwarnings('ig... | github_jupyter |
## Trajectory equations:
```
%matplotlib inline
import matplotlib.pyplot as plt
from sympy import *
init_printing()
Ex, Ey, Ez = symbols("E_x, E_y, E_z")
Bx, By, Bz, B = symbols("B_x, B_y, B_z, B")
x, y, z = symbols("x, y, z")
vx, vy, vz, v = symbols("v_x, v_y, v_z, v")
t = symbols("t")
q, m = symbols("q, m")
c, eps0 ... | github_jupyter |
# Intro to Pandas
Pandas is a Python package for data analysis and exposes two new
data structures: Dataframes and Series.
- [Dataframes](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html) store tabular data consisting of rows and columns.
- [Series](https://pandas.pydata.org/pandas-docs/sta... | github_jupyter |
```
import cv2
import time
import h5py
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from tqdm import tqdm
data_path = "/home/sid-pc/ashutosh/DDP/NYU Dataset and Toolbox/nyu_depth_v2_labeled.mat"
img_resize_X = 320
img_resize_Y = 240
depth_resize_X = 80
depth_resize_Y = 60
t1 = time.time()
f... | github_jupyter |
**This notebook is an exercise in the [Intro to Game AI and Reinforcement Learning](https://www.kaggle.com/learn/intro-to-game-ai-and-reinforcement-learning) course. You can reference the tutorial at [this link](https://www.kaggle.com/alexisbcook/n-step-lookahead).**
---
# Introduction
In the tutorial, you learned ... | github_jupyter |
# Computer Vision Example: Image Classification with WMLA
https://developer.ibm.com/technologies/artificial-intelligence/tutorials/use-computer-vision-with-dli-watson-machine-learning-accelerator/
This workflow is documented here...
### Contents
- [Introduction](#Introduction)
- [Upload this notebook to your envir... | github_jupyter |
```
import pandas as pd
import scipy as sp
from scipy.sparse import diags
import numpy as np
from numpy import linalg as LA
import sys
import matplotlib.pyplot as plt
#importing seaborn for plotting
import seaborn as sns
#for plotting purposes
%pylab inline
sns.set_style('ticks')
sns.set_context('paper')
from IPyth... | github_jupyter |
```
%reload_ext autoreload
%autoreload 2
%matplotlib inline
import IPython
import matplotlib.pyplot as plt
import numpy as np
import soundfile as sf
import time
from tqdm import tqdm
import tensorflow as tf
from nara_wpe.tf_wpe import wpe
from nara_wpe.tf_wpe import online_wpe_step, get_power_online
from nara_wpe.uti... | github_jupyter |
[](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/streamlit_notebooks/healthcare/NER_DIAG_PROC.ipynb)
# **Detect diagnosis an... | github_jupyter |
## Rover Lab Notebook
This notebook contains the functions from the lesson and provides the scaffolding you need to test out your mapping methods. The steps you need to complete in this notebook for the project are the following:
* First just run each of the cells in the notebook, examine the code and the results of ... | github_jupyter |
# Prerequisites
Install Theano and Lasagne using the following commands:
```bash
pip install -r https://raw.githubusercontent.com/Lasagne/Lasagne/master/requirements.txt
pip install https://github.com/Lasagne/Lasagne/archive/master.zip
```
Working in a virtual environment is recommended.
# Data preparation
Current ... | github_jupyter |
```
# import the important libraries
import pandas as pd
pd.set_option('display.max_columns', 500)
pd.set_option('display.max_rows', 1000)
# Check what's in this file
# The file is from NOAA for year 1950
df = pd.read_csv("StormEvents_details-ftp_v1.0_d1950_c20170120.csv")
ls
# the first 5 rows of the file
df.head()
... | github_jupyter |
> **Copyright (c) 2021 CertifAI Sdn. Bhd.**<br>
<br>
This program is part of OSRFramework. You can redistribute it and/or modify
<br>it under the terms of the GNU Affero General Public License as published by
<br>the Free Software Foundation, either versi... | github_jupyter |
# Project - Seminar Computer Vision by Deep Learning (CS4245) 2020/2021
Group Number: 20
Student 1: Stan Zwinkels
Student 2: Ted de Vries Lentsch
Date: June 14, 2021
## Instruction
For correct functioning of this notebook, the dataset [morado_5may](https://www.kaggle.com/teddevrieslentsch/morado-5may) must be in ... | github_jupyter |
## Reinforcement Learning for seq2seq
This time we'll solve a problem of transribing hebrew words in english, also known as g2p (grapheme2phoneme)
* word (sequence of letters in source language) -> translation (sequence of letters in target language)
Unlike what most deep learning researchers do, we won't only trai... | github_jupyter |
<a href="https://colab.research.google.com/github/joselvira/BiomecanicaPython/blob/master/Notebooks/Transformar_Bases_de_Datos.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# CÓMO TRANSFORMAR LA ORIENTACIÓN DE LAS BASES DE DATOS
Normalmente utili... | github_jupyter |
# Plot Entropy of Gaussian
```
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
from scipy.integrate import quadrature
def exact_entropy(s):
return np.log(s*np.sqrt(2*np.pi*np.e))
sigmas = [0.4,0.8,1.2,2.0,3.5]
x_pts = np.linspace(-5,5,1000)
fig, axs = plt.subplots(1,2,figsize=(12,3)... | github_jupyter |
<a href="https://colab.research.google.com/github/yuanqing-wang/AFEP/blob/master/test_inference.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import sys
sys.path.append('..')
import warnings
if not sys.warnoptions:
warnings.simplefilter("i... | github_jupyter |
##### Copyright 2019 The TensorFlow Probability Authors.
Licensed under the Apache License, Version 2.0 (the "License");
```
#@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# you may not use this file except in compliance with the License.
# You may obtain a copy of th... | github_jupyter |
```
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
```
### Helper Functions
```
import tensorflow as tf
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import scipy
from scipy import ndimage
from sklearn.datasets import fetch_openml
mnist = fetch_openml('mnist_784')
x = mnist.data
y = mnist.target
e_k = np.zeros_like(x)
s_k = np.zeros_like(x)
n_k = np.zeros_like(x)
nw_k = np.zeros_like(x)
ne_k = np.zeros_like(x)
s... | github_jupyter |
## 3.4 编辑段落
### 3.4.1 段落首行缩进调整
许多出版社要求文章段落必须首行缩进，若想调整段落首行缩进的距离，可以使用`\setlength{\parindent}{长度}`命令，在`{长度}`处填写需要设置的距离即可。
【**例3-10**】使用`\setlength{\parindent}{长度}`命令调整段落首行缩进为两字符。
```tex
\documentclass[12pt]{article}
\setlength{\parindent}{2em}
\begin{document}
In \LaTeX, We can use the setlength command to adjust th... | github_jupyter |
# Qt Demo
This will launch various Qt compatible packages
Nota: as of 2019-05-26th, PySide2-5.13+ compatibility is
- Ok for Qtconsole, Qtpy, pyzo, wppm, PyQtgraph, rx
- ToDo for Spyder, guidata, guiqwt
## Qt4 & Qt5 Dedicated Graphic libraries: PyQtgraph, guidata, guiqwt
```
# PyQtgraph (Scientific Graphics and G... | github_jupyter |
```
"""
Distributed Proximal Policy Optimization (Distributed PPO or DPPO) continuous
version implementation with distributed Tensorflow and Python’s multiprocessing
package. This implementation uses normalized running rewards with GAE. The code
is tested with Gym’s continuous action space environment, Pendulum-v0 o... | github_jupyter |
```
from IPython.display import Image
```
# CNTK 204: Sequence to Sequence Networks with Text Data
## Introduction and Background
This hands-on tutorial will take you through both the basics of sequence-to-sequence networks, and how to implement them in the Microsoft Cognitive Toolkit. In particular, we will implem... | github_jupyter |
<a href="https://colab.research.google.com/github/GoogleCloudPlatform/tensorflow-without-a-phd/blob/master/tensorflow-mnist-tutorial/keras_02_mnist_dense.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
### Parameters
```
BATCH_SIZE = 128
EPOCHS = 1... | github_jupyter |
# CX 4230, Spring 2016: [22] Input modeling
This notebook includes sample code to accompany the slides from the Monday, February 29 class. It does not contain any exercises.
```
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
%matplotlib inline
X = np.array ([105.84, 28.92, 98.64, 55.64,
... | github_jupyter |
# Ex2 - Getting and Knowing your Data
Check out [Chipotle Exercises Video Tutorial](https://www.youtube.com/watch?v=lpuYZ5EUyS8&list=PLgJhDSE2ZLxaY_DigHeiIDC1cD09rXgJv&index=2) to watch a data scientist go through the exercises
This time we are going to pull data directly from the internet.
Special thanks to: https:/... | github_jupyter |
```
import tensorflow as tf
import numpy as np
import random
import matplotlib.pyplot as plt
from zipfile import ZipFile
def unzip(nm):
with ZipFile(nm,"r") as zip:
zip.extractall()
unzip("archive.zip")
random.seed(123)
np.random.seed(123)
tf.random.set_seed(123)
train_ds = tf.keras.preprocessing.image... | github_jupyter |
```
import numpy as np
import pandas as pd
from plotnine import *
from mizani.transforms import trans
```
### Guitar Neck ###
*Using a transformed x-axis to visualise guitar chords*
The x-axis is transformed to resemble the narrowing width of frets on a 25.5 inch Strat. To do that
we create custom transformation.
Th... | github_jupyter |
<a href="https://colab.research.google.com/github/pachterlab/GFCP_2021/blob/main/notebooks/vcy_scvelo_comparison.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#Figure 1: The user-facing workflows of `velocyto` and `scVelo`
In this notebook, we re... | github_jupyter |
```
%matplotlib inline
from pyvista import set_plot_theme
set_plot_theme('document')
```
Compare Field Across Mesh Regions
=================================
Here is some velocity data from a glacier modelling simulation that is
compared across nodes in the simulation. We have simplified the mesh to
have the simulatio... | github_jupyter |
```
# Import Python packages
import pickle
# Import Third party packages
import numpy as np
import matplotlib.pyplot as plt
S1_terms = ['u', 'du/dx', 'f']
S2_terms = ['u', 'du/dx', 'f', 'u^{2}']
S3_terms = ['du/dx', 'f']
S4_terms = ['f']
true_terms = [S1_terms, S2_terms, S3_terms, S4_terms]
tags = ["S1", "NLSL", "S3"... | github_jupyter |
```
# This notebook is used to decide on a tolerable level of corruptableness.
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from scipy.stats import entropy as KL_divergence
from slda.topic_models import BLSLDA
from modules.helpers import plot_images
# Generate topics
# We... | github_jupyter |
# Chapter 1, figures 3 and 4
This notebook will show you how to produce figures 1.3 and 1.4 after the predictive modeling is completed.
The predictive modeling itself, unfortunately, doesn't fit in a notebook. The number-crunching can take several hours, and although logistic regression itself is not complicated, the... | github_jupyter |
# Ch 9 Multi-Agent Reinforcement Learning
##### Listing 9.3
```
import numpy as np
import torch
from matplotlib import pyplot as plt
def init_grid(size=(10,)):
grid = torch.randn(*size)
grid[grid > 0] = 1
grid[grid <= 0] = 0
grid = grid.byte() #A
return grid
def get_reward(s,a): #B
r = -1
... | github_jupyter |
# Store tracts and rental listings in PostGIS
...for a fast spatial-join of listings to tracts.
First, create the database from command prompt if it doesn't already exist:
```
createdb -U postgres craigslist_tracts
psql -U postgres -d craigslist_tracts -c "CREATE EXTENSION postgis;"
```
More info in the psycopg2 do... | github_jupyter |
<a href="https://colab.research.google.com/github/jonfisik/Projects/blob/master/VetoresPython.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
u = [1,2]
v = [2,1]
# somou listas
u + v
u = np.arra... | github_jupyter |
[](http://rpi.analyticsdojo.com)
<center><h1>Introduction to MatplotLab - Python</h1></center>
<center><h3><a href = 'http://rpi.analyticsdojo.com'>rpi.analyticsdojo.com</a></h3></center>
This has been... | github_jupyter |
# Automated ML
```
from azureml.core import Workspace, Experiment
from azureml.data.dataset_factory import TabularDatasetFactory
from train import clean_data
import pandas as pd
from sklearn.model_selection import train_test_split
import os
from azureml.core.compute import ComputeTarget, AmlCompute
from azureml.core.c... | github_jupyter |
## Training a recommendation model for Google Analytics data using BigQuery ML
This notebook accompanies the article
[Training a recommendation model for Google Analytics data using BigQuery ML](https://towardsdatascience.com/training-a-recommendation-model-for-google-analytics-data-using-bigquery-ml-2327f9a2e8e9)
##... | github_jupyter |
<img src="http://hilpisch.com/tpq_logo.png" alt="The Python Quants" width="35%" align="right" border="0"><br>
# Python for Finance
**Analyze Big Financial Data**
O'Reilly (2014)
Yves Hilpisch
<img style="border:0px solid grey;" src="http://hilpisch.com/python_for_finance.png" alt="Python for Finance" width="30%" a... | github_jupyter |
# Training Deep Neural Networks
> Chapter 11
- permalink: /11_training_deep_neural_networks
_This notebook contains all the sample code and solutions to the exercises in chapter 11._
# Setup
First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. ... | github_jupyter |
# Exploratory Data Analysis
ALS Hiring
A dataset simulating CRM data is available in these public AWS S3 files:
Constituent Information: https://als-hiring.s3.amazonaws.com/fake_data/2020-07-01_17%3A11%3A00/cons.csv
Constituent Email Addresses: https://als-hiring.s3.amazonaws.com/fake_data/2020-07-01_17%3A11%... | github_jupyter |
```
import os
from argparse import Namespace
from collections import Counter
import json
import re
import string
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import torch.optim as ... | github_jupyter |
```
import jax.numpy as np
# from jax.config import config; config.update("jax_enable_x64", True)
from jax import jacfwd, jacrev, hessian
import numpy as onp
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import xara
import xaosim as xs
from xaosim.pupil import PHARO
from scipy.ndimage import fourier_shi... | github_jupyter |
# Fickian Diffusion
In this example, we will learn how to perform Fickian diffusion on a `Cubic` network. The algorithm works fine with every other network type, but for now we want to keep it simple. [See here](/examples/notebooks/networks/generation) for more details on different network types.
```
import numpy as... | github_jupyter |
## Part 1: LLE
Implement Locally Linear Embedding function
```
from sklearn.neighbors import kneighbors_graph
from scipy.sparse import csr_matrix
from numpy import matlib
import numpy as np
def csr_from_mat(W, NI):
n, k = W.shape
data = np.reshape(W, n*k)
cols = np.reshape(NI, n*k)
r... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.lines import Line2D
from TimeSeriesCrossValidation import splitTrain, splitTrainVal, splitTrainValTest
timeSeries = np.arange(27)
timeSeries
def show_train(X, y, num):
for j in np.arange(num):
print... | github_jupyter |
# Feature Extraction from Text
This notebook is divided into two sections:
* First, we'll find out what what is necessary to build an NLP system that can turn a body of text into a numerical array of *features* by **manually calcuating frequencies and building out TF-IDF**.
* Next we'll show how to perform these steps... | github_jupyter |
```
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import boto3
import os
from sagemaker.amazon.amazon_estimator import get_image_uri
import sagemaker
from sagemaker import get_execution_role
from sklearn.model_selection import train_test_split
import numpy as np
import sagemaker
from random import... | github_jupyter |
# Constellation and Chain Analysis: Prebuilt Chains
<img src="chainPaths.jpg" alt="Drawing" style="width: 500px;"/>
**Terminology**
* Node = Object in STK
* Edge = Access between two objects in STK
* Strand = The sequence of nodes and edges to complete access in a chain
**This notebook shows how to:**
* Merge access... | github_jupyter |
# Importing modules
```
import numpy as np
import pandas as pd
```
# Data
```
colunas = ['ANO_CINEMATOGRAFICO', 'SEMANA_CINEMATOGRAFICA', 'TIPO_SESSAO',
'REGISTRO_COMPLEXO', 'REGISTRO_GRUPO','REGISTRO_SALA', 'CPB_ROE', 'ASSENTOS_INFERIDO',
'OCUPAÇÃO_SALA_INFERIDA', 'd_t', 'id_NAC', 'xt_comp', 't_comp',... | github_jupyter |
# Introduction to Object Oriented Programming
## Lesson outline
- Object-oriented programming syntax
- Procedural vs. object-oriented programming
- Classes, objects, methods and attributes
- Coding a class
- Magic methods
- Inheritance
- Using object-oriented programming to make a Python package
... | github_jupyter |
# Contrast Effects
### Authors
Ndèye Gagnessiry Ndiaye and Christin Seifert
### License
This work is licensed under the Creative Commons Attribution 3.0 Unported License https://creativecommons.org/licenses/by/3.0/
This notebook illustrates 3 contrast effects:
- Simultaneous Brightness Contrast
- Chevreul Illusio... | github_jupyter |
# Rasterio plotting of Landsat-8 scenes
In this notebook, we will download bands of a Landsat-8 scene, visualize them with [rasterio's plotting module]( https://rasterio.readthedocs.io/en/latest/topics/plotting.html), and write an RGB image as rendered GeoTIFF.
```
import os
import matplotlib.pyplot as plt
import num... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
%aimport utils_1_1
import pandas as pd
import numpy as np
import altair as alt
from altair_saver import save
import datetime
import dateutil.parser
from os.path import join
from constants_1_1 import SITE_FILE_TYPES
from utils_1_1 import (
get_site_file_paths,
get_site_fi... | github_jupyter |
# Example: CanvasXpress splom Chart No. 3
This example page demonstrates how to, using the Python package, create a chart that matches the CanvasXpress online example located at:
https://www.canvasxpress.org/examples/splom-3.html
This example is generated using the reproducible JSON obtained from the above page and ... | github_jupyter |
(nm_heun_method)=
# Heun's method
```{index} Heun's method
```
{ref}`Euler's method <nm_euler_method>` is first-order accurate because it calculates the derivative using only the information available at the beginning of the time step. Higher-order convergence can be obtained if we also employ information from other po... | github_jupyter |
# Temporal-Difference Methods
In this notebook, you will write your own implementations of many Temporal-Difference (TD) methods.
While we have provided some starter code, you are welcome to erase these hints and write your code from scratch.
---
### Part 0: Explore CliffWalkingEnv
We begin by importing the necess... | github_jupyter |
# Using `pyoscode` in cosmology
`pyoscode` is a fast numerical routine suitable for equations of the form
$$ \ddot{x} + 2\gamma(t)\dot{x} + \omega^2(t) = 0, $$
with
- $x(t)$: a scalar variable (e.g. curvature perturbation),
- $\omega(t)$: frequency,
- $\gamma(t)$: friction or first-derivative term.
In general $\ga... | github_jupyter |
# Project 5: NLP on Financial Statements
## Instructions
Each problem consists of a function to implement and instructions on how to implement the function. The parts of the function that need to be implemented are marked with a `# TODO` comment. After implementing the function, run the cell to test it against the uni... | github_jupyter |
```
import datetime
import os
import yaml
import optuna
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Lecture du fichier d'environnement
ENV_FILE = '../env.yaml'
with open(ENV_FILE) as f:
params = yaml.load(f) #, Loader=yaml.FullLoader)
# Initialisation des chemins vers les fichiers
ROOT... | github_jupyter |
<h1> Classifying Iris Flower Dataset Using Naive Bayes Classifier </h1>
<h2> Naive Bayes Classifier </h2>
Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem. It comprises of a collection of algorithms where all of them share a common principle, that is every pair of features... | github_jupyter |
*Analytical Information Systems*
# Tutorial 1 - Introduction
Matthias Griebel<br>
Lehrstuhl für Wirtschaftsinformatik und Informationsmanagement
SS 2019
<h1>Agenda<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#1-Course-Overview" data-toc-modified-id="1-Course-Overview-1... | github_jupyter |
Taller práctico - SQL
===
**Juan David Velásquez Henao**
jdvelasq@unal.edu.co
Universidad Nacional de Colombia, Sede Medellín
Facultad de Minas
Medellín, Colombia
---
Haga click [aquí](https://github.com/jdvelasq/R-for-data-science/blob/master/01-uso-interactivo.ipynb) para acceder a la última versión onlin... | github_jupyter |
```
%load_ext watermark
%watermark -v -p numpy,sklearn,scipy,matplotlib,tensorflow
```
**14장 – 순환 신경망**
_이 노트북은 14장에 있는 모든 샘플 코드와 연습문제 해답을 가지고 있습니다._
# 설정
파이썬 2와 3을 모두 지원합니다. 공통 모듈을 임포트하고 맷플롯립 그림이 노트북 안에 포함되도록 설정하고 생성한 그림을 저장하기 위한 함수를 준비합니다:
```
# 파이썬 2와 파이썬 3 지원
from __future__ import division, print_function, un... | github_jupyter |
# EventVestor: Credit Facility
In this notebook, we'll take a look at EventVestor's *Credit Facility* dataset, available on the [Quantopian Store](https://www.quantopian.com/store). This dataset spans January 01, 2007 through the current day, and documents financial events covering new or extended credit facilities.
... | github_jupyter |
#### Package Import
```
import numpy as np
from numpy import math
from scipy.stats import norm
from scipy import stats
import matplotlib.pyplot as plt
import progressbar
```
#### Model Specification: OU Process
1. $dX_{t} = \theta_{1}(\theta_{2} - X_{t})dt + \sigma dW_{t}$, $Y_{t}|X_{t} \sim \mathcal{N}(X_{t}, \thet... | github_jupyter |
```
!pip install -U -q PyDrive
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials
auth.authenticate_user()
gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_application_default()
drive = GoogleDrive(gaut... | github_jupyter |
# pyscal `Trajectory`
`Trajectory` is a `pyscal` module intended for working with molecular dynamics trajectories which contain more than one time slice. Currently, the module only supports [LAMMPS dump](https://lammps.sandia.gov/doc/dump.html) text file formats. It can be used to get a single or slices from a traject... | github_jupyter |
# Adding and Removing Data
We will be working with the `data/earthquakes.csv` file again, so we need to handle our imports and read it in.
## About the Data
In this notebook, we will be working with Earthquake data from September 18, 2018 - October 13, 2018 (obtained from the US Geological Survey (USGS) using the [USG... | github_jupyter |
# Triplet Loss for Implicit Feedback Neural Recommender Systems
The goal of this notebook is first to demonstrate how it is possible to build a bi-linear recommender system only using positive feedback data.
In a latter section we show that it is possible to train deeper architectures following the same design princi... | github_jupyter |
# Discrete Bayes Animations
```
from __future__ import division, print_function
import matplotlib.pyplot as plt
import sys
sys.path.insert(0,'..') # allow us to format the book
sys.path.insert(0,'../code')
import book_format
book_format.load_style(directory='..')
```
This notebook creates the animations for the Disc... | github_jupyter |
# nn explain
nn has two main parts : data and model components
containers are responsible for model components and parameters/buffers are responsible for model data
containers : Module, Sequential, ModuleList, ModuleDict, ParameterList, ParameterDict for module construction
parameters : parameter(...) for model t... | github_jupyter |
## First day: list comprehensions and generators
> List comprehensions and generators are in my top 5 favorite Python features leading to clean, robust and Pythonic code.
```
from collections import Counter
import calendar
import itertools
import random
import re
import string
import requests
```
### List comprehen... | github_jupyter |
# 7. Alfven operator
```
from numpy import linspace, meshgrid, pi, zeros, asarray
from scipy.linalg import eig
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import Math
from sympy.core.containers import Tuple
from sympy import symbols
from sympy import Symbol
from sympy import Lambda
from ... | github_jupyter |
# Contextual Bandit Content Personalization
In the Contextual Bandit(CB) introduction tutorial, we learnt about CB and different CB algorithms. In this tutorial we will simulate the scenario of personalizing news content on a site, using CB, to users. The goal is to maximize user engagement quantified by measuring cli... | github_jupyter |
# 01 Intro
- Introduction to Data Visualization
- Introduction to Matplotlib
- Basic Plotting with Matplotlib
- Dataset on Immigration to Canada
- Line Plots
# Introduction to Data Visualization
## Data visualization
> a way to show a
complex data in a form that is graphical and easy to understand.
>Transforming ... | github_jupyter |
Notebook written by [Zhedong Zheng](https://github.com/zhedongzheng)
```
import tensorflow as tf
import numpy as np
import sklearn
VOCAB_SIZE = 20000
EMBED_DIM = 100
RNN_SIZE = 70
CLIP_NORM = 5.0
BATCH_SIZE = 32
LR = {'start': 5e-3, 'end': 5e-4, 'steps': 1500}
N_EPOCH = 2
N_CLASS = 2
def sort_... | github_jupyter |
# NLP Learners
This module contains the main class to quickly define a `Learner` (and automatically generates an appropriate model) from your NLP data.
```
from fastai.gen_doc.nbdoc import *
from fastai.text import *
from fastai.docs import *
```
## Class RNNLearner
This is the class that handles the whole creatio... | github_jupyter |
# The Scientific Python Ecosystem
The Scientific Python Ecosystem is made up of a robust collection of packages that provide functionality for everything from simple numeric arrays to sophisticated machine learning algorithms. In this notebook, we'll introduce the core scientific python packages and some important ter... | github_jupyter |
# AdaBoost
在做重要决定的时候,我们会考虑吸收多个专家的意见而不是一个人的意见,机器学习处理问题的时候也可以采用这种方法.这就是元算法(meta-algorithm)背后的思路.元算法是对其他算法进行组合的一种方式,我们会先建立一个**单层决策树(decision stump)**分类器，实际上它是一个单节点的决策树.AdaBoots算法将应用在上述单层决策树之上,然后将在一个难数据集上应用AdaBoots分类器,以了解该算法是如何迅速超越其他分类器的.
强可学习(strongly learnable)和弱可学习(weakly learnable)
- 强可学习:如果存在一个多项式学习算法,并且它的学习率很高,那么我... | github_jupyter |
# Experiments with the bivariate Gaussian
In this notebook, we'll get a feel for the two-dimensional Gaussian by varying the covariance matrix, drawing random samples from the resulting distribution, and plotting contour lines of the density.
We begin, as always, by loading in standard packages.
```
%matplotlib inli... | github_jupyter |
## Computer Vision Interpret
[`vision.interpret`](/vision.interpret.html#vision.interpret) is the module that implements custom [`Interpretation`](/train.html#Interpretation) classes for different vision tasks by inheriting from it.
```
from fastai.gen_doc.nbdoc import *
from fastai.vision import *
from fastai.vision... | github_jupyter |
# Graphical User Interface programming in Python
Goal: Writing a simple Graphical User Interface (GUI) with PyQt based on available widgets.
## Exercice
The exercice for this training is to create a GUI for calculating the diffraction image obtained from a 2D cristal composed on a square of NxN atoms using the Laue ... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import sys
sys.path.append("..")
from optimus import Optimus
op = Optimus("dask")
df.cols.max()
# df = op.create.dataframe({"name": ["A1", "B2"]*20})
df = op.load.csv("store.csv", dtype="str")
df.cols.len("*").cols.max()
df.cols.min(compute=False)
df.cols.min(compute=True)
df
df.c... | github_jupyter |
```
# standard libraries
import pandas as pd
# Binance wrapper libraries
from binance.client import Client
from binance.websockets import BinanceSocketManager
def web_socket_modularized():
"""
Signature: web_socket() -> 'BinanceSocketManager'
Docstring:
Deals with real-time data.
Also takes ca... | github_jupyter |
# Explore ways to read/write params to/from a file
```
from see import base_classes
from see.Segmentors import segmentor
from see.ColorSpace import colorspace
from see.Workflow import workflow
from see.Segment_Fitness import segment_fitness
workflow.addalgos([colorspace, segmentor, segment_fitness])
wf = workflow()
`... | github_jupyter |
# Simple kriging in Python
This follows a tutorial and code by Connor Johnson, in [his blog post](http://connor-johnson.com/2014/03/20/simple-kriging-in-python/). It is openly licensed under the MIT license.
Some more geostatistics resources:
- More from Connor Johnson: https://github.com/cjohnson318/geostatsmodels
... | github_jupyter |
[](https://colab.research.google.com/github/giswqs/geemap/blob/master/examples/notebooks/tn_surface_water.ipynb)
# Automated mapping of surface water in the state of Tennessee using Google Earth Engine cloud computing
Author: Qiusheng Wu ([Web... | github_jupyter |
___
#### NAME:
#### STUDENT ID:
___
## Numpy Introduction
```
# Load required modules
import numpy as np
```
<br>
**1a) Create two numpy arrays called** ```a``` **and** ```b``` **where** ```a``` **should be all integers ... | github_jupyter |
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