text stringlengths 2.5k 6.39M | kind stringclasses 3
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|---|---|
```
#IMPORT SEMUA LIBARARY
#IMPORT LIBRARY PANDAS
import pandas as pd
#IMPORT LIBRARY UNTUK POSTGRE
from sqlalchemy import create_engine
import psycopg2
#IMPORT LIBRARY CHART
from matplotlib import pyplot as plt
from matplotlib import style
#IMPORT LIBRARY BASE PATH
import os
import io
#IMPORT LIBARARY PDF
from fpdf im... | github_jupyter |
```
try:
file = open('Curso em Vídeo/PYTHON_W3/ex115/lib/dados/cadastro.txt', 'rt')
file.close()
except:
print('deu ruim')
else:
print('foi...')
a = ['leo', '50\n']
print(a)
print(a[1][:-1])
print(a[0])
def éInt(str):
try:
str = int(str)
except (ValueError, TypeError):
print('Tip... | github_jupyter |
```
from lcls_live.datamaps.tabular import TabularDataMap
from lcls_live.datamaps.klystron import KlystronDataMap, klystron_pvinfo, existing_LCLS_klystrons_sector_station, subbooster_pvinfo, SUBBOOSTER_SECTORS
from pytao import Tao
import json
import os
from lcls_live import data_dir
import pandas as pd
```
## Build d... | github_jupyter |
# EDA Flights Dataset
```
# Packages
#install.packages("psych")
library(psych)
library(ggplot2)
#install.packages('ggpubr')
library(ggpubr)
library(dplyr)
#install.packages('reshape')
library(reshape)
library(ggrepel)
```
# Airlines Analysis
## Rate and Punctuality X Number of Flights (FIG 8)
```
dsAtd = read.csv('... | github_jupyter |
# Title
_Brief abstract/introduction/motivation. State what the chapter is about in 1-2 paragraphs._
_Then, have an introduction video:_
```
from bookutils import YouTubeVideo
YouTubeVideo("w4u5gCgPlmg")
```
**Prerequisites**
* _Refer to earlier chapters as notebooks here, as here:_ [Earlier Chapter](Fuzzer.ipynb)... | github_jupyter |
## Importing the requirements
```
import pandas as pd
import numpy as np
from collections import deque
import random
from sklearn import preprocessing
from google.colab import files
import time
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.compat.v1.k... | github_jupyter |
# Automated Machine Learning
#### Forecasting away from training data
## Contents
1. [Introduction](#Introduction)
2. [Setup](#Setup)
3. [Data](#Data)
4. [Prepare remote compute and data.](#prepare_remote)
4. [Create the configuration and train a forecaster](#train)
5. [Forecasting from the trained model](#forecasti... | github_jupyter |
### Instructions
The lecture applies an svc model (specifically a regression model) to the [epicurious dataset on Kaggle](https://www.kaggle.com/hugodarwood/epirecipes). The information in the dataset includes a list keywords and ingredients as well as the title of the recipe and a rating. The goal was to predict the... | github_jupyter |
```
import pandas as pd
from pathlib import Path
sub_folder = "9"
results_path = Path("results_community") / sub_folder # Directory where we will store all the results
results_path.mkdir(exist_ok=True, parents=True)
# Write mp3 lengths typescript object (requires mutagen)
from mutagen.mp3 import MP3
audio_base_path =... | github_jupyter |
# Decision tree (classification) algorithm
---
- **Traininig**:
1. Find most *informative* combination of `node of the tree`, `feature`, and `split value`
2. Do split if `max_depth` is not reached
3. Iterate over 1-2.
- **Inference** (prediction):
- Follow the rules ^_^.
## Decision t... | github_jupyter |
```
import pandas as pd
import random
import requests
import time
# formatting
pd.set_option('display.float_format', lambda x: '%.3f' % x)
pd.set_option('display.max_columns', 500)
pd.set_option('display.max_rows', 5000)
# postgres
# from sqlalchemy import create_engine
# import psycopg2
# To be Added, Database Lo... | github_jupyter |
# "AGILE SAFe Training - Notes"
> "Notes about SAFe (Scaled Agile Framework for Enterprise)"
- toc: true
- branch: master
- badges: false
- comments: true
- categories: [Others]
- hide: false
- search_exclude: false
- image: images/post-thumbnails/safe_agile.png
- metadata_key1: Agile
- metadata_key2:
## Introducing... | github_jupyter |
# How to use custom data and implement custom models and metrics
## Building a simple, first model
For demonstration purposes we will choose a simple fully connected model. It takes a timeseries of size `input_size` as input and outputs a new timeseries of size `output_size`. You can think of this `input_size` encodi... | github_jupyter |
<a href="https://colab.research.google.com/github/borisbrodski/py-interplanetary-simulator/blob/master/StarKicker%20-%20cutting%20down%20on%20travel%20time%20to%20Mars/Calculate_mission_profile.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# StarK... | github_jupyter |
```
# Reload when code changed:
%load_ext autoreload
%autoreload 2
%pwd
%matplotlib inline
import os
import sys
path = "../"
sys.path.append(path)
#os.path.abspath("../")
print(os.path.abspath(path))
import pandas as pd
import numpy as np
import json
import pickle
import core
import importlib
importlib.reload(core)
im... | github_jupyter |
# Data Explorer PixieApp
This notebook contains a [PixieApp](https://ibm-watson-data-lab.github.io/pixiedust/pixieapps.html) that provides quick visual and numeric summaries of the fields in a Pandas DataFrame.
## Requirements
A 2-dimensional Pandas DataFrame
## Help
Post an issue on https://github.com/ibm-watson... | github_jupyter |
# Adversarial X
**Разработчики: Алексей Умнов, Александр Шевченко, Ирина Сапарина**
# Adversarial examples
В этом разделе мы будем создавать adversarial примеры для типичной архитектуры сетей. Для начала нужно сделать простую сверточную сеть для классификации (2-3 слоя) и обучить ее до нормального качества (>97%). Д... | github_jupyter |
<a href="https://colab.research.google.com/github/Freitashbruno/Portfolio/blob/master/SemanaDS.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## **Semana de Data Science**
- Minerando Dados
### Conhecendo a base de dados
Monta o drive
```
from ... | github_jupyter |
# Ordinal Regression
```
import numpy as np
import pandas as pd
import scipy.stats as stats
from statsmodels.miscmodels.ordinal_model import OrderedModel
```
Loading a stata data file from the UCLA website.This notebook is inspired by https://stats.idre.ucla.edu/r/dae/ordinal-logistic-regression/ which is a R notebo... | github_jupyter |
```
import os
from tqdm.notebook import tqdm
from pathlib import Path
import pandas as pd
from nltk.lm import Vocabulary
import sys
sys.path.append("../../lib")
from metrics import levenshtein
import pickle
folder = "../../data/ICDAR2019_POCR_competition_dataset/ICDAR2019_POCR_competition_training_18M_without_Finnish/F... | github_jupyter |
# Matching Networks
Matching networks are yet another simple and efficient one-shot learning algorithm published by Google's DeepMind. It can even produce labels for the unobserved class in the dataset. Let us say we have a support set $S$ containing $K$ examples as ${(x_1,y_1),(x_2,y_2)...(x_k,y_k)}$. When given a qu... | github_jupyter |
# Numba example
```
from numba import njit, jit
import numba.cuda as cuda
import random
points = 100000
def pi(npoints):
n_in_circle = 0
for i in range(npoints):
x = random.random()
y = random.random()
if (x**2+y**2 < 1):
n_in_circle += 1
return 4*n_in_circle / npoint... | github_jupyter |
```
"""Tutorial on how to create a convolutional autoencoder w/ Tensorflow.
Parag K. Mital, Jan 2016
"""
import tensorflow as tf
import numpy as np
import math
from libs.activations import lrelu
from libs.utils import corrupt
# %%
def autoencoder(input_shape=[None, 784],
n_filters=[1, 10, 10, 10],
... | github_jupyter |
# Donkey library tools
Let's take a look the tools provided by the [Donkey](https://github.com/wroscoe/donkey) library.
## The Donkey library
The [Donkey library](https://github.com/wroscoe/donkey) has several components.
It is first and foremost a python library installed where your other python libraries are (e.g... | github_jupyter |
```
import numpy as np
import pandas as pd
titanic = pd.read_csv("titanictrain.csv")
titanic_test = pd.read_csv("titanictest.csv")
titanic.drop("PassengerId", axis = 1, inplace = True)
titanic.drop("Name", axis=1, inplace = True)
titanic.drop("Ticket", axis=1, inplace = True)
titanic.drop("Cabin", axis=1, inplace = Tru... | github_jupyter |
___
<img style="float: right; margin: 0px 0px 15px 15px;" src="https://upload.wikimedia.org/wikipedia/commons/thumb/4/4a/Python3-powered_hello-world.svg/1000px-Python3-powered_hello-world.svg.png" width="300px" height="100px" />
# <font color= #8A0829> Simulación matemática.</font>
#### <font color= #2E9AFE> `Lunes(... | github_jupyter |
# Node classification with GraphSAGE
<table><tr><td>Run the latest release of this notebook:</td><td><a href="https://mybinder.org/v2/gh/stellargraph/stellargraph/master?urlpath=lab/tree/demos/node-classification/graphsage-node-classification.ipynb" alt="Open In Binder" target="_parent"><img src="https://mybinder.org/... | github_jupyter |
# Tirone Levels
https://www.metastock.com/customer/resources/taaz/?p=110
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
# fix_yahoo_finance is used to fetch data
import fix_yahoo_finance as yf
yf.pdr_override()
# input
symbol = 'AAPL'
st... | github_jupyter |
## Geo classification
Divide tweets based on their geographic location and perform stance analysis per group.
```
import ast
import fasttext
import os
import pandas as pd
import re
import sys
from IPython.display import clear_output
from nltk.tokenize import TweetTokenizer
BELGIUM = "Belgium"
DATE = "date"
DISTANCE ... | github_jupyter |
###### Content under Creative Commons Attribution license CC-BY 4.0, code under MIT license (c)2014 L.A. Barba, C. Cooper, G.F. Forsyth, A. Krishnan.
# Phugoid Motion
Welcome to [**"Practical Numerical Methods with Python!"**](http://openedx.seas.gwu.edu/courses/GW/MAE6286/2014_fall/about) This course is a collaborat... | github_jupyter |
# SqueezeNet Architecture Design
*by Marvin Bertin*
<img src="../../images/keras-tensorflow-logo.jpg" width="400">
# SqueezeNet
**What is SqueezeNet?**
- a deep convolutional neural network (CNN)
- compressed architecture design
- model contains relatively small amount of parameters
- achieve AlexNet-level accuracy o... | github_jupyter |
```
sc
user_data = sc.textFile('ml-100k/u.user')
user_data.first()
user_fields = user_data.map(lambda line: line.split('|'))
num_users = user_fields.map(lambda fields:fields[0]).count() # 统计用户数
num_genders = user_fields.map(lambda fields: fields[2]).distinct().count() # gender count
num_occupations = user_fields.map(... | github_jupyter |
```
#hide
from mrl.core import *
```
# Molecular Reinforcement Learning
> Unlocking reinforcement learning for drug design
- hide_colab_badge:true
MRL is an open source python library designed to unlock the potential of drug design with reinforcement learning.
MRL bridges the gap between generative models and prac... | github_jupyter |
# Generating the Plots of the Denoising Results
In this notebook we reproduce the denoising results shown in figure 4 of the paper. We start by importing the required libraires, setting the plot parameters and loading the result data.
```
#import libraries
import numpy as np
import matplotlib.pyplot as plt
#set plot... | github_jupyter |
# Import and format MP data datasets for SCHOLAR
```
import os, sys, scipy, json
from scipy import sparse
import codecs
import numpy as np
import pandas as pd
import file_handling as fh
```
# MP speeches
## load booking data and save in SCHOLAR format
```
if sys.platform == "darwin":
pass
else:
raw_data_pat... | github_jupyter |
<a href="https://colab.research.google.com/github/thingumajig/colab-experiments/blob/master/qa.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Init
## Install packages
```
!pip install spacy
!pip3 uninstall --quiet --yes tensorflow
!pip3 instal... | github_jupyter |
<a href="https://cognitiveclass.ai"><img src = "https://ibm.box.com/shared/static/9gegpsmnsoo25ikkbl4qzlvlyjbgxs5x.png" width = 400> </a>
<h1 align=center><font size = 5>Introduction to Matplotlib and Line Plots</font></h1>
## Introduction
The aim of these labs is to introduce you to data visualization with Python a... | github_jupyter |
# Hierarchical Clustering
**Hierarchical clustering** refers to a class of clustering methods that seek to build a **hierarchy** of clusters, in which some clusters contain others. In this assignment, we will explore a top-down approach, recursively bipartitioning the data using k-means.
**Note to Amazon EC2 users**:... | github_jupyter |
# Deep Learning Bootcamp November 2017, GPU Computing for Data Scientists
<img src="../images/bcamp.png" align="center">
## 05 PyTorch Automatic differentiation
Web: https://www.meetup.com/Tel-Aviv-Deep-Learning-Bootcamp/events/241762893/
Notebooks: <a href="https://github.com/QuantScientist/Data-Science-PyCUDA-GPU... | 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 |
## RIHAD VARIAWA, Data Scientist - Who has fun LEARNING, EXPLORING & GROWING
## Visualizing Matrix Multiplication
In the videos on *__Linear Transformation and Matrices__*, you learned how a vector can be decomposed into it's basis vectors $\hat{i}$ and $\hat{j}$.
You also learned that you can tranform a vector by mul... | github_jupyter |
# Accuracy: Pitfalls and Edge Cases
This notebook describes SmartNoise's accuracy calculations, and ways in which an analyst might be tripped up by them.
### Overview
#### Accuracy vs. Confidence Intervals
Each privatizing mechanism (e.g. Laplace, Gaussian) in SmartNoise has an associated accuracy that is a functi... | github_jupyter |
```
# default_exp nlp
```
# NLP
> API details.
```
#hide
from nbdev.showdoc import *
%load_ext autoreload
%autoreload 2
%matplotlib inline
# export
from bs4 import BeautifulSoup
from collections import Counter
from collections.abc import Iterable
from functools import partial
from multipledispatch import dispatch
im... | github_jupyter |
<a href="https://colab.research.google.com/github/constantinpape/dl-teaching-resources/blob/main/exercises/classification/3_multi_layer_perceptron.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Multi-layer Perceptron on CIFAR10
Based on the prev... | github_jupyter |
# Constraints
Constraints are the second key element of a an optimization problem formulation. They ensure that the optimization results conforms to feasible / realistic solutions. There are three types of constraints in optimization:
* Variable bounds - upper and lower boundary values for design variables
* inequali... | github_jupyter |
#Setup
Specify your desired blender version and the path to your blend file within google drive or colab local storage.
###Info
If you do need more information on parameters etc. look here: [Blender CLI Wiki](https://docs.blender.org/manual/en/latest/advanced/command_line/arguments.html)
```
#@title Setup
#@markdown ... | github_jupyter |
```
import pongGym
import random
import numpy as np
import os
env = pongGym.DoublePong()
PAD_HEIGHT = 80
HALF_PAD_HEIGHT = PAD_HEIGHT // 2
def encode(state):
pd1y = state[21]
pd2y = state[23]
st = [(max(0, state[i*4+0] + state[i*4+2] * 3), abs(state[i*4+1] + state[i*4+3] * 3), state[i*4+2], state[i*4+3]) fo... | github_jupyter |
# **Running Pyspark in Colab**
To run spark in Colab, we need to first install all the dependencies in Colab environment i.e. Apache Spark 3.0.0 with hadoop 3.2, Java 8 and Findspark to locate the spark in the system. We can use the wget functionality to download these files directly to the current directory. This is... | github_jupyter |
```
import string
import itertools
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from tqdm import tqdm_notebook as tqdm
from gensim.models import *
import tensorflow as tf
from tensorflow.keras.utils import *
from tensorflow.keras.layers import *
from tensorflow.keras.models import *
import t... | github_jupyter |
Analytic solutions of viscoelastic fluids
======
This notebook outlines and solves for an analytic solution of a viscoelastic material undergoing simple shear.
**Simple shear in two dimensions**
This model compares the analytic to numeric stored stress of a viscoelastic material undergoing simple shear in two dimens... | github_jupyter |
```
library(gdata)
library(ggplot2)
library(grid)
library(gridExtra)
DT <- read.table("../Data/All_data.txt")
ratio_align <- read.table("../Data/Alignment_ratios_within_regions_across_diseases_wt_sims_patients_metrs_burdens.txt")
metr_burden <- "daly"
metr_res <- "RCTs"
max_plot <- 40
DT$Dis_lab <- DT$Disease
levels(DT... | github_jupyter |
## Import necessary packages (run once upon startup)
```
from __future__ import division
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.style.use("ggplot")
%matplotlib inline
from skimage.transform import resize
from skimage.morphology import skeletonize
from scipy.signal impor... | github_jupyter |
# Python Language Basics, IPython, and Jupyter Notebooks
```
import numpy as np
np.random.seed(12345)
np.set_printoptions(precision=4, suppress=True)
```
## The Python Interpreter
```python
$ python
Python 3.6.0 | packaged by conda-forge | (default, Jan 13 2017, 23:17:12)
[GCC 4.8.2 20140120 (Red Hat 4.8.2-15)] on l... | github_jupyter |
## Mini Project # 9 - Handwritten Digit Recognition
### Data Prep, Training and Evaluation
```
import numpy as np
import cv2
# Let's take a look at our digits dataset
image = cv2.imread('images/digits.png')
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
small = cv2.pyrDown(image)
cv2.imshow('Digits Image', small)
cv2... | github_jupyter |
In this exercise, you will use your new knowledge to propose a solution to a real-world scenario. To succeed, you will need to import data into Python, answer questions using the data, and generate **line charts** to understand patterns in the data.
## Scenario
You have recently been hired to manage the museums in th... | github_jupyter |
<img src="../Master/NotebookAddons/blackboard-banner.png" width="100%" />
<font face="Calibri">
<br>
<font size="6"> <b>Flood Mapping from Single Sentinel-1 SAR Images</b><img style="padding: 7px" src="../Master/NotebookAddons/UAFLogo_A_647.png" width="170" align="right"/></font>
<br>
<font size="4"> <b> Franz J Meyer... | github_jupyter |
# Channel combination with Siemens twix data
In our previous example, we loaded data from the Siemens RDA format. While this is the default format for exporting MRS data from the scanner, Siemens also supports exporting the data in the twix format, which contains the true raw data before any processing is done to it. ... | github_jupyter |
### <b> 1. Install some Libraries </b>
```
gpu_info = !nvidia-smi
gpu_info = '\n'.join(gpu_info)
if gpu_info.find('failed') >= 0:
print('Select the Runtime > "Change runtime type" menu to enable a GPU accelerator, ')
print('and then re-execute this cell.')
else:
print(gpu_info)
from psutil import virtual_m... | github_jupyter |
# CANDO Tutorial
This notebook will walk you through how to generate a CANDO matrix, set up a CANDO object, probe the data, benchmark the platform, and make therapeutic predictions.
## ToC
* [Introduction](#intro)
* [Get Started](#get-started)
* [Generate interaction matrix](#interaction-matrix)
* [Setting up CANDO ... | github_jupyter |
```
import csv
meta_table = []
with open('meta_table.csv', newline='') as csvfile:
reader = csv.reader(csvfile, delimiter=',')
for row in reader:
meta_table += [row]
lang_to_sizes = {}
orig_lang = {}
for name, size_str, _, _, lang in meta_table[1:]:
if lang.startswith('zh'):
orig_lang[n... | github_jupyter |
```
import os
import json
```
This notebook creates a dataset (images and labels as a json file). The dataset created can be used for pose classification.
In order to create a new dataset for gesture recoginition specify the following parameters
**no_of_classes** - Number of classes to be created. i.e. For hand p... | github_jupyter |
<img src="https://www.microsoft.com/en-us/research/uploads/prod/2020/05/Attribution.png" width="400">
<h1 align="left">Multi-investment Attribution: Distinguish the Effects of Multiple Outreach Efforts</h1>
A startup that sells software would like to know whether its multiple outreach efforts were successful in attra... | github_jupyter |
# **02. Computing HDX deuterated fractions from MD simulations**
```
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.stats import linregress
# Matplotlib settings for plotting
plt.rc('lines', linewidth=3, markersize=4)
plt.rc('axes', labelweight='heavy', labelsize=22, titles... | github_jupyter |
```
import pandas as pd
import numpy as np
import calendar
import re
```
### Import Datasets
```
#Import Covid Dataset
ds_covid = pd.read_csv("Data/COVID/owid-covid-data.csv")
#If missing values: Interpolate: ds_covid = ds_covid.interpolate(method='nearest')
ds_covid = ds_covid.fillna(0)
ds_covid.date = pd.to_dateti... | 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=Science/HeatAndTemperature/heat-and-tem... | github_jupyter |
```
%pylab inline
%config InlineBackend.figure_format='retina'
from scipy.integrate import cumtrapz
from scipy.interpolate import interp1d
import seaborn as sns
sns.set_context('notebook')
sns.set_style('ticks')
sns.set_palette('colorblind')
```
Some $\LaTeX$ macros:
$$
\DeclareMathOperator{\erf}{erf}
$$
This simpl... | github_jupyter |
### Common imports
```
import ipywidgets as widgets
import matplotlib.pyplot as plt
import numpy as np
%matplotlib notebook
from IPython.display import display
```
### Spline Policy
```
import sys
import os
sys.path.insert(0, "/home/giuseppe/catkin_ws/src/sampling_based_control/mppi/python")
import numpy as np
im... | github_jupyter |
# CAPÍTULO 04 - Variáveis e Tipos de Dados
```
#1. Faça um programa que leia um número inteiro e o imprima.
a = 10
b = type(a)
print(a)
print(b)
#3. Peça ao usuário para digitar três valores inteiros e imprima a soma deles.
a = input('Informe o primeiro valor inteiro:')
b = input('Informe o segundo valor inteiro:')
c ... | github_jupyter |
```
import pickle
import sys
sys.path.append("../")
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
import tensorflow as tf
from sklearn.metrics import mean_squared_error, mean_absolute_error
from math import sqrt
from surprise import accuracy
from reco_utils.dataset.python_sp... | github_jupyter |
# Bar data
```
from ib_insync import *
util.startLoop()
ib = IB()
ib.connect('127.0.0.1', 7497, clientId=15)
```
## Historical data
To get the earliest date of available bar data the "head timestamp" can be requested:
```
contract = Stock('AMC', 'ISLAND', 'USD')
ib.reqHeadTimeStamp(contract, whatToShow='TRADES', ... | github_jupyter |
Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.
- Author: Sebastian Raschka
- GitHub Repository: https://github.com/rasbt/deeplearning-models
```
%load_ext watermark
%watermark -a 'Sebastian Raschka' -v -p torch
```
# Model... | github_jupyter |
## <strong>Introduction of linear Algebra using NumPy library</strong>
NumPy, which stands for Numerical Python, is a library consisting of multidimensional array objects and a collection of routines for processing those arrays. Using NumPy, mathematical and logical operations on arrays can be performed.It also has fun... | github_jupyter |
# Indexing and Slicing
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Indexing-and-Slicing" data-toc-modified-id="Indexing-and-Slicing-1"><span class="toc-item-num">1 </span>Indexing and Slicing</a></span><ul class="toc-item"><li><span><a hr... | github_jupyter |
```
from pyspark import SparkConf, SparkContext
from pyspark.sql import SparkSession
from pyspark.sql import *
from pyspark.sql.types import *
from pyspark.sql.functions import udf
from pyspark.sql.functions import *
from pyspark.sql.window import Window
NoneType = type(None)
import os
import socket
import hashlib
impo... | github_jupyter |
## Facial Filters
Using your trained facial keypoint detector, you can now do things like add filters to a person's face, automatically. In this optional notebook, you can play around with adding sunglasses to detected face's in an image by using the keypoints detected around a person's eyes. Checkout the `images/` di... | github_jupyter |
## Closed-loop batch, constrained BO in BoTorch with qEI and qNEI
In this tutorial, we illustrate how to implement a simple Bayesian Optimization (BO) closed loop in BoTorch.
In general, we recommend for a relatively simple setup (like this one) to use Ax, since this will simplify your setup (including the amount of ... | github_jupyter |
<a href="https://colab.research.google.com/github/Tessellate-Imaging/monk_v1/blob/master/study_roadmaps/1_getting_started_roadmap/5_update_hyperparams/2_data_params/1)%20Change%20batch%20sizes%20from%20default%20state.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open ... | github_jupyter |
# Introduction to optimization
The basic components
* The objective function (also called the 'cost' function)
```
import numpy as np
objective = np.poly1d([1.3, 4.0, 0.6])
print(objective)
```
* The "optimizer"
```
import scipy.optimize as opt
x_ = opt.fmin(objective, [3])
print("solved: x={}".format(x_))
%matplo... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Qcodes-example-with-Keysight-B1500-Semiconductor-Parameter-Analyzer" data-toc-modified-id="Qcodes-example-with-Keysight-B1500-Semiconductor-Parameter-Analyzer-1"><span class="toc-item-num">1 </sp... | github_jupyter |
```
### This file contains the implementation of the Support Vector Machine Classification on the
### Digit Recongition Dataset.
### Below are the libraries used for the implementation.
### @Author: Chaitanya Sri Krishna Lolla.
from sklearn import svm
import csv
import numpy as np
## TLoading the training Dataset into... | github_jupyter |
<!--NAVIGATION-->
< [Installation](Installation.ipynb) | [Index](Index.ipynb) | [Catalog](Catalog.ipynb) >
<a href="https://colab.research.google.com/github/simonscmap/pycmap/blob/master/docs/API.ipynb"><img align="left" src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open in Colab" title="Open and... | 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/Pyth... | github_jupyter |
```
import pandas as pd
import numpy as np
# TIMES
rng = pd.date_range('2016 Jul 1', periods = 10, freq = 'D')
rng
# Which of these formats DON'T work?
#'2016 Jul 1', '7/1/2016', '1/7/2016', 'July 1, 2016', '2016-07-01', '2016/07/01'
rng = pd.date_range('7/1/2016', periods = 10, freq = 'D')
rng
rng = pd.date_range('1/7... | github_jupyter |
<a href="https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/assignments/assignment_yourname_class2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# T81-558: Applications of Deep Neural Networks
* Instructor: [Jeff... | github_jupyter |
<a href="https://colab.research.google.com/github/Tessellate-Imaging/Monk_Object_Detection/blob/master/example_notebooks/inference_engine/Object%20Detection%20-%20EfficientDet%20Pytorch.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Installation
... | github_jupyter |
# Introduction to py-tedopa
This package consists of different files, each serving a different purpose.
**tedopa.py** provides functions to first map the Hamiltonian of an open quantum system, linearly coupled to an environment of continuous bosonic modes, to a one dimensional chain and then perform time evolution on... | github_jupyter |
```
#@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 the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,... | github_jupyter |
# Table of Contents
<p><div class="lev1 toc-item"><a href="#Intelligent-Agents" data-toc-modified-id="Intelligent-Agents-1"><span class="toc-item-num">1 </span>Intelligent Agents</a></div><div class="lev2 toc-item"><a href="#Agents-and-Environments" data-toc-modified-id="Agents-and-Environments-11"><span cl... | github_jupyter |
<small><small><i>
All the IPython Notebooks in this **Python Examples** series by Dr. Milaan Parmar are available @ **[GitHub](https://github.com/milaan9/90_Python_Examples)**
</i></small></small>
# Python Program to Find Hash of File
In this example, you'll learn to find the hash of a file and display it.
To unders... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Objectives" data-toc-modified-id="Objectives-1"><span class="toc-item-num">1 </span>Objectives</a></span><ul class="toc-item"><li><span><a href="#Agenda" data-toc-modified-id="Agenda-1.1"><span c... | github_jupyter |
# Variability analysis for HBEC IFN experiment
```
import scanpy as sc
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
from pybedtools import BedTool
import pickle as pkl
%matplotlib inline
import itertools
import sys
sys.path.append('/home/ssm-u... | github_jupyter |
```
# !apt-get install libgeos-3.5.0
# !apt-get install libgeos-dev
# !pip install https://github.com/matplotlib/basemap/archive/master.zip
#from google.colab import drive
#drive.mount('/content/drive')
```
# OCO2 - Analyze the CO² plume of Laiwu city
Project for **Data For Good**, season 7.
*By Quentin Kamenda, Be... | github_jupyter |
```
import os
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd
from tqdm.auto import tqdm
import torch
from torch import nn
import gin
import pickle
import io
from sparse_causal_model_learner_rl.trainable.gumbel_switch import With... | github_jupyter |
# DataSynthesizer Usage (independent attribute mode)
> This is a quick demo to use DataSynthesizer in independent attribute mode.
### Step 1 import packages
```
from DataSynthesizer.DataDescriber import DataDescriber
from DataSynthesizer.DataGenerator import DataGenerator
from DataSynthesizer.ModelInspector import M... | github_jupyter |
```
import tensorflow as tf
import csv
import os
print(tf.__version__)
train_data_files = ['data/train-data.csv']
valid_data_files = ['data/valid-data.csv']
test_data_files = ['data/test-data.csv']
HEADER = ['key','x','y','alpha','beta','target']
HEADER_DEFAULTS = [[0], [0.0], [0.0], ['NA'], ['NA'], [0.0]]
NUMERIC_FE... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Filter/filter_range_contains.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_blank" ... | github_jupyter |
This notebook was prepared by [Donne Martin](http://donnemartin.com). Source and license info is on [GitHub](https://github.com/donnemartin/interactive-coding-challenges).
# Solution Notebook
## Problem: Implement insertion sort.
* [Constraints](#Constraints)
* [Test Cases](#Test-Cases)
* [Algorithm](#Algorithm)
* [... | github_jupyter |
# Day2
## Importing heavenly Bodies
```
#time to call the gods
import pandas as pd
from sklearn import preprocessing
import numpy as np
%matplotlib notebook
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import seaborn as sns
```
## Data Loading
```
def preprocess_df(df):
processed_df = df.c... | github_jupyter |
```
# default_exp models.TSTPlus
```
# TSTPlus (Time Series Transformer)
> This is an unofficial PyTorch implementation created by Ignacio Oguiza (timeseriesAI@gmail.com) based on TST (Zerveas, 2020) and Transformer (Vaswani, 2017).
**References:**
This is an unofficial PyTorch implementation by Ignacio Oguiza of ... | github_jupyter |
### Prior and Posterior
$$A \sim \cal{N}(0,s I)$$
$$q(A) =\cal{N}(\mu_A, \Lambda_A)$$
### Likelihood
$$Y|A,X = Y|A^T X$$
```
import tensorflow as tf
import os
import numpy as np
from tqdm import tqdm
from matplotlib import pyplot as plt
from matplotlib import cm
tf.logging.set_verbosity(tf.logging.ERROR)
np.random.se... | github_jupyter |
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