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<img src="../../images/banners/python-basics.png" width="600"/>
# <img src="../../images/logos/python.png" width="23"/> Python Program Lexical Structure
You have now covered Python variables, operators, and data types in depth, and you’ve seen quite a bit of example code. Up to now, the code has consisted of short i... | github_jupyter |
# Load Data From Snowflake
## Overview
<a href="https://www.snowflake.com/" target='_blank' rel='noopener'>Snowflake</a> is a data platform built for the cloud that allows for fast SQL queries. This ... | github_jupyter |
<a href="https://colab.research.google.com/github/PyTorchLightning/lightning-flash/blob/master/flash_notebooks/image_classification.ipynb" target="_parent">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
In this notebook, we'll go over the basics of lightning Flash b... | github_jupyter |
# Symbolic Regression
This example combines neural differential equations with regularised evolution to discover the equations
$\frac{\mathrm{d} x}{\mathrm{d} t}(t) = \frac{y(t)}{1 + y(t)}$
$\frac{\mathrm{d} y}{\mathrm{d} t}(t) = \frac{-x(t)}{1 + x(t)}$
directly from data.
**References:**
This example appears as ... | github_jupyter |
```
# %cd /Users/Kunal/Projects/TCH_CardiacSignals_F20/
from numpy.random import seed
seed(1)
import numpy as np
import os
import matplotlib.pyplot as plt
import tensorflow
tensorflow.random.set_seed(2)
from tensorflow import keras
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.regularizers ... | github_jupyter |
# Web Data Extraction (1)
by Dr Liang Jin
- Step 1: access crawler.idx files from SEC EDGAR
- Step 2: re-write crawler data to csv files
- Step 3: retrieve 10K filing information including URLs
- Step 4: read text from html
## Step 0: Setup...
```
# import packages as usual
import os, requests, csv, webbrowser
from ... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
from astropy import units as u
from astroduet.bbmag import bb_abmag_fluence
from astroduet.image_utils import construct_image, find
from astroduet.config import Telescope
from astroduet.background import background_pixel_rate
fr... | github_jupyter |
```
# Copyright 2020 Google LLC
#
# 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 agreed to in writi... | github_jupyter |
<a href="https://colab.research.google.com/github/txusser/Master_IA_Sanidad/blob/main/Modulo_2/2_3_3_Extraccion_de_caracteristicas.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Extracción de características
## Análisis de la componente principa... | github_jupyter |
# Multi-linear regression: how many variables?
[](https://github.com/eabarnes1010/course_objective_analysis/tree/main/code)
[](https://colab.research.google.com/github/eabar... | github_jupyter |
## Keras implementation of https://phillipi.github.io/pix2pix
```
import os
os.environ['KERAS_BACKEND']='theano' # can choose theano, tensorflow, cntk
os.environ['THEANO_FLAGS']='floatX=float32,device=cuda,optimizer=fast_run,dnn.library_path=/usr/lib'
#os.environ['THEANO_FLAGS']='floatX=float32,device=cuda,optimizer=f... | github_jupyter |
```
import gc
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import torch
import torch.nn as nn
import torchvision
# To view tensorboard metrics
# tensorboard --logdir=logs --port=6006 --bind_all
from torch.utils.tensorboard import SummaryWriter
from functools import partial
from evol... | github_jupyter |
# Exploring Data with Python
A significant part of a a data scientist's role is to explore, analyze, and visualize data. There's a wide range of tools and programming languages that they can use to do this; and of of the most popular approaches is to use Jupyter notebooks (like this one) and Python.
Python is a flexi... | github_jupyter |
<a href="https://colab.research.google.com/github/mohd-faizy/CAREER-TRACK-Data-Scientist-with-Python/blob/main/Police_Activity_data_for_analysis.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
---
<strong>
<h1 align='center'>Preparing the Po... | github_jupyter |
# Workshop: Deep Learning 3
Outline
1. Regularization
2. Hand-Written Digits with Convolutional Neural Networks
3. Advanced Image Classification with Convolutional Neural Networks
Source: Deep Learning With Python, Part 1 - Chapter 4
## 1. Regularization
To prevent a model from learning misleading or irrelevant ... | github_jupyter |
# ORF MLP
Trying to fix bugs.
NEURONS=128 and K={1,2,3}.
```
import time
def show_time():
t = time.time()
print(time.strftime('%Y-%m-%d %H:%M:%S %Z', time.localtime(t)))
show_time()
PC_TRAINS=8000
NC_TRAINS=8000
PC_TESTS=8000
NC_TESTS=8000
RNA_LEN=1000
MAX_K = 3
INPUT_SHAPE=(None,84) # 4^3 + 4^2 + 4... | github_jupyter |
```
import google.datalab.bigquery as bq
import numpy as np
from sklearn.metrics import mean_squared_error, mean_absolute_error
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
#training price data
training = bq.Query('''
Select date_utc,price
from Energy.MarketPT
where date_utc between '2015... | github_jupyter |
# Inter-annotator agreement between the first 10 annotators of WS-353
Measured in Kappa and Rho:
- against the gold standard which is the mean of all annotators, as described in Hill et al 2014 (footnote 6)
- against each other
Using Kohen's kappa, which is binary, so I average across pairs of annotators.
```
%cd... | github_jupyter |
# Using AI planning to explore data science pipelines
```
from __future__ import print_function
import sys
import os
import types
sys.path.append(os.path.abspath(os.path.join(os.getcwd(), "../grammar2lale")))
# Clean output directory where we store planning and result files
os.system('rm -rf ../output')
os.system('m... | github_jupyter |
This Jupyter notebook details theoretically the architecture and the mechanism of the Convolutional Neural Network (ConvNet) step by step. Then, we implement the CNN code for multi-class classification task using pytorch. <br>
The notebook was implemented by <i>Nada Chaari</i>, PhD student at Istanbul Technical Univer... | github_jupyter |
# Read in catalog information from a text file and plot some parameters
## Authors
Adrian Price-Whelan, Kelle Cruz, Stephanie T. Douglas
## Learning Goals
* Read an ASCII file using `astropy.io`
* Convert between representations of coordinate components using `astropy.coordinates` (hours to degrees)
* Make a spherica... | github_jupyter |
```
import os
import cv2
import numpy as np
#import layers
import matplotlib.pyplot as plt
# credits to https://towardsdatascience.com/lines-detection-with-hough-transform-84020b3b1549
import matplotlib.lines as mlines
# ist a,b == m, c
def line_detection_non_vectorized(image, edge_image, num_rhos=100, num_thetas=10... | github_jupyter |
# Activity 02
```
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from keras.models import Sequential
from keras.layers import Dense
from tensorflow import random
import matplotlib.pyplot as plt
import matplotlib
%matplotlib ... | github_jupyter |
# Autoregressions
This notebook introduces autoregression modeling using the `AutoReg` model. It also covers aspects of `ar_select_order` assists in selecting models that minimize an information criteria such as the AIC.
An autoregressive model has dynamics given by
$$ y_t = \delta + \phi_1 y_{t-1} + \ldots + \phi_... | github_jupyter |
```
import os
import pandas as pd
import math
import nltk
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
import re
from nltk.tokenize import WordPunctTokenizer
import pickle
def load_csv_as_df(file_name, sub_directories, col_name=None):
'''
Load any csv as a pandas dat... | github_jupyter |
# Example: CanvasXpress bubble Chart No. 4
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/bubble-4.html
This example is generated using the reproducible JSON obtained from the above page an... | github_jupyter |
## Imports
```
import os
import sys
%env CUDA_VISIBLE_DEVICES=0
%matplotlib inline
import pickle
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from matplotlib.ticker import FormatStrFormatter
import tensorflow as tf
root_path = os.path.dirname(os.path.dirn... | github_jupyter |
# Data description & Problem statement:
This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constr... | github_jupyter |
```
# Select TensorFlow 2.0 environment (works only on Colab)
%tensorflow_version 2.x
# Install wandb (ignore if already done)
!pip install wandb
# Authorize wandb
!wandb login
# Imports
from tensorflow.keras.models import *
from tensorflow.keras.layers import *
from wandb.keras import WandbCallback
import tensorflow a... | github_jupyter |
# Integrate 3rd party transforms into MONAI program
This tutorial shows how to integrate 3rd party transforms into MONAI program.
Mainly shows transforms from `BatchGenerator`, `TorchIO`, `Rising` and `ITK`.
```
! pip install batchgenerators==0.20.1
! pip install torchio==0.16.21
! pip install rising==0.2.0
! pip i... | github_jupyter |
```
import matplotlib,aplpy
from astropy.io import fits
from general_functions import *
import matplotlib.pyplot as plt
font = {'size' : 14, 'family' : 'serif', 'serif' : 'cm'}
plt.rc('font', **font)
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['lines.linewidth'] = 1
plt.rcParams['axes.linewidth'] = 1... | github_jupyter |
# Global Segment Overflow
- recall function pointers are pointers that store addresses of functions/code
- see [Function-Pointers notebook](./Function-Pointers.ipynb) for a review
- function pointers can be overwritten using overflow techniques to point to different code/function
## Lucky 7 game
- various luck-ba... | 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 |
# Deep Learning with PyTorch Step-by-Step: A Beginner's Guide
# Chapter 1
```
try:
import google.colab
import requests
url = 'https://raw.githubusercontent.com/dvgodoy/PyTorchStepByStep/master/config.py'
r = requests.get(url, allow_redirects=True)
open('config.py', 'wb').write(r.content)
excep... | github_jupyter |
# Transfer Learning
## Imports and Version Selection
```
# TensorFlow ≥2.0 is required for this notebook
import tensorflow as tf
from tensorflow import keras
assert tf.__version__ >= "2.0"
# check if GPU is available as this notebook will be very slow without GPU
if not tf.test.is_gpu_available():
print("No GPU w... | github_jupyter |
# Example of simple use of active learning API
Compare 3 query strategies: random sampling, uncertainty sampling, and active search.
Observe how we trade off between finding targets and accuracy.
# Imports
```
import warnings
warnings.filterwarnings(action='ignore', category=RuntimeWarning)
from matplotlib import py... | github_jupyter |
<a href="https://colab.research.google.com/github/aruanalucena/Car-Price-Prediction-Machine-Learning/blob/main/Car_Price_Prediction_.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# **Car Price Prediction with python**.
# **Previsão de carrro com P... | github_jupyter |
```
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="ticks")
%matplotlib inline
import numpy as np
np.random.seed(sum(map(ord, "axis_grids")))
```
```
tips = sns.load_dataset("tips")
g = sns.FacetGrid(tips, col="time")
```
```
g = sns.FacetGrid(tips, col="time")
g.map(plt.hist, "tip");
```
```
g ... | github_jupyter |
**handson用資料としての注意点**
普通、同じセル上で何度も試行錯誤するので、最終的に上手くいったセルしか残らず、失敗したセルは残りませんし、わざわざ残しません。
今回はhandson用に 試行・思考過程を残したいと思い、エラーやミスが出ても下のセルに進んで処理を実行するようにしています。
notebookのセル単位の実行ができるからこそのやり方かもしれません。良い。
（下のセルから文は常体で書きます。）
kunai (@jdgthjdg)
---
# ここまでの処理を整理して、2008〜2019のデータを繋いでみる
## xls,xlsxファイルを漁る
```
from pathlib import Pa... | github_jupyter |
**Outline of Steps**
+ Initialization
+ Download COCO detection data from http://cocodataset.org/#download
+ http://images.cocodataset.org/zips/train2014.zip <= train images
+ http://images.cocodataset.org/zips/val2014.zip <= validation images
+ http://images.cocodataset.... | github_jupyter |
## Launching Spark
Spark's Python console can be launched directly from the command line by `pyspark`. SparkSession can be found by calling `spark` object. The Spark SQL console can be launced by `spark-sql`. We will experiment with these in the upcoming sessions.
If we have `pyspark` and other required packages inst... | github_jupyter |
2018-10-26
실용주의 파이썬 101 - Part 2
[김영호](https://www.linkedin.com/in/danielyounghokim/)
난이도 ● ● ◐ ○ ○
# Data Structures
- Immutable vs. Mutable
- Immutable: `tuple`
- Mutable: `list`, `set`, `dict`
- Mutable Container 설명 순서
- 초기화
- 추가/삭제
- 특정 값 접근(access)
- 정렬
## `tuple`
### 초기화
```
seq = ()
t... | github_jupyter |
<a href="https://colab.research.google.com/github/mmoghadam11/ReDet/blob/master/train_UCAS_AOD.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
from google.colab import drive
drive.mount('/content/drive')
#باشد tesla t4 باید
#اگر نبود در بخش ران ... | github_jupyter |
## Importing the library
```
import numpy as np
```
## Data Types
### Scalars
```
# creating a scalar, we use the 'array' in order to create any type of data type e.g. scalar, vector, matrix
s = np.array(5)
# visualizing the shape of a scalar, in the example below it returns an empty tuple which is normal
# a scala... | github_jupyter |
```
#Load dependencies
import numpy as np
import pandas as pd
pd.options.display.float_format = '{:,.1e}'.format
import sys
sys.path.insert(0, '../../statistics_helper')
from CI_helper import *
from excel_utils import *
```
# Estimating the total biomass of marine deep subsurface archaea and bacteria
We use our best ... | github_jupyter |
# Probabilistic Matrix Factorization for Making Personalized Recommendations
```
%matplotlib inline
import numpy as np
import pandas as pd
import pymc3 as pm
from matplotlib import pyplot as plt
plt.style.use("seaborn-darkgrid")
print(f"Running on PyMC3 v{pm.__version__}")
```
## Motivation
So you are browsing for... | github_jupyter |
<a href="https://colab.research.google.com/github/josearangos/PDI/blob/Colab/Colab_Class/binarySegmentation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import cv2
import numpy as np
import matplotlib.pyplot as plt
from google.colab.patches i... | github_jupyter |
# Housing economy, home prices and affordibility
Alan Greenspan in 2014 pointed out that there was never a recovery from recession
without improvements in housing construction. Here we examine some relevant data,
including the Case-Shiller series, and derive an insightful
measure of the housing economy, **hscore**,... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Tutorials/Keiko/glad_alert.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_blank" h... | github_jupyter |
# Convert verse ranges of genres to TF verse node features
```
import collections
import pandas as pd
from tf.fabric import Fabric
from tf.compose import modify
from tf.app import use
A = use('bhsa', hoist=globals())
genre_ranges = pd.read_csv('genre_ranges.csv')
genre_ranges
```
# Compile data & sanity checks
```
#... | github_jupyter |
## 1. Importing the required libraries for EDA
```
import pandas as pd
import numpy as np # For mathematical calculations
import seaborn as sns # For data visualization
import matplotlib.pyplot as plt # For plotting graphs
%matplotlib inline
sns.set(color_codes=True)
im... | github_jupyter |
<a href="https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_08_4_bayesian_hyperparameter_opt.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
**Module 8:... | github_jupyter |
```
name = '2015-12-11-meeting-summary'
title = 'Introducing Git'
tags = 'git, github, version control'
author = 'Denis Sergeev'
from nb_tools import connect_notebook_to_post
from IPython.core.display import HTML
html = connect_notebook_to_post(name, title, tags, author)
```
Today we talked about git and its function... | github_jupyter |
# Basic Bayesian Linear Regression Implementation
```
# Pandas and numpy for data manipulation
import pandas as pd
import numpy as np
# Matplotlib and seaborn for visualization
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
# Linear Regression to verify implementation
from sklearn.linear_... | github_jupyter |
# Text recognition
We have a set of water meter images. We need to get each water meter’s readings. We ask performers to look at the images and write down the digits on each water meter.
To get acquainted with Toloka tools for free, you can use the promo code **TOLOKAKIT1** on $20 on your [profile page](https://toloka... | github_jupyter |
# Multi-Layer Perceptron, MNIST
---
In this notebook, we will train an MLP to classify images from the [MNIST database](http://yann.lecun.com/exdb/mnist/) hand-written digit database.
The process will be broken down into the following steps:
>1. Load and visualize the data
2. Define a neural network
3. Train the model... | github_jupyter |
# 붓꽃(Iris) 품종 데이터 예측하기
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#DataFrame" data-toc-modified-id="DataFrame-1"><span class="toc-item-num">1 </span>DataFrame</a></span></li><li><span><a href="#Train/Test-데이터-나누어-학습하기" data-toc-modified-i... | github_jupyter |
# Density estimation demo
Here we demonstrate how to use the ``inference.pdf`` module for estimating univariate probability density functions from sample data.
```
from numpy import linspace, zeros, exp, log, sqrt, pi
from numpy.random import normal, exponential
from scipy.special import erfc
import matplotlib.pyplot... | github_jupyter |
# Assignment 3: RTRL
Implement an RNN with RTRL. The ds/dw partial derivative is 2D hidden x (self.n_hidden * self.n_input) instead of 3d.
```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
class RNN(object):
def __init__(self, n_input, n_hidden, n_output):
# init weights and biases... | github_jupyter |
```
from gtts import gTTS
LANG_PATH = '../lang/{0}/speech/{1}.mp3'
tts = gTTS(text='Se ha detectado más de una persona, inténtelo de nuevo con una persona sólo por favor', lang='es', slow=False)
tts.save(LANG_PATH.format('es', 'more_than_one_face'))
tts = gTTS(text='There appears to be more than one person, try again ... | github_jupyter |
<img src="../../images/qiskit-heading.gif" alt="Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook" width="500 px" align="left">
# Getting Started with Qiskit
Here, we provide an overview of working with Qiskit. Qiskit provides the basic building blocks necessary... | github_jupyter |
____
__Universidad Tecnológica Nacional, Buenos Aires__\
__Ingeniería Industrial__\
__Cátedra de Investigación Operativa__\
__Autor: Martín Palazzo__ (Mpalazzo@frba.utn.edu.ar) y __Rodrigo Maranzana__ (Rmaranzana@frba.utn.edu.ar)
____
# Simulación con distribución Exponencial
<h1>Índice<span class="tocSkip"></span></... | github_jupyter |
<p><font size="6"><b>Visualization - Matplotlib</b></font></p>
> *DS Data manipulation, analysis and visualization in Python*
> *May/June, 2021*
>
> *© 2021, Joris Van den Bossche and Stijn Van Hoey (<mailto:jorisvandenbossche@gmail.com>, <mailto:stijnvanhoey@gmail.com>). Licensed under [CC BY 4.0 Creative Commons]... | github_jupyter |
#### The purpose of this notebook is to compare D-REPR with other methods such as KR2RML and R2RML in term of performance
```
import re, numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm_notebook as tqdm
%matplotlib inline
plt.rcParams["figure.figsize"] = (10.0, 8.0) # set default size of plots
plt.rc... | github_jupyter |
```
#Importing necessary libraries
import keras
import numpy as np
import pandas as pd
from keras.applications import VGG16, inception_v3, resnet50, mobilenet
from keras import models
from keras import layers
from keras import optimizers
from sklearn.metrics import classification_report, confusion_matrix
import matplo... | github_jupyter |
```
%matplotlib inline
```
# Tuning a scikit-learn estimator with `skopt`
Gilles Louppe, July 2016
Katie Malone, August 2016
Reformatted by Holger Nahrstaedt 2020
.. currentmodule:: skopt
If you are looking for a :obj:`sklearn.model_selection.GridSearchCV` replacement checkout
`sphx_glr_auto_examples_sklearn-grids... | github_jupyter |
# <center>MobileNet - Pytorch
# Step 1: Prepare data
```
# MobileNet-Pytorch
import argparse
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim.lr_scheduler import StepLR
from torchvision import datasets, transforms
from torch.autograd i... | github_jupyter |
```
from database.market import Market
from database.strategy import Strategy
from extractor.tiingo_extractor import TiingoExtractor
from preprocessor.model_preprocessor import ModelPreprocessor
from preprocessor.predictor_preprocessor import PredictorPreprocessor
from modeler.modeler import Modeler
from datetime impor... | github_jupyter |
<a href="https://colab.research.google.com/github/katie-chiang/ARMultiDoodle/blob/master/Copy_of_Welcome_To_Colaboratory.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
<p><img alt="Colaboratory logo" height="45px" src="/img/colab_favicon.ico" align... | github_jupyter |
# Visulizing spatial information - California Housing
This demo shows a simple workflow when working with geospatial data:
* Obtaining a dataset which includes geospatial references.
* Obtaining a desired geometries (boundaries etc.)
* Visualisation
In this example we will make a simple **proportional symbols m... | github_jupyter |
# Build sentence/paragraph level QA application from python with Vespa
> Retrieve paragraph and sentence level information with sparse and dense ranking features
We will walk through the steps necessary to create a question answering (QA) application that can retrieve sentence or paragraph level answers based on a co... | github_jupyter |
# Quadtrees iterating on pairs of neighbouring items
A quadtree is a tree data structure in which each node has exactly four children. It is a particularly efficient way to store elements when you need to quickly find them according to their x-y coordinates.
A common problem with elements in quadtrees is to detect pa... | github_jupyter |
```
import sys
import os
# path_to_script = os.path.dirname(os.path.abspath(__file__))
path_to_imcut = os.path.abspath("..")
sys.path.insert(0, path_to_imcut)
path_to_imcut
import imcut
imcut.__file__
import numpy as np
import scipy
import scipy.ndimage
# import sed3
import matplotlib.pyplot as plt
```
## Input da... | github_jupyter |
```
from edahelper import *
import sklearn.naive_bayes as NB
import sklearn.linear_model
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.pipeline import Pipeline
from sklearn.linear... | github_jupyter |
## Sleep analysis, using Passive Infrared (PIR) data, in 10sec bins from a single central PIR, at 200-220mm above the cage floor. Previously EEG-telemetered animals allow direct comparison of sleep scored by direct and non-invasive methods.
### 1st setup analysis environment:
```
import numpy as np # calculations
i... | github_jupyter |
### What is Matplotlib?
Matplotlib is a plotting library for the Python, Pyplot is a matplotlib module which provides a MATLAB-like interface. Matplotlib is designed to be as usable as MATLAB, with the ability to use Python, and the advantage of being free and open-source.
#### What does Matplotlib Pyplot do?
Matplot... | 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 |
This script is based on instructions given in [this lesson](https://github.com/HeardLibrary/digital-scholarship/blob/master/code/scrape/pylesson/lesson2-api.ipynb).
## Import libraries and load API key from file
The API key should be the only item in a text file called `flickr_api_key.txt` located in the user's home... | github_jupyter |
```
import matplotlib.pyplot as plt
import torch
import gpytorch
import time
import numpy as np
%matplotlib inline
import pickle
import finite_ntk
%pdb
class ExactGPModel(gpytorch.models.ExactGP):
# exact RBF Gaussian process class
def __init__(self, train_x, train_y, likelihood, model, use_linearstrategy=Fals... | github_jupyter |
Precipitation Metrics (consecutive dry days, rolling 5-day precip accumulation, return period)
```
! pip install xclim
%matplotlib inline
import xarray as xr
import numpy as np
import matplotlib.pyplot as plt
import os
import pandas as pd
from datetime import datetime, timedelta, date
import dask
import dask.array a... | github_jupyter |
# IElixir - Elixir kernel for Jupyter Project
<img src="logo.png" title="Hosted by imgur.com" style="margin: 0 0;"/>
---
## Google Summer of Code 2015
> Developed by [Piotr Przetacznik](https://twitter.com/pprzetacznik)
> Mentored by [José Valim](https://twitter.com/josevalim)
---
## References
* [Elixir language... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
A = np.random.randn(4,3)
B = np.sum(A, axis = 1, keepdims = True)
B.shape
```
# Data Loading
```
data = pd.read_csv("ner_dataset.csv", encoding="latin1")
data = data.drop(['POS'], axis =1)
data.head()
plt.style.use("ggplot")
data = pd.read_csv... | github_jupyter |
# Logistic Regression
Notebook version: 2.0 (Nov 21, 2017)
2.1 (Oct 19, 2018)
Author: Jesús Cid Sueiro (jcid@tsc.uc3m.es)
Jerónimo Arenas García (jarenas@tsc.uc3m.es)
Changes: v.1.0 - First version
v.1.1 - Typo correction. Prepared for slide presentation
... | github_jupyter |
TSG086 - Run `top` in all containers
====================================
Steps
-----
### Instantiate Kubernetes client
```
# Instantiate the Python Kubernetes client into 'api' variable
import os
from IPython.display import Markdown
try:
from kubernetes import client, config
from kubernetes.stream import ... | github_jupyter |
### Irrigation model input file prep
This code prepares the final input file to the irrigation (agrodem) model. It extracts all necessary attributes to crop locations. It also applies some name fixes as needed for the model to run smoothly.The output dataframe is exported as csv and ready to be used in the irrigation ... | github_jupyter |
<a href="https://colab.research.google.com/github/KristynaPijackova/Radio-Modulation-Recognition-Networks/blob/main/Radio_Modulation_Recognition_Networks.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Radio Modulation Recognition Networks
---
*... | github_jupyter |
# Comparison FFTConv & SpatialConv
In this notebook, we compare the speed and the error of utilizing fft and spatial convolutions.
In particular, we will:
* Perform a forward and backward pass on a small network utilizing different types of convolution.
* Analyze their speed and their error response w.r.t. spatial c... | 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 |
## [Experiments] Uncertainty Sampling with a 1D Gaussian Process as model
First, we define a prior probablility for a model.
The GaussianRegressor approximates this model using an optimization method (probably similar to EM) for a given data input.
The resulting model has a mean and a certainty.
We use these to determ... | github_jupyter |
# Probability Distribution:
In [probability theory](https://en.wikipedia.org/wiki/Probability_theory) and [statistics](https://en.wikipedia.org/wiki/statistics), a probability distribution is a [mathematical function](https://en.wikipedia.org/wiki/Function_(mathematics)) that, stated in simple terms, can be thought o... | github_jupyter |
<h1>02 Pandas</h1>
$\newcommand{\Set}[1]{\{#1\}}$
$\newcommand{\Tuple}[1]{\langle#1\rangle}$
$\newcommand{\v}[1]{\pmb{#1}}$
$\newcommand{\cv}[1]{\begin{bmatrix}#1\end{bmatrix}}$
$\newcommand{\rv}[1]{[#1]}$
$\DeclareMathOperator{\argmax}{arg\,max}$
$\DeclareMathOperator{\argmin}{arg\,min}$
$\DeclareMathOperator{\... | github_jupyter |
# Tutorial 01: Running Sumo Simulations
This tutorial walks through the process of running non-RL traffic simulations in Flow. Simulations of this form act as non-autonomous baselines and depict the behavior of human dynamics on a network. Similar simulations may also be used to evaluate the performance of hand-design... | github_jupyter |
### Data Frame Plots
documentation: http://pandas.pydata.org/pandas-docs/stable/visualization.html
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')
```
The plot method on Series and DataFrame is just a simple wrapper around plt.plot()
If the index consists of dates, ... | github_jupyter |
<a href="http://landlab.github.io"><img style="float: left" src="../media/landlab_header.png"></a>
# The deAlmeida Overland Flow Component
<hr>
<small>For more Landlab tutorials, click here: <a href="https://landlab.readthedocs.io/en/latest/user_guide/tutorials.html">https://landlab.readthedocs.io/en/latest/user_guid... | github_jupyter |
# Modes of the Ball-Channel Pendulum Linear Model
```
import numpy as np
import numpy.linalg as la
import matplotlib.pyplot as plt
from resonance.linear_systems import BallChannelPendulumSystem
%matplotlib widget
```
A (almost) premade system is available in `resonance`. The only thing missing is the function that ca... | github_jupyter |
Steane code fault tolerance encoding scheme b
=======================================
1. Set up two logical zero for Steane code based on the parity matrix in the book by Nielsen MA, Chuang IL. Quantum Computation and Quantum Information, 10th Anniversary Edition. Cambridge University Press; 2016. p. 474
2. Set up ... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from datetime import time
import geopandas as gpd
from shapely.geometry import Point, LineString, shape
```
## Load Data
```
df = pd.read_csv(r'..\data\processed\trips_custom_variables.csv', dtype = {'VORIHORAINI':str, 'VDE... | github_jupyter |
# U.S. Border Patrol Nationwide Apprehensions by Citizenship and Sector
**Data Source:** [CBP Apprehensions](https://www.cbp.gov/sites/default/files/assets/documents/2021-Aug/USBORD~3.PDF) <br>
**Download the Output:** [here](../data/extracted_data/)
## Overview
The source PDF is a large and complex PDF with varying... | github_jupyter |
# FAQs for Regression, MAP and MLE
* So far we have focused on regression. We began with the polynomial regression example where we have training data $\mathbf{X}$ and associated training labels $\mathbf{t}$ and we use these to estimate weights, $\mathbf{w}$ to fit a polynomial curve through the data:
\begin{equatio... | github_jupyter |
# 7. Overfitting Prevention
## Why do we need to solve overfitting?
- To increase the generalization ability of our deep learning algorithms
- Able to make predictions well for out-of-sample data
## Overfitting and Underfitting: Examples
- **_This is an example from scikit-learn's webs... | github_jupyter |
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