text stringlengths 2.5k 6.39M | kind stringclasses 3
values |
|---|---|
```
from time import time
import secrets
import flickrapi
import requests
import os
import pandas as pd
import pickle
import logging
def get_photos(image_tag):
# setup dataframe for data
raw_photos = pd.DataFrame(columns=['latitude', 'longitude','farm','server','id','secret'])
# initialize api
f... | github_jupyter |
```
import numpy as np
import pandas as pd
from os import makedirs
from os.path import join, exists
#from nilearn.input_data import NiftiLabelsMasker
from nilearn.connectome import ConnectivityMeasure
from nilearn.plotting import plot_anat, plot_roi
import bct
#from nipype.interfaces.fsl import InvWarp, ApplyWarp
impor... | github_jupyter |
# Example Map Plotting
### At the start of a Jupyter notebook you need to import all modules that you will use
```
import pandas as pd
import xarray as xr
import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import griddata
import cartopy
import cartopy.crs as ccrs # For plotting ... | github_jupyter |
# Description
This notebook documents allows the following on a group seven LIFX Tilechain with 5 Tiles
laid out horizontaly as following
T1 [0] [1] [2] [3] [4]
T2 [0] [1] [2] [3] [4]
T3 [0] [1] [2] [3] [4]
T4 [0] [1] [2] [3] [4]
T5 [0] [1] [2] [3] [4]
T6 [0] [1] [2] [3] [4]
T7 [0] [1] [2] [3] [4... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels.api as sm
```
# Import Risk INFORM index
```
path = "C:\\batch8_worldbank\\datasets\\tempetes\\INFORM_Risk_2021.xlsx"
xl = pd.ExcelFile(path)
xl.sheet_names
inform_df = xl.parse(xl.sheet_names[2])
inform_df.columns = info... | github_jupyter |
# Db2 Jupyter Notebook Extensions Tutorial
The SQL code tutorials for Db2 rely on a Jupyter notebook extension, commonly refer to as a "magic" command. The beginning of all of the notebooks begin with the following command which will load the extension and allow the remainder of the notebook to use the %sql magic comm... | github_jupyter |
```
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
df_train = pd.read_excel('wpbc.train.xlsx')
df_test = pd.read_excel('wpbc.test.xlsx')
train = df_train
test = df_test
train.shape
test.shape
train.describe()
import seaborn
import m... | github_jupyter |
# oneDPL- Gamma Correction example
#### Sections
- [Gamma Correction](#Gamma-Correction)
- [Why use buffer iterators?](#Why-use-buffer-iterators?)
- _Lab Exercise:_ [Gamma Correction](#Lab-Exercise:-Gamma-Correction)
- [Image outputs](#Image-outputs)
## Learning Objectives
* Build a sample __DPC++ application__ to p... | github_jupyter |
# DECOMON tutorial #3
## Local Robustness to Adversarial Attacks for classification tasks
## Introduction
After training a model, we want to make sure that the model will give the same output for any images "close" to the initial one, showing some robustness to perturbation.
In this notebook, we start from a class... | github_jupyter |
```
import argparse
import copy
import sys
sys.path.append('../../')
import sopa.src.models.odenet_cifar10.layers as cifar10_models
from sopa.src.models.odenet_cifar10.utils import *
parser = argparse.ArgumentParser()
# Architecture params
parser.add_argument('--is_odenet', type=eval, default=True, choices=[True, Fals... | github_jupyter |
1/14 최초 구현 by 소연
수정 및 테스트 시 본 파일이 아닌 사본 사용을 부탁드립니다.
```
import os, sys
from google.colab import drive
drive.mount('/content/drive')
%cd /content/drive/Shareddrives/KPMG_Ideation
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
from pprint import pprint
from krwordra... | github_jupyter |
```
from pynq import Overlay
from pynq import PL
from pprint import pprint
pprint(PL.ip_dict)
print(PL.timestamp)
ol2 = Overlay('base.bit')
ol2.download()
pprint(PL.ip_dict)
print(PL.timestamp)
PL.interrupt_controllers
PL.gpio_dict
a = PL.ip_dict
for i,j in enumerate(a):
print(i,j,a[j])
a['SEG_rgbled_gpio_Reg']
b =... | github_jupyter |
# Capsule Networks (CapsNets)
Based on the paper: [Dynamic Routing Between Capsules](https://arxiv.org/abs/1710.09829), by Sara Sabour, Nicholas Frosst and Geoffrey E. Hinton (NIPS 2017).
Inspired in part from Huadong Liao's implementation: [CapsNet-TensorFlow](https://github.com/naturomics/CapsNet-Tensorflow).
# In... | github_jupyter |
<a href="https://colab.research.google.com/github/Anmol42/IDP-sem4/blob/main/notebooks/Sig-mu_vae.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import torch
import torchvision
import torch.nn as nn
import matplotlib.pyplot as plt
import torch.... | github_jupyter |
# Analyzing interstellar reddening and calculating synthetic photometry
## Authors
Kristen Larson, Lia Corrales, Stephanie T. Douglas, Kelle Cruz
Input from Emir Karamehmetoglu, Pey Lian Lim, Karl Gordon, Kevin Covey
## Learning Goals
- Investigate extinction curve shapes
- Deredden spectral energy distributions an... | github_jupyter |
```
# Checkout www.pygimli.org for more examples
%matplotlib inline
```
# 2D ERT modeling and inversion
```
import matplotlib.pyplot as plt
import numpy as np
import pygimli as pg
import pygimli.meshtools as mt
from pygimli.physics import ert
```
Create geometry definition for the modelling domain.
worldMarker=Tr... | github_jupyter |
This page was created from a Jupyter notebook. The original notebook can be found [here](https://github.com/klane/databall/blob/master/notebooks/parameter-tuning.ipynb). It investigates tuning model parameters to achieve better performance. First we must import the necessary installed modules.
```
import itertools
imp... | github_jupyter |
# PySDDR: An Advanced Tutorial
In the beginner's guide only tabular data was used as input to the PySDDR framework. In this advanced tutorial we show the effects when combining structured and unstructured data. Currently, the framework only supports images as unstructured data.
We will use the MNIST dataset as a sour... | github_jupyter |
```
# Copyright 2021 Google LLC
# Use of this source code is governed by an MIT-style
# license that can be found in the LICENSE file or at
# https://opensource.org/licenses/MIT.
# Author(s): Kevin P. Murphy (murphyk@gmail.com) and Mahmoud Soliman (mjs@aucegypt.edu)
```
<a href="https://opensource.org/licenses/MIT" t... | github_jupyter |
# TimeEval shared parameter optimization result analysis
```
# Automatically reload packages:
%load_ext autoreload
%autoreload 2
# imports
import json
import warnings
import pandas as pd
import numpy as np
import scipy as sp
import plotly.offline as py
import plotly.graph_objects as go
import plotly.figure_factory as ... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc" style="margin-top: 1em;"><ul class="toc-item"><li><span><a href="#label-identity-hairstyle" data-toc-modified-id="label-identity-hairstyle-1"><span class="toc-item-num">1 </span>label identity hairstyle</a></span></li><li><span><a href=... | github_jupyter |
```
import numpy as np
import pandas as pd
```
### loading dataset
```
data = pd.read_csv("student-data.csv")
data.head()
data.shape
type(data)
```
### Exploratory data analysis
```
import matplotlib.pyplot as plt
import seaborn as sns
a = data.plot()
data.info()
data.isnull().sum()
a = sns.heatmap(data.isnull(),cm... | github_jupyter |
<a href="https://colab.research.google.com/github/NataliaDiaz/colab/blob/master/MI203-td2_tree_and_forest.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# TD: prédiction du vote 2016 aux Etats-Unis par arbres de décisions et méthodes ensemblistes
... | github_jupyter |
<a href="https://colab.research.google.com/github/Laelapz/Some_Tests/blob/main/BERTimbau.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Tem caracteres em chinês? Pq eles pegam a maior distribuição do dataset???
Tirado do Twitter? (Alguns nomes/sob... | github_jupyter |
# The Binomial Distribution
This notebook is part of [Bite Size Bayes](https://allendowney.github.io/BiteSizeBayes/), an introduction to probability and Bayesian statistics using Python.
Copyright 2020 Allen B. Downey
License: [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativ... | github_jupyter |
```
# Import libraries
import numpy as np
import pandas as pd
import sklearn as sk
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties # for unicode fonts
import psycopg2
import sys
import datetime as dt
import mp_utils as mp
from sklearn.pipeline import Pipeline
# use... | github_jupyter |
# Fairseq in Amazon SageMaker: Pre-trained English to French translation model
In this notebook, we will show you how to serve an English to French translation model using pre-trained model provided by the [Fairseq toolkit](https://github.com/pytorch/fairseq)
## Permissions
Running this notebook requires permissions... | github_jupyter |
```
import codecs
from itertools import *
import numpy as np
from sklearn import svm
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn import tree
from sklearn import model_selection
from sklearn.model_selection import train_test_split
from sklearn.ensemble impo... | github_jupyter |
```
# -*- coding: utf-8 -*-
"""
EVCで変換する.
詳細 : https://pdfs.semanticscholar.org/cbfe/71798ded05fb8bf8674580aabf534c4dbb8bc.pdf
Converting by EVC.
Check detail : https://pdfs.semanticscholar.org/cbfe/71798ded05fb8bf8674580abf534c4dbb8bc.pdf
"""
from __future__ import division, print_function
import os
from shutil imp... | github_jupyter |
<CENTER>
<header>
<h1>Pandas Tutorial</h1>
<h3>EuroScipy, Erlangen DE, August 24th, 2016</h3>
<h2>Joris Van den Bossche</h2>
<p></p>
Source: <a href="https://github.com/jorisvandenbossche/pandas-tutorial">https://github.com/jorisvandenbossche/pandas-tutorial</a>
</header>
</CENTER>
Two data files a... | github_jupyter |
# Closed-Loop Evaluation
In this notebook you are going to evaluate Urban Driver to control the SDV with a protocol named *closed-loop* evaluation.
**Note: this notebook assumes you've already run the [training notebook](./train.ipynb) and stored your model successfully (or that you have stored a pre-trained one).**
... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
from quantumnetworks import MultiModeSystem, plot_full_evolution
import numpy as np
```
# Trapezoidal Method
```
# params stored in txt
sys = MultiModeSystem(params={"dir":"data/"})
x_0 = np.array([1,0,0,1])
ts = np.linspace(0, 10, 101)
X = sys.trapezoidal(x_0, ts)
fig, ax = plo... | github_jupyter |
___
<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
___
# Principal Component Analysis
Let's discuss PCA! Since this isn't exactly a full machine learning algorithm, but instead an unsupervised learning algorithm, we will just have a lecture on this topic, but no full machine learnin... | github_jupyter |
## osumapper: create osu! map using Tensorflow and Colab
### -- For osu!mania game mode --
For mappers who don't know how this colaboratory thing works:
- Press Ctrl+Enter in code blocks to run them one by one
- It will ask you to upload .osu file and audio.mp3 after the third block of code
- .osu file needs to have ... | github_jupyter |
<a href="https://colab.research.google.com/github/magenta/ddsp/blob/master/ddsp/colab/tutorials/0_processor.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
##### Copyright 2020 Google LLC.
Licensed under the Apache License, Version 2.0 (the "Licen... | github_jupyter |
### Tutorial: Parameterized Hypercomplex Multiplication (PHM) Layer
#### Author: Eleonora Grassucci
Original paper: Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with 1/n Parameters.
Aston Zhang, Yi Tay, Shuai Zhang, Alvin Chan, Anh Tuan Luu, Siu Cheung Hui, Jie Fu.... | github_jupyter |
```
import sys
import pandas as pd
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
sys.path.append('../Scripts')
from Data_Processing import DataProcessing
from tensorflow import keras
from keras.callbacks import ModelCheckpoint
from keras.models import load_model
from keras import back... | github_jupyter |
# Amazon Fine Food Reviews Analysis
Data Source: https://www.kaggle.com/snap/amazon-fine-food-reviews <br>
EDA: https://nycdatascience.com/blog/student-works/amazon-fine-foods-visualization/
The Amazon Fine Food Reviews dataset consists of reviews of fine foods from Amazon.<br>
Number of reviews: 568,454<br>
Numb... | github_jupyter |
# Conditional statements - part 1
## Motivation
All the previous programs are based on a pure sequence of statements. After the start of the program the statements are
executed step by step and the program ends afterwards. However, it is often necessary that parts of a program are
only executed under certain conditio... | github_jupyter |
# Using a random forest for demographic model selection
In Schrider and Kern (2017) we give a toy example of demographic model selection via supervised machine learning in Figure Box 1. Following a discussion on twitter, Vince Buffalo had the great idea of our providing a simple example of supervised ML in population g... | github_jupyter |
#### Copyright 2017 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 writin... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import interpn
import os
import config
import utils
# Read measured profiles
measuredDoseFiles10 = ['./Measured/Method3/PDD1_10x10.dat','./Measured/Method3/PDD2_10x10.dat',
'./Measured/Method3/PROF1_10x10_14mm.dat','.... | github_jupyter |
```
import numpy as np
import theano
import theano.tensor as T
import lasagne
import os
#thanks @keskarnitish
```
# Agenda
В предыдущем семинаре вы создали (или ещё создаёте - тогда марш доделывать!) {вставьте имя монстра}, который не по наслышке понял, что люди - негодяи и подлецы, которым неведом закон и справедлив... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import sklearn
import numpy as np
import scipy as sp
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import seaborn as sns
#from viz import viz
from bokeh.plotting import figure, show, output_notebook, output_file, save
#from functions import ... | github_jupyter |
```
# Packages
from IPython.display import Image
import rasterio
from rasterio import windows
import skimage
import skimage.io as skio
import json
import skimage.draw
import os
import sys
import pathlib
import math
import itertools
from shutil import copy2
import functools
from skimage import exposure
import matplotlib... | github_jupyter |
# Import packages & Connect the database
```
# Install MYSQL client
pip install PyMySQL
import sklearn
print('The scikit-learn version is {}.'.format(sklearn.__version__))
%load_ext autoreload
%autoreload 2
%matplotlib inline
import numpy as np
import pandas as pd
import datetime as dt
# Connect to database
import p... | github_jupyter |
# Archive data
The Wellcome archive sits in a collections management system called CALM, which follows a rough set of standards and guidelines for storing archival records called [ISAD(G)](https://en.wikipedia.org/wiki/ISAD(G). The archive is comprised of _collections_, each of which has a hierarchical set of series, s... | github_jupyter |
<a href="https://colab.research.google.com/github/lakigigar/Caltech-CS155-2021/blob/main/psets/set1/set1_prob3.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Problem 3
Use this notebook to write your code for problem 3 by filling in the sections... | github_jupyter |
In this tutorial you'll learn all about **histograms** and **density plots**.
# Set up the notebook
As always, we begin by setting up the coding environment. (_This code is hidden, but you can un-hide it by clicking on the "Code" button immediately below this text, on the right._)
```
#$HIDE$
import pandas as pd
im... | github_jupyter |
# IMPORTING THE LIBRARIES
```
import os
import pandas as pd
import pickle
import numpy as np
import seaborn as sns
from sklearn.datasets import load_files
from keras.utils import np_utils
import matplotlib.pyplot as plt
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D
from keras.layers import Drop... | github_jupyter |
```
# Import that good good
import sys
import os
sys.path.append('/Users/kolbt/Desktop/ipython/diam_files')
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import math
from IPython.display import display
from collections import OrderedDict
pd.options.display.max_rows = 2
import matplotlib.colors... | github_jupyter |
# TensorFlow Neural Machine Translation on Cloud TPUs
This tutorial demonstrates how to translate text using a LSTM Network from one language to another (from English to German in this case). We will work with a dataset that contains pairs of English-German phrases. Given a sequence of words in English, we train a mod... | github_jupyter |
```
# We tweak the style of this notebook a little bit to have centered plots.
from IPython.core.display import HTML
HTML("""
<style>
.output_png {
display: table-cell;
text-align: center;
vertical-align: middle;
}
</style>
""");
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
warning... | github_jupyter |
# Notebook Goal & Approach
## Goal
For each FERC 714 respondent that reports hourly demand as an electricity planning area, create a geometry representing the geographic area in which that electricity demand originated. Create a separate geometry for each year in which data is available.
## Approach
* Use the `eia_co... | github_jupyter |
# Part 0: Mining the web
Perhaps the richest source of openly available data today is [the Web](http://www.computerhistory.org/revolution/networking/19/314)! In this lab, you'll explore some of the basic programming tools you need to scrape web data.
> **Note.** The Vocareum platform runs in a cloud-based environment... | github_jupyter |
# Main notebook for battery state estimation
```
import numpy as np
import pandas as pd
import scipy.io
import math
import os
import ntpath
import sys
import logging
import time
import sys
from importlib import reload
import plotly.graph_objects as go
import tensorflow as tf
from tensorflow import keras
from tensorf... | github_jupyter |
```
%matplotlib inline
"""
The data set in this example represents 1059 songs from various countries obtained
from the UCI Machine Learning library. Various features of the audio tracks have been
extracted, and each track has been tagged with the latitude and longitude of the capital
city of its country of origin. ... | github_jupyter |
##### Copyright 2020 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0
# 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 writ... | github_jupyter |
```
import open3d as o3d
import numpy as np
import os
import sys
# monkey patches visualization and provides helpers to load geometries
sys.path.append('..')
import open3d_tutorial as o3dtut
# change to True if you want to interact with the visualization windows
o3dtut.interactive = not "CI" in os.environ
```
# RGBD ... | github_jupyter |
# Using Interrupts and asyncio for Buttons and Switches
This notebook provides a simple example for using asyncio I/O to interact asynchronously with multiple input devices. A task is created for each input device and coroutines used to process the results. To demonstrate, we recreate the flashing LEDs example in the ... | github_jupyter |
# Making Simple Plots
## Objectives
+ Learn how to make a simple 1D plot in Python.
+ Learn how to find the maximum/minimum of a function in Python.
We will use [Problem 4.B.2](https://youtu.be/w-IGNU2i3F8) of the lecturebook as a motivating example.
We find that the moment of the force $\vec{F}$ about point A is:
$$... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Plot-Validation-and-Train-loss" data-toc-modified-id="Plot-Validation-and-Train-loss-1"><span class="toc-item-num">1 </span>Plot Validation and Train loss</a></span></li><li><span><a href="#Extra... | github_jupyter |
# 6. Hidden Markov Models with Theano and TensorFlow
In the last section we went over the training and prediction procedures of Hidden Markov Models. This was all done using only vanilla numpy the Expectation Maximization algorithm. I now want to introduce how both `Theano` and `Tensorflow` can be utilized to accomplis... | github_jupyter |
```
# use python eval sometimes. great trickdefining a class and operator overloading
import aoc
f = open('input.txt')
lines = [line.rstrip('\n') for line in f]
lines[0]
# part 1
def evaluate(line):
ans = 0
firstop = None
operator = None
wait = 0
for i, ch in enumerate(line):
if wait > 0: # ... | github_jupyter |
# Creating Provenance an Example Using a Python Notebook
```
import prov, requests, pandas as pd, io, git, datetime, urllib
from prov.model import ProvDocument
```
## Initialising a Provenance Document
First we use the prov library to create a provenance and initialise it with some relevant namespaces that can be us... | github_jupyter |
# Cython in Jupyter notebooks
To use cython in a Jupyter notebook, the extension has to be loaded.
```
%load_ext cython
```
## Pure Python
To illustrate the performance difference between a pure Python function and a cython implementation, consider a function that computes the list of the first $k_{\rm max}$ prime ... | github_jupyter |
```
!pip install plotly
```
<a href="https://plotly.com/python/" target="_blank">Plotly's</a> Python graphing library makes interactive, publication-quality graphs. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar ch... | github_jupyter |
### Mit kellene tudni?
#### 1. Megfogalmazni egy programozási problémát <!-- .element: class="fragment" -->
#### 1. Számításelmélet értelmét elmagyarázni <!-- .element: class="fragment" -->
#### 1. Lebontani egy komplex problémát egyszerűbbekre <!-- .element: class="fragment" -->
#### 1. Megérteni egy leírt progr... | github_jupyter |
# Módulo 2: Scraping con Selenium
## LATAM Airlines
<a href="https://www.latam.com/es_ar/"><img src="https://i.pinimg.com/originals/dd/52/74/dd5274702d1382d696caeb6e0f6980c5.png" width="420"></img></a>
<br>
Vamos a scrapear el sitio de Latam para averiguar datos de vuelos en funcion el origen y destino, fecha y cabin... | github_jupyter |
## RIHAD VARIAWA, Data Scientist - Who has fun LEARNING, EXPLORING & GROWING
<h1>2D <code>Numpy</code> in Python</h1>
<p><strong>Welcome!</strong> This notebook will teach you about using <code>Numpy</code> in the Python Programming Language. By the end of this lab, you'll know what <code>Numpy</code> is and the <code... | github_jupyter |
```
# Let's keep our notebook clean, so it's a little more readable!
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
```
# Machine learning to predict age from rs-fmri
The goal is to extract data from several rs-fmri images, and use that data as features in a machine learning model. We will integ... | github_jupyter |
<a href="https://colab.research.google.com/github/bhadreshpsavani/ExploringSentimentalAnalysis/blob/main/SentimentalAnalysisWithGPTNeo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## Step1. Import and Load Data
```
!pip install -q pip install gi... | github_jupyter |
```
from collections import OrderedDict
from collections import namedtuple
import numpy as np
from scipy import stats
# R precision
def r_precision(targets, predictions, max_n_predictions=500):
# Assumes predictions are sorted by relevance
# First, cap the number of predictions
predictions = predictions[... | github_jupyter |
# Implementing a CGAN for the Iris data set to generate synthetic data
### Import necessary modules and packages
```
import os
while os.path.basename(os.getcwd()) != 'Synthetic_Data_GAN_Capstone':
os.chdir('..')
from utils.utils import *
safe_mkdir('experiments')
from utils.data_loading import load_raw_dataset
imp... | github_jupyter |
```
import qiskit
import numpy as np, matplotlib.pyplot as plt
import sys
sys.path.insert(1, '../')
import qtm.base, qtm.constant, qtm.nqubit, qtm.onequbit, qtm.fubini_study
num_qubits = 3
num_layers = 2
psi = 2*np.random.rand(2**num_qubits)-1
psi = psi / np.linalg.norm(psi)
qc_origin = qiskit.QuantumCircuit(num_qubits... | github_jupyter |
# Collaborative Filtering on Google Analytics Data
### Learning objectives
1. Prepare the user-item matrix and use it with WALS.
2. Train a `WALSMatrixFactorization` within TensorFlow locally and on AI Platform.
3. Visualize the embedding vectors with principal components analysis.
## Overview
This notebook demonstra... | github_jupyter |
```
import numpy as np
from resonance.nonlinear_systems import SingleDoFNonLinearSystem
```
To apply arbitrary forcing to a single degree of freedom linear or nonlinear system, you can do so with `SingleDoFNonLinearSystem` (`SingleDoFLinearSystem` does not support arbitrary forcing...yet).
Add constants, a generalize... | github_jupyter |
# Fit $k_{ij}$ and $r_c^{ABij}$ interactions parameter of Ethanol and CPME
This notebook has te purpose of showing how to optimize the $k_{ij}$ and $r_c^{ABij}$ for a mixture with induced association.
First it's needed to import the necessary modules
```
import numpy as np
from sgtpy import component, mixture, saft... | github_jupyter |
# Mini Project: 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
Use the code cell below... | github_jupyter |
```
#hide
#skip
! [ -e /content ] && pip install -Uqq fastai # upgrade fastai on colab
#default_exp data.core
#export
from fastai.torch_basics import *
from fastai.data.load import *
#hide
from nbdev.showdoc import *
```
# Data core
> Core functionality for gathering data
The classes here provide functionality for ... | github_jupyter |
<img src="images/logo.jpg" style="display: block; margin-left: auto; margin-right: auto;" alt="לוגו של מיזם לימוד הפייתון. נחש מצויר בצבעי צהוב וכחול, הנע בין האותיות של שם הקורס: לומדים פייתון. הסלוגן המופיע מעל לשם הקורס הוא מיזם חינמי ללימוד תכנות בעברית.">
# <span style="text-align: right; direction: rtl; float: r... | 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 |
## Fundamentals, introduction to machine learning
The purpose of these guides is to go a bit deeper into the details behind common machine learning methods, assuming little math background, and teach you how to use popular machine learning Python packages. In particular, we'll focus on the Numpy and PyTorch libraries... | github_jupyter |
# The Graph Data Access
In this notebook, we read in the data that was generated and saved as a csv from the [TheGraphDataSetCreation](TheGraphDataSetCreation.ipynb) notebook.
Goals of this notebook are to obtain:
* Signals, states, event and sequences
* Volatility metrics
* ID perceived shocks (correlated with an... | github_jupyter |
```
import pandas as pd
import numpy as np
import pickle
BASEDIR_MIMIC = '/Volumes/MyData/MIMIC_data/mimiciii/1.4'
def get_note_events():
n_rows = 100000
icd9_code = pd.read_csv(f"{BASEDIR_MIMIC}/DIAGNOSES_ICD.csv", index_col = None)
# create the iterator
noteevents_iterator = pd.read_csv(
f"{... | github_jupyter |
# Make Corner Plots of Posterior Distributions
This file allows me to quickly and repeatedly make the cornor plot to examin the results of the MCMC analsys
```
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import pandas as pd
from astropy.table import Table
import corner
# import seaborn
matplo... | github_jupyter |
```
import radical.analytics as ra
import radical.pilot as rp
import radical.utils as ru
import radical.entk as re
import os
from glob import glob
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import cm
import csv
import pandas as pd
import json
import matplotlib as mpl
mpl.rcParams['text.uset... | github_jupyter |
# Running, Debugging, Testing & Packaging
```
!code ./1-helloconnectedworld
```
Let's look at the key parts of our app:
**package.json**
This defines all contributions: commands, context menus, UI, everything!
```json
"activationEvents": [
// Use "*" to start on application start. If contributing comm... | github_jupyter |
# Convolutional Autoencoder
Sticking with the MNIST dataset, let's improve our autoencoder's performance using convolutional layers. Again, loading modules and the data.
```
%matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import i... | github_jupyter |
Practice geospatial aggregations in geopandas before writing them to .py files
```
%load_ext autoreload
%autoreload 2
import sys
sys.path.append('../utils')
import wd_management
wd_management.set_wd_root()
import geopandas as gp
import pandas as pd
import requests
res = requests.get('https://services5.arcgis.com/GfwWN... | github_jupyter |
<a href="https://colab.research.google.com/github/prateekjoshi565/Fine-Tuning-BERT/blob/master/Fine_Tuning_BERT_for_Spam_Classification.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Install Transformers Library
```
!pip install transformers
imp... | github_jupyter |
# Run hacked AlphaFold2 on the designed bound states
### Imports
```
%load_ext lab_black
# Python standard library
from glob import glob
import os
import socket
import sys
# 3rd party library imports
import dask
import matplotlib.pyplot as plt
import pandas as pd
import pyrosetta
import numpy as np
import scipy
impo... | github_jupyter |
<a href="https://colab.research.google.com/github/parshwa1999/Map-Segmentation/blob/master/ResNet_RoadTest.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Segmentation of Road from Satellite imagery
## Importing Libraries
```
import warnings
war... | github_jupyter |
## Homework 4
Today we'll start by reproducing the DQN and then try improving it with the tricks we learned on the lecture:
* Target networks
* Double q-learning
* Prioritized experience replay
* Dueling DQN
* Bootstrap DQN
```
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
# If you are runni... | github_jupyter |
```
dtypes = {
'MachineIdentifier': 'category',
'ProductName': 'category',
'EngineVersion': 'category',
'AppVersion': 'category',
... | github_jupyter |
```
import numpy as np
import nibabel as nb
import matplotlib.pyplot as plt
# helper function to plot 3D NIfTI
def plot_slice (fname):
# Load image
img = nb.load (fname)
data = img.get_data ()
# cut in the middle of brain
cut = int (data.shape[-1]/2) + 10
# plot data
plt.imsh... | github_jupyter |
<font size ='3'>*First, let's read in the data and necessary libraries*<font/>
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from mypy import print_side_by_side
from mypy import display_side_by_side
#https://stackoverflow.com/a/44923103/8067752
%matplotlib inline
pd.... | github_jupyter |
# Appendix
Hao Lu 04/04/2020
In this notebook, we simulated EEG data with the method described in the paper by Bharadwaj and Shinn-Cunningham (2014) and analyzed the data with the toolbox proposed in the same paper.
The function was modifed so the values of thee variables within the function can be extracted and stu... | github_jupyter |
# Statistics
## Introduction
In this chapter, you'll learn about how to do statistics with code. We already saw some statistics in the chapter on probability and random processes: here we'll focus on computing basic statistics and using statistical tests. We'll make use of the excellent [*pingouin*](https://pingouin-... | github_jupyter |
```
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.pyplot import savefig
import cv2
np.set_printoptions(threshold=np.inf)
num_images = 3670
dataset = []
for i in range(1, num_images+1):
img = cv2.imread("color_images/color_" +str(i) +".jpg" )
dataset.append(np.array... | github_jupyter |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.