text stringlengths 2.5k 6.39M | kind stringclasses 3
values |
|---|---|
# Generador Unix
Utilizando el generador UNIX de números aleatorios, pero con los coeficientes del generador Visual Basic, programe una serie de 60 números aleatorios en hoja de cálculo, verificando que, a igual semilla corresponde igual serie.
Utilice como “Blanco” una serie del mismo tamaño generada con una macro en ... | github_jupyter |
## 2. Multi Layer Perceptron
### 1) import modules
```
import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
from modules import multi_layer_perceptron
```
### 2) define placeholder for INPUT & LABELS
```
INPUT = tf.placeholder(tf.float32, [None, 28*28])
LABELS = tf... | github_jupyter |
# Subpockets to target residue(s)
We explore the distance of the `kissim` subpocket centers to their target residues.
```
%load_ext autoreload
%autoreload 2
from pathlib import Path
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from opencadd.databases.klifs import setup... | github_jupyter |
```
# import os
# os.environ['LIBRARY_PATH'] = os.environ['LD_LIBRARY_PATH'] = '/home/apanin/cuda-8.0/lib64'
# os.environ['PATH'] = "/usr/local/cuda-8.0/bin/:/home/apanin/cuda-8.0/lib64:"+os.environ['PATH']
# %env THEANO_FLAGS=device=cuda0,gpuarray.preallocate=0.5,floatX=float32
# import theano
# import theano.tensor a... | github_jupyter |
```
import skimage.io as sio
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import diff_classifier.aws as aws
from skimage.filters import roberts, sobel, scharr, prewitt, median, rank
from skimage import img_as_ubyte
from skimage.morphology import erosion, dilation, opening, closing, white_topha... | github_jupyter |
```
import statsmodels.formula.api as smf
import matplotlib.pyplot as plt
import fastreg as fr
from fastreg import I, R, C
%config InlineBackend.figure_format = 'retina'
%matplotlib inline
```
### Generate Data
```
models = ['linear', 'poisson', 'negbin', 'zinf_poisson', 'zinf_negbin']
data = fr.dataset(N=1_000_000, ... | github_jupyter |
# Chapter 8
## Question 10
Using boosting to predict `Salary` in the `Hitters` data set
```
import statsmodels.api as sm
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import sklearn.model_selection
import sklearn.ensemble
import sklearn.tree
import sklearn.metrics
impo... | github_jupyter |
```
#Import Library
#Preprocessing
import os
import cv2
import pandas as pd
import numpy as np
import tensorflow as tf
import scipy.ndimage as ndi
from random import shuffle
from scipy.misc import imread, imresize
from scipy.io import loadmat
#Model
from keras.preprocessing.image import ImageDataGenerator
from sklear... | github_jupyter |
# SIT742: Modern Data Science
**(Week 01: Programming Python)**
---
- Materials in this module include resources collected from various open-source online repositories.
- You are free to use, change and distribute this package.
Prepared by **SIT742 Teaching Team**
---
# Session 1B - Control Flow, File usage, and... | github_jupyter |
# Periodic Signals
*This Jupyter notebook is part of a [collection of notebooks](../index.ipynb) in the bachelors module Signals and Systems, Comunications Engineering, Universität Rostock. Please direct questions and suggestions to [Sascha.Spors@uni-rostock.de](mailto:Sascha.Spors@uni-rostock.de).*
## Spectrum
Peri... | github_jupyter |
# Homework 07
### Preparation...
Run this code from the lecture to be ready for the exercises below!
```
import glob
import os.path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import datasets, linear_model, ensemble, neural_network
from sklearn.metrics import mean_squared_e... | github_jupyter |
```
import torch
import numpy as np
import matplotlib.pyplot as plt
import os
import sys; sys.path.append("../src")
from models.cgans import AirfoilAoACEGAN, AirfoilAoAGenerator
from train_final_cebgan import read_configs, assemble_new_gan
from utils.dataloader import AirfoilDataset, NoiseGenerator
from torch.util... | github_jupyter |
# Single Shot Object Detection
SSD (Single Shot Multi-box Detection) is detecting objects in images using a single deep neural network. This tutorial use a model provided from [TensorFlow](https://github.com/tensorflow/examples/blob/master/lite/examples/object_detection/android/README.md).
```
import (
"log"
... | github_jupyter |
```
import os
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")
from sklearn.metrics import roc_curve
from sklearn.metrics import roc_auc_score
from sklearn.metrics import confusion_matrix, ConfusionMa... | github_jupyter |
<a href="https://colab.research.google.com/github/loosak/pysnippets/blob/master/Graphs.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Exploring the World of Graphs
John Paul Mueller and Luca Massaron: Algorithms For Dummies®, 2nd Edition
Graphs... | github_jupyter |
<h1 align="center">PROGRAMACIÓN DE COMPUTADORES </h1>
<h2 align="center">UNIVERSIDAD EAFIT</h2>
<h3 align="center">MEDELLÍN - COLOMBIA </h3>
<h2 align="center">Sesión 12 - Ecosistema Python - Matplotlib</h2>
## Instructor:
> <strong> *Carlos Alberto Álvarez Henao, I.C. Ph.D.* </strong>
## Matplotlib
> Primero hay... | github_jupyter |
# Air Quality Monitoring
### Import/Install packages
```
#!pip install git+https://github.com/datakaveri/iudx-python-sdk
#!pip install geojsoncontour
#!pip install voila
#!pip install voila-gridstack
# Use !voila airQualityMonitoring.ipynb --enable_nbextensions=True --template=gridstack to launch dashboard or use jup... | github_jupyter |
# Complex Numbers as Vectors
We saw that a complex number $z = a + bi$ is equivalent to (and therefore can be represented as) the ordered tuple $(a; b)$, which can be plotted in a 2D space. So, complex numbers and 2D points are equivalent. What is more, we can draw a vector from the origin of the coordinate plane to ou... | github_jupyter |
# Optimizing Performance Using Numba & Cython
## Numba & Cython: What are they?
At a high level, Numba and Cython are both modules that make your Python code run faster. This means we can have the quick prototyping and iteration that Python is known for, while getting the speed we expect from programs written in C. Th... | github_jupyter |
# Jupyter
We'll be using Jupyter for all of our examples—this allows us to run python in a web-based notebook, keeping a history of input and output, along with text and images.
For Jupyter help, visit:
https://jupyter.readthedocs.io/en/latest/content-quickstart.html
We interact with python by typing into _cel... | github_jupyter |
```
#default_exp nn_utils
#export
import torchvision
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt
#export
def c_imshow(img):
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))
plt.show()
#export nn_utils
class Flatten(nn... | github_jupyter |
In this lab session we will learn how to pre-process feature vectors using numpy. For this purpose, lets create 10 feature vectors that have 5 features. We use numpy.random for generating these examples.
```
import numpy
X = numpy.random.randn(10, 5)
```
Lets print this matrix X where each row is a feature vector.
`... | github_jupyter |
```
from __future__ import division, print_function, unicode_literals
%matplotlib inline
%config InlineBackend.print_figure_kwargs = {'dpi' : 150}
import numpy as np
import qinfer as qi
import matplotlib.pyplot as plt
plt.style.use('ggplot-rq')
plt.rcParams['savefig.frameon'] = False
```
## Example: Impovrishment ##
... | github_jupyter |
```
import os
import pandas as pd
import numpy as np
import warnings
import pickle
import math
from collections import OrderedDict, Counter
from copy import deepcopy
from Bio.PDB import PDBParser, ResidueDepth, PDBIO, Superimposer, Select
from Bio.SeqUtils import seq3
from Bio.PDB.vectors import calc_angle
from Bio i... | github_jupyter |
# Tanzanian Ministry of Water Dataset Modeling
**Import libraries**
```
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.experimental import enable_iterative_imputer
from sklearn.impute import KNNImputer, Iterative... | github_jupyter |
# Special care should be taken with missing data on this problem.
Missing data shall never be filled in the target variable, or the results evaluation would be corrupted. That is a risk on this problem, if things are done without care, because the target variable and the features are the same, only time-shifted.
Firs... | github_jupyter |
```
# Connect to Google Drive
from google.colab import drive
drive.mount('/content/drive')
# ls /content/drive/MyDrive/
# Copy the dataset from Google Drive to local
!cp "/content/drive/MyDrive/CBIS_DDSM.zip" .
!unzip -qq CBIS_DDSM.zip
!rm CBIS_DDSM.zip
cbis_path = 'CBIS_DDSM'
# Import libraries
%tensorflow_version ... | github_jupyter |
# The Pasta Production Problem
This tutorial includes everything you need to set up IBM Decision Optimization CPLEX Modeling for Python (DOcplex), build a Mathematical Programming model, and get its solution by solving the model with IBM ILOG CPLEX Optimizer.
Table of contents:
- [Describe the business problem](#D... | github_jupyter |
# Cell Tower Coverage
## Objective and Prerequisites
In this example, we'll solve a simple covering problem: how to build a network of cell towers to provide signal coverage to the largest number of people possible. We'll construct a mathematical model of the business problem, implement this model in the Gurobi Pytho... | github_jupyter |
# Thesis thoughts and tests, week 1
This is a summary of my work to date. The first cell contains all the helper functions and such, and can be safely skipped.
```
import skimage.io
from matplotlib import pyplot as plt
import cairocffi as cairo
import math, random
import numpy as np
from IPython.display import Image
... | github_jupyter |
## General Exploratory Data Analysi
## General Exploratory Data Analysi
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import math
import os.path
from datetime import datetime
from datetime import date
from dateutil import parser
#import pickle
#import asyncio
from datetime import timedelt... | github_jupyter |
*This notebook is part of course materials for CS 345: Machine Learning Foundations and Practice at Colorado State University.
Original versions were created by Ben Sattelberg and Asa Ben-Hur.
The content is availabe [on GitHub](https://github.com/asabenhur/CS345).*
*The text is released under the [CC BY-SA license](... | github_jupyter |
# Enter State Farm
```
from __future__ import division, print_function
%matplotlib inline
#path = "data/state/"
path = "data/state/sample/"
from importlib import reload # Python 3
import utils; reload(utils)
from utils import *
from IPython.display import FileLink
batch_size=64
```
## Setup batches
```
batches = ge... | github_jupyter |
Basics
---
In this example, we'll go over the basics of atom and reside selection in MDTraj. First let's load up an example trajectory.
```
from __future__ import print_function
import mdtraj as md
traj = md.load('ala2.h5')
print(traj)
```
We can also more directly find out how many atoms or residues there are by us... | github_jupyter |
# Continuous Control
---
Congratulations for completing the second project of the [Deep Reinforcement Learning Nanodegree](https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893) program! In this notebook, you will learn how to control an agent in a more challenging environment, where the goal ... | github_jupyter |
```
import sys
sys.path.append("functions/")
from datastore import DataStore
from searchgrid import SearchGrid
from crossvalidate import CrossValidate
from sklearn.dummy import DummyClassifier
from sklearn.metrics import f1_score
from sklearn.metrics import roc_auc_score
from sklearn.linear_model import LogisticRegress... | github_jupyter |
# Rank Classification using BERT on Amazon Review dataset
## Introduction
In this tutorial, you learn how to train a rank classification model using [Transfer Learning](https://en.wikipedia.org/wiki/Transfer_learning). We will use a pretrained DistilBert model to train on the Amazon review dataset.
## About the data... | github_jupyter |
```
import numpy as np
import pandas as pd
import seaborn as sns
import nibabel as nib
import matplotlib.pyplot as plt
from nilearn import plotting
from os.path import join
from glob import glob
from matplotlib.colors import LinearSegmentedColormap
sns.set_context('talk')
def grab_corr(subjects, nodes, task, conditio... | github_jupyter |
```
import pandas as pd
from sklearn import model_selection
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import cohen_kappa_score
from sklearn.svm... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/FeatureCollection/distance.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 |
# Resample Data
## Pandas Resample
You've learned about bucketing to different periods of time like Months. Let's see how it's done. We'll start with an example series of days.
```
import numpy as np
import pandas as pd
dates = pd.date_range('10/10/2018', periods=11, freq='D')
close_prices = np.arange(len(dates))
cl... | github_jupyter |
# Linear Support Vector Regressor with PowerTransformer
This Code template is for the Classification task using Support Vector Regressor (SVR) based on the Support Vector Machine algorithm with Power Transformer as Feature Transformation Technique in a pipeline.
### Required Packages
```
import warnings
import nump... | github_jupyter |
##### Copyright 2021 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 |
### Egeria Hands-On Lab
# Welcome to the Understanding Server Configuration Lab
## Introduction
Egeria is an open source project that provides open standards and implementation libraries to connect tools, catal... | github_jupyter |
# Obtaining priority data from WIPO PatentScope
**Version**: Dec 16 2020
Reference: [Web Scraping using Selenium and Python](https://www.scrapingbee.com/blog/selenium-python/)
## Import the package.
```
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
from selenium.common.excepti... | github_jupyter |
# Derived generators
```
import tohu
from tohu.v6.primitive_generators import *
from tohu.v6.derived_generators import *
from tohu.v6.generator_dispatch import *
from tohu.v6.utils import print_generated_sequence, make_dummy_tuples
from datetime import datetime
#tohu.v6.logging.logger.setLevel('DEBUG')
print(f'Tohu ve... | github_jupyter |
```
!git clone https://github.com/PhysicsTeacher13/NFT-Image-Generator.git
cd nft-image-generator/
from PIL import Image
from IPython.display import display
import random
import json
# Each image is made up a series of traits
# The weightings for each trait drive the rarity and add up to 100%
background = ["Blue", "... | github_jupyter |
<div align="center">
<h1><img width="30" src="https://madewithml.com/static/images/rounded_logo.png"> <a href="https://madewithml.com/">Made With ML</a></h1>
Applied ML · MLOps · Production
<br>
Join 30K+ developers in learning how to responsibly <a href="https://madewithml.com/about/">deliver value</a> with ML.
... | github_jupyter |
```
import os
import requests
import json
import numpy as np
from dotenv import load_dotenv
import pandas as pd
from pycoingecko import CoinGeckoAPI
cg = CoinGeckoAPI()
from coinapi_rest_v1.restapi import CoinAPIv1
import datetime, sys
load_dotenv()
coin_api_key = os.getenv("COIN_API_KEY2")
coin_api_key2 = os.getenv(... | github_jupyter |
# Exploring violations related to farming activity
To run this notebook, load SDWIS csv data files into the folder ``../../../data/sdwis/SDWIS``
```
import os
import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
STATE_CODE = 'VT'
DATA_DIR = '../../../../data'
SDWIS_DIR = os.pat... | github_jupyter |
```
import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pymc as pm
import scipy.stats as stats
%config InlineBackend.figure_format = 'retina'
az.style.use('arviz-darkgrid')
```
#### Code 2.1
```
ways = np.array([0, 3, 8, 9, 0])
ways / ways.sum()
```
#### Code 2.2
$$Pr(w \mid n, p) = \frac{... | github_jupyter |
# Final Prediction Model and Results
Now that we have evaluated our model, we can use all the data and build a model to predict values of the future. In this case, we predict Electricity Consumption and Generation in year 2020 in Germany.
## Import Libraries
```
import numpy as np
import pandas as pd
import matplotl... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# AML P... | github_jupyter |
# Data Mining (Bing News)
### Required Packages
```
#!pip install selenium
#!pip install beautfulsoup
#!pip install webdriver_manager
#!pip install pandas
#!pip install numpy
#!pip install matplotlib
from bs4 import BeautifulSoup
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from sele... | github_jupyter |
```
import os
import time
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision import transforms
import matplotlib.pyplot as plt
from PIL import Image
import os
os.environ['... | github_jupyter |
# Introduction to data in NCEDC
1. seismic network data
2. earthquake catalog
3. earthquake focal mechanism catalog
4. earthquake phase/polarity data
5. earthquake waveform data
## Prepare modules, file directory
```
import pandas as pd
import urllib3
# dfkds
# url for different datasets
url_station = "https://ncedc.... | github_jupyter |
# Quick Start
This tutorial show how to create a scikit-criteria `Data` structure, and how to feed them inside different multicriteria decisions algorithms.
## Conceptual Overview
The multicriteria data are really complex thing; mostly because you need at least 2 totally disconected vectors to decribe your problem:... | github_jupyter |
<h1 style="text-align: center;">Data Mining Project 1: Frequent Pattern & Association Rule</h1>
<p style="text-align:center;">
呂伯駿<br>
Q56074085<br>
NetDB<br>
National Cheng Kung University<br>
pclu@netdb.csie.ncku.edu.tw
</p>
## 1. Introduction
Frequent Pattern & Association Rule 是 Data mining 中的... | github_jupyter |
# Dataset - US
```
import pandas as pd
```
## Initialize
```
srcUS = "./time_series_covid19_confirmed_US.csv"
dest = "./time_series_covid19_confirmed_US_transformed.csv"
stateCoordinates = {
"Wisconsin": (44.500000, -89.500000),
"West Virginia": (39.000000, -80.500000),
"Vermont": (44.000000, -72.699997)... | github_jupyter |
# Binary Image Denoising
#### *Jiaolong Xu (GitHub ID: [Jiaolong](https://github.com/Jiaolong))*
#### This notebook is written during GSoC 2014. Thanks Shell Hu and Thoralf Klein for taking time to help me on this project!
This notebook illustrates how to use shogun structured output learning framework for binary im... | github_jupyter |
# Text Clustering with Sentence-BERT
```
!pip3 install sentence-transformers
!pip install datasets
import pandas as pd, numpy as np
import torch, os
from datasets import load_dataset
dataset = load_dataset("amazon_polarity",split="train")
dataset
corpus=dataset.shuffle(seed=42)[:10000]['content']
pd.Series([len(e.spl... | github_jupyter |
# Scientific Languages
**Julia**, **Python**, and **R** are three open source languages used for scientific computing today (2020).
They all come with a simplistic command line interface where you type in a statement and it is executed immediately, this is the **REPL**, short for read-evaluate-print-loop.
The REPL is ... | github_jupyter |
# Converting RMarkdown files to SoS notebooks
* **Difficulty level**: easy
* **Time need to lean**: 10 minutes or less
* **Key points**:
* `sos convert file.Rmd file.ipynb` converts a Rmarkdown file to SoS notebook. A `markdown` kernel is used to render markdown text with in-line expressions.
* `sos convert file.R... | github_jupyter |
# Visualization
PySwarms implements tools for visualizing the behavior of your swarm. These are built on top of `matplotlib`, thus rendering charts that are easy to use and highly-customizable.
In this example, we will demonstrate three plotting methods available on PySwarms:
- `plot_cost_history`: for plotting the co... | github_jupyter |
## KNN Classifier
The model predicts the severity of the landslide (or if there will even be one) within the next 2 days, based on weather data from the past 5 days.
Binary Classification yielded a maximum accuracy of 77.53%. Severity Classification (multiple classes) was around 56%.
```
import pandas as pd
import num... | github_jupyter |
# Machine Learning Using Random Forests
*Curtis Miller*
A **random forest** is a collection of decision trees that each individually make a prediction for an observation. Each tree is formed from a random subset of the training set. The majority decision among the trees is then the predicted value of an observation. R... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import pyqg
year = 24*60*60*360.
# Set up a model which will run for 20 years and start averaging after 10 years.
# There are lots of parameters that can be specified as keyword arguments
# but we are just using the defaults.
m = pyqg.QGModel(tm... | github_jupyter |
## Mergesort
Implement mergesort.
### Approach
Mergesort is a divide-and-conquer algorithm. We divide the array into two sub-arrays, recursively call the function and pass in the two halves, until each sub-array has one element. Since each sub-array has only one element, they are all sorted. We then merge each sub-arr... | github_jupyter |
```
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
```
# Lecture 3B - Data Integration*
# Table of Contents
* [Lecture 3B - Data Integration*](#Lecture-12---Data-Integration*)
*
* [Content](#Content)
* [Learning Outcomes](#Learning-Outcom... | github_jupyter |
```
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics.pairwise import cosine_similarity
from surprise import Reader, Dataset, SVD
import warnings; warnings.simplefilter('ignore')
```
## Data Preprocessing and Visualization
```
df= pd. ... | github_jupyter |
<a href="https://colab.research.google.com/github/anjali0503/Internship-Projects/blob/main/Iris_ML_DTC.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## ***ANJALI RAMLOLARAKH PANDEY***
**TSF GRIP SPARKS FOUNDATION**
Prediction using Decision Tre... | github_jupyter |
# State preparation with circuit optimization
We want to create a circuit that produces the Bell state $\vert\Phi^+\rangle = \dfrac{\vert00\rangle + \vert11\rangle}{\sqrt 2}$. We already know that this state can be produced by a circuit containing a Hadamard gate on the first qubit followed by a CNOT gate [[1](https:/... | github_jupyter |
```
## Necessary packages
import numpy as np
import pandas as pd
import itertools
import math
import time
import os
import glob
import copy
## Signal Processing
from scipy import signal
import scipy.io.wavfile as wavfile
import scipy.io
import librosa
# from scipy.fftpack import fft
# import adaptfilt as adf
## Visu... | github_jupyter |
```
%matplotlib inline
```
`Learn the Basics <intro.html>`_ ||
`Quickstart <quickstart_tutorial.html>`_ ||
`Tensors <tensorqs_tutorial.html>`_ ||
**Datasets & DataLoaders** ||
`Transforms <transforms_tutorial.html>`_ ||
`Build Model <buildmodel_tutorial.html>`_ ||
`Autograd <autogradqs_tutorial.html>`_ ||
`Optimizat... | github_jupyter |
# Saving a model trained with a tabular dataset in fast.ai
- Example of saving and reloading a model trained with a tabular dataset in fast.ai.
- This notebook is an extension of
The example shown here is adapted from the paper by Howard and Gugger https://arxiv.org/pdf/2002.04688.pdf
# Prepare the notebook and inge... | github_jupyter |
# V0.1.6 - System Identification Using Adaptative Filters
Example created by Wilson Rocha Lacerda Junior
## Generating 1 input 1 output sample data
The data is generated by simulating the following model:
$y_k = 0.2y_{k-1} + 0.1y_{k-1}x_{k-1} + 0.9x_{k-1} + e_{k}$
If *colored_noise* is set to True:
$e_{k} = 0.8... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
from keras.layers import Input, Dense, Convolution2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from keras import regularizers
import numpy as np
```
### Reference :
* Blog : building autoencoders in keras : https://blog.keras.io/building-autoencod... | github_jupyter |
# 1. Python basics
This chapter only gives a short introduction to Python to make the explanations in the following chapters more understandable. A detailed description would be too extensive and would go beyond the scope of this tutorial. Take a look at https://docs.python.org/tutorial/.
Now let's take our first ste... | github_jupyter |
If you're opening this Notebook on colab, you will probably need to install 🤗 Transformers and 🤗 Datasets. Uncomment the following cell and run it.
```
#! pip install datasets transformers[sentencepiece] sacrebleu
```
If you're opening this notebook locally, make sure your environment has an install from the last v... | github_jupyter |
**Loading Data and creating benchmark model**
```
# Defining the path to the Github repository
file_url = 'https://raw.githubusercontent.com/PacktWorkshops/The-Data-Science-Workshop/master/Chapter17/Datasets/bank-full.csv'
# Loading data using pandas
import pandas as pd
bankData = pd.read_csv(file_url,sep=";")
bankD... | 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/SourcesOfEnergy/resources-and-r... | github_jupyter |
```
from pytorch_vision_classifier.pytorch_dataset_samplers import ImbalancedDatasetSampler
from pytorch_vision_classifier.pytorch_dataset_preparation import PytorchDatasetPreparation
from pytorch_vision_classifier.pytorch_device_manager import DeviceManager
from pytorch_vision_classifier.pytorch_model_training import ... | github_jupyter |
# Welcome to jupyter notebooks!
### Congratulations, the hardest step is always the first one. This exercise is designed to help you get personal with the format of jupyter notebooks, as well as learn how data is accessed and manipulated in python.
```
name = "Liz" #type your name before the pound sign, make sure yo... | github_jupyter |
```
import cvxopt
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.append('../../pyutils')
import metrics
```
# Introduction
The predictor $G(X)$ takes values in a discrete set $\mathbb{G}$. The input space is divided into a collection regions labeled according to their clasification.
The bou... | github_jupyter |
<small><small><i>
Introduction to Python for Bioinformatics - available at https://github.com/kipkurui/Python4Bioinformatics.
</i></small></small>
## Dictionaries
Dictionaries are mappings between keys and items stored in the dictionaries. Unlike lists and tuples, dictionaries are unordered. Alternatively one can thi... | github_jupyter |
```
# import numpy as np
# import pandas as pd
# import matplotlib.pyplot as plt
# from laspy.file import File
# from pickle import dump, load
# import torch
# import torch.nn as nn
# import torch.nn.functional as F
# import torch.optim as optim
# import torch.utils.data as udata
# from torch.autograd import Variable
... | github_jupyter |
```
# This cell is added by sphinx-gallery
!pip install mrsimulator --quiet
%matplotlib inline
import mrsimulator
print(f'You are using mrsimulator v{mrsimulator.__version__}')
```
# MCl₂.2D₂O, ²H (I=1) Shifting-d echo
²H (I=1) 2D NMR CSA-Quad 1st order correlation spectrum simulation.
The following is a static ... | github_jupyter |
```
try:
import openmdao.api as om
import dymos as dm
except ImportError:
!python -m pip install openmdao[notebooks]
!python -m pip install dymos[docs]
import openmdao.api as om
import dymos as dm
```
# How do I run two phases parallel-in-time?
Complex models sometimes encounter state variable... | github_jupyter |
[View in Colaboratory](https://colab.research.google.com/github/ale93111/Unet_dsb2018/blob/master/Unet_weighted_dsb2018.ipynb)
```
!apt-get install -y -qq software-properties-common python-software-properties module-init-tools
!add-apt-repository -y ppa:alessandro-strada/ppa 2>&1 > /dev/null
!apt-get update -qq 2>&1 >... | github_jupyter |
# Performative Prediction: A Case Study in Strategic Classification
This notebook replicates the main experiments in [Performative Prediction](https://arxiv.org/abs/2002.06673):
- Juan C. Perdomo, Tijana Zrnic, Celestine Mendler-Dünner, Moritz Hardt. "Performative Prediction." arXiv preprint 2002.06673, 2020.
Strate... | github_jupyter |
# Modeling and Simulation in Python
Case study.
Copyright 2017 Allen Downey
License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)
```
# Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an ... | github_jupyter |
Use this utlity to update the returns and std_dev fields within investment-options.csv
```
%%javascript
IPython.OutputArea.prototype._should_scroll = function(lines) {
return false;
}
# imports
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
import brownbear as bb
#... | github_jupyter |
**Data Description**
age: The person's age in years
sex: The person's sex (1 = male, 0 = female)
cp: The chest pain experienced (Value 1: typical angina, Value 2: atypical angina, Value 3: non-anginal pain, Value 4: asymptomatic)
trestbps: The person's resting blood pressure (mm Hg on admission to the hospital)
ch... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (12,8)
import numpy as np
import tensorflow as tf
import keras
import pandas as pd
from keras_tqdm import TQDMNotebookCallback
from keras.preprocessing.sequence import pad_sequences
def data_generator(batch_size, tfrecord, start_fr... | github_jupyter |
##### Copyright 2018 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 |
```
%reload_ext autoreload
%autoreload 2
import logging
import numpy as np
# Make analysis reproducible
np.random.seed(0)
# Enable logging
logging.basicConfig(level=logging.INFO)
from dask.distributed import Client
Client(n_workers=2,
threads_per_worker=2,
processes=True,
memory_limit='25GB')
fr... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df=pd.read_csv('FearData.csv')
df.head(22)
# Preprocessing :
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report,confusion_matrix
from itertools im... | github_jupyter |
<!--NOTEBOOK_HEADER-->
*This notebook contains material from [CBE40455-2020](https://jckantor.github.io/CBE40455-2020);
content is available [on Github](https://github.com/jckantor/CBE40455-2020.git).*
<!--NAVIGATION-->
| [Contents](toc.html) | [2.0 Modeling](https://jckantor.github.io/CBE40455-2020/02.00-Modeling.htm... | github_jupyter |
<a href="https://colab.research.google.com/github/dinesh110598/Spin_glass_NN/blob/master/main.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Classifying Bimodal triangular EA lattices
The Hamiltonian for the EA model on a 2d triangular lattice wi... | github_jupyter |
# 线性回归 --- 从0开始
虽然强大的深度学习框架可以减少很多重复性工作，但如果你过于依赖它提供的便利抽象，那么你可能不会很容易地理解到底深度学习是如何工作的。所以我们的第一个教程是如何只利用ndarray和autograd来实现一个线性回归的训练。
## 线性回归
给定一个数据点集合`X`和对应的目标值`y`，线性模型的目标是找一根线，其由向量`w`和位移`b`组成，来最好地近似每个样本`X[i]`和`y[i]`。用数学符号来表示就是我们将学`w`和`b`来预测，
$$\boldsymbol{\hat{y}} = X \boldsymbol{w} + b$$
并最小化所有数据点上的平方误差
$$\sum_{i=1}... | github_jupyter |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.