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# Hidden Markov Model Demo
A Hidden Markov Model (HMM) is one of the simpler graphical models available in _SSM_. This notebook demonstrates creating and sampling from and HMM using SSM, and fitting an HMM to synthetic data. A full treatment of HMMs is beyond the scope of this notebook, but there are many good resourc... | github_jupyter |
---
_You are currently looking at **version 1.0** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-data-analysis/resources/0dhYG) course resource._
---
# Merging Dataframes
... | github_jupyter |
```
%matplotlib inline
```
# Visualizing Part-of-Speech Tagging with Yellowbrick
This notebook is a sample of the text visualizations that yellowbrick provides, in particular a feature that enables visual part-of-speech tagging, which can be used to make decisions about text normalization and vectorization.
```
impo... | 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 |
```
# for use in tutorial and development; do not include this `sys.path` change in production:
import sys ; sys.path.insert(0, "../")
```
# Vector embedding with `gensim`
Let's make use of deep learning through a technique called *embedding* – to analyze the relatedness of the labels used for recipe ingredients.
Am... | github_jupyter |
```
import nltk
```
# 1、Sentences Segment（分句）
```
sent_tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
paragraph = "The first time I heard that song was in Hawaii on radio. I was just a kid, and loved it very much! What a fantastic song!"
sentences = sent_tokenizer.tokenize(paragraph)
sentences
```
... | github_jupyter |
This notebook shows how to calculate all the angles. There are three major functions for the calculation. The <code>filter_sensor_points_to_cube_id</code> function returns only the sensor points that corresponds to one HSI cube. This significantly increases the efficiency and make the process faster. The second functio... | github_jupyter |
<img src="https://drive.google.com/uc?id=1E_GYlzeV8zomWYNBpQk0i00XcZjhoy3S" width="100"/>
# DSGT Bootcamp Week 1: Introduction and Environment Setup
# Learning Objectives
1. Gain an understanding of Google Colab
2. Introduction to team project
3. Gain an understanding of Kaggle
4. Download and prepare dataset
5. Ins... | github_jupyter |
## Figure 12
Similar to [Figure 5](https://github.com/EdwardJKim/astroclass/blob/master/paper/notebooks/figure05/purity_mag_integrated.ipynb)
but for the reduced training set.
```
from __future__ import division, print_function, unicode_literals
%matplotlib inline
import numpy as np
from scipy.special import gammaln
... | github_jupyter |
```
Copyright 2021 IBM Corporation
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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, softwa... | github_jupyter |
## YUV color space
Colors in images can be encoded in different ways. Most well known is perhaps the RGB-encoding, in which the image consists of a Red, Green, and Blue channel. However, there are many other encodings, which sometimes have arisen for historical reasons or to better comply with properties of human perce... | github_jupyter |
```
a=5
print(a)
import numpy as np
np.pi
import pandas as pd
data = pd.read_csv("./data_SNat/BRGM_Mayotte_2018.txt",delimiter = "\t")
print(data.head())
import matplotlib.pyplot as plt
plt.plot(data['sec'],data['mag'])
plt.show()
from Codes_Graphes.OmoriUtsu import GraphOmoriUtsu,RegressionOU,RegressionOU_foreshock
a,... | github_jupyter |
```
import os
os.environ['CASTLE_BACKEND'] = 'pytorch'
from collections import OrderedDict
import warnings
import numpy as np
import networkx as nx
import ges
from castle.common import GraphDAG
from castle.metrics import MetricsDAG
from castle.datasets import IIDSimulation, DAG
from castle.algorithms import PC, ICAL... | github_jupyter |
# Оценка pi ($\pi$) с использованием квантового алгоритма оценки фазы
# Оценка pi ($\pi$) с использованием квантового алгоритма оценки фазы.
## 1. Краткий обзор [квантового алгоритма оценки фазы](https://qiskit.org/textbook/ch-algorithms/quantum-phase-estimation.html)
Quantum Phase Estimation (QPE) is a quantum algo... | github_jupyter |
```
%pylab inline
import numpy as np
import seaborn as sns
from tqdm import tqdm
from laika.lib.coordinates import LocalCoord, ecef2geodetic
# A practical way to confirm the accuracy of laika's processing
# is by downloading some observation data from a CORS station
# and confirming our position estimate to the known p... | github_jupyter |
# T81-558: Applications of Deep Neural Networks
**Module 6: Convolutional Neural Networks (CNN) for Computer Vision**
* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), McKelvey School of Engineering, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)
* For mo... | github_jupyter |
# Union and intersection of rankers
Let's build a pipeline using union `|` and intersection `&` operators.
```
%load_ext autoreload
%autoreload 2
from cherche import data, rank, retrieve
from sentence_transformers import SentenceTransformer
```
The first step is to define the corpus on which we will perform the neur... | github_jupyter |
## <small>
Copyright (c) 2017-21 Andrew Glassner
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, ... | github_jupyter |
```
import os
from os.path import join, dirname
import cv2
import lmdb
import pickle
import matplotlib.pyplot as plt
import numpy as np
dataset_name = 'rscd'
data_path = join('/home/zhong/Dataset',dataset_name)
lmdb_path = join('/home/zhong/Dataset',dataset_name+'_lmdb')
for dataset_part in ['train', 'valid', 'test']:
... | github_jupyter |
```
#This function gets the raw data and clean it
def data_clean(data):
print("Data shape before cleaning:" + str(np.shape(data)))
#Change the data type of any column if necessary.
print("Now it will print only those columns with non-numeric values")
print(data.select_dtypes(exclud... | github_jupyter |
```
# Copyright 2019 NVIDIA Corporation. All Rights Reserved.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | github_jupyter |
<!-- :Author: Arthur Goldberg <Arthur.Goldberg@mssm.edu> -->
<!-- :Date: 2020-07-13 -->
<!-- :Copyright: 2020, Karr Lab -->
<!-- :License: MIT -->
# DE-Sim tutorial
DE-Sim is an open-source, object-oriented, discrete-event simulation (OO DES) tool implemented in Python.
DE-Sim makes it easy to build and simulate discr... | github_jupyter |
# First Steps with Huggingface
```
from IPython.display import display, Markdown
with open('../../doc/env_variables_setup.md', 'r') as fh:
content = fh.read()
display(Markdown(content))
```
## Import Packages
Try to avoid 'pip install' in the notebook. This can destroy dependencies in the env.
```
# only runnin... | github_jupyter |
# 使用SentimentNet实现情感分类
`GPU` `CPU` `进阶` `自然语言处理` `全流程`
[](https://authoring-modelarts-cnnorth4.huaweicloud.com/console/lab?share-url-b64=aHR0cHM6Ly9taW5kc3BvcmUtd2Vic2l0ZS5vYnMuY24tbm9ydGgtNC5teWh1YXdlaWNsb3VkLmNvbS9ub3RlYm9vay9tY... | github_jupyter |
# Datasets to download
Here we list a few datasets that might be interesting to explore with vaex.
## New York taxi dataset
The very well known dataset containing trip infromation from the iconic Yellow Taxi company in NYC.
The raw data is curated by the [Taxi & Limousine Commission (TLC)](
https://www1.nyc.gov/site... | github_jupyter |
```
# Setting up a model and a mesh for the MT forward problem
import SimPEG as simpeg, sys
import numpy as np
from SimPEG import NSEM
import telluricpy
import matplotlib.pyplot as plt
%matplotlib inline
import copy
# Define the area of interest
bw, be = 557100, 557580
bs, bn = 7133340, 7133960
bb, bt = 0,480
```
# Bu... | github_jupyter |
## List comprehensions
A *list comprehension* is a compact way to construct a new collection by performing operations on some or all of the elements of another collection
It is a powerful and succinct way to specify a data transformation (from one collection to another).
General form: `[ expression for-clause condi... | github_jupyter |
# Images to Text: A Gentle Introduction to Optical Character Recognition with PyTesseract
***How to install & run the course notebooks on your own computer***
For this course, we've been working in Jupyter Notebooks hosted on [Binder](https://binder.constellate.org/). If you want to host your own materials in Binder,... | github_jupyter |
```
import torchaudio as ta
import torch
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.nn.functional as F
import torch.autograd.profiler as profiler
# import pytorch_lightning as pl
import numpy as np
import os
import IPython.display as ipd
import numpy as np
import math
import glob
f... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive')
path = '/content/drive/MyDrive/Research/AAAI/dataset1/second_layer_with_entropy/'
import numpy as np
import pandas as pd
import torch
import torchvision
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import tor... | github_jupyter |
```
import pandas as pd
import janitor as jn
import pymc3 as pm
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from utils import ECDF
import arviz as az
%load_ext autoreload
%autoreload 2
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
```
# Darwin's Finches
A research gro... | github_jupyter |
Trees and diversity estimates for molecular markers. Env from 62_phylo_reduced.
After first run and evaluation, manually resolve problems with selected sequences (on plate fasta level):
- VBS00055 (aconitus) its2 trimmed 3' - weak signal, multipeaks
- VBS00021,VBS00022,VBS00023 (barbirostris) its2 re-trimmed to the sa... | github_jupyter |
<!-- dom:TITLE: PHY321: Two-body problems, gravitational forces, scattering and begin Lagrangian formalism -->
# PHY321: Two-body problems, gravitational forces, scattering and begin Lagrangian formalism
<!-- dom:AUTHOR: [Morten Hjorth-Jensen](http://mhjgit.github.io/info/doc/web/) at Department of Physics and Astronom... | github_jupyter |
ENS'IA - Session 4: Convolutional neural networks
-----
Today, we move on to **Convolutional neural networks (CNN)**!
These are neural networks specialized in image processing.
You will implement a basic CNN architecture and learn some techniques to boost your scores!
Let's load the libraries we will use along with t... | github_jupyter |
<a href="https://colab.research.google.com/github/arthurflor23/handwritten-text-recognition/blob/master/src/tutorial.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
<img src="https://github.com/arthurflor23/handwritten-text-recognition/blob/master/d... | github_jupyter |
# Kili Tutorial: How to leverage Counterfactually augmented data to have a more robust model
This recipe is inspired by the paper *Learning the Difference that Makes a Difference with Counterfactually-Augmented Data*, that you can find here on [arXiv](https://arxiv.org/abs/1909.12434)
In this study, the authors point... | github_jupyter |
## by Jan Willem de Gee (jwdegee@gmail.com)
```
import sys, os
import numpy as np
import pandas as pd
import matplotlib as mpl
mpl.rcParams['pdf.fonttype'] = 42
import matplotlib.pyplot as plt
import seaborn as sns
import hddm
from joblib import Parallel, delayed
from IPython import embed as shell
```
Let's start wi... | github_jupyter |
```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
#defining the function
def function_for_roots(x): #defining the function where we are finding the roots
a = 1.01 #using variables for the constants
b = -3.04
c = 2.07
return a*x**2 + b*x + c # finding the roots... | github_jupyter |
# Tutorial: FICO Explainable Machine Learning Challenge
In this tutorial, we use the dataset form the FICO Explainable Machine Learning Challenge: https://community.fico.com/s/explainable-machine-learning-challenge. The goal is to create a pipeline by combining a binning process and logistic regression to obtain an ex... | github_jupyter |
# Sales Analysis
Source: [https://github.com/d-insight/code-bank.git](https://github.com/d-insight/code-bank.git)
License: [MIT License](https://opensource.org/licenses/MIT). See open source [license](LICENSE) in the Code Bank repository.
-------------
#### Import libraries
```
import os
import pandas as pd
```
... | github_jupyter |
### **Import Google Drive**
```
from google.colab import drive
drive.mount('/content/drive')
```
### **Import Library**
```
import glob
import numpy as np
import os
import shutil
np.random.seed(42)
from sklearn.preprocessing import LabelEncoder
import cv2
import tensorflow as tf
import keras
import shutil
import ran... | github_jupyter |
# Generative Adversarial Networks
This code is based on https://arxiv.org/abs/1406.2661 paper from Ian J. Goodfellow, Jean Pouget-Abadie, et all
```
from google.colab import drive
drive.mount('/co... | github_jupyter |
```
# Imports
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
plt.rcParams['figure.figsize'] = (15.0, 8.0) # set default size of plots
plt.rcParams['figure.facecolor'] = 'white'
pd.set_option('display.max_rows', None)
matplotlib.rcParams.update({'font.size': 15})
test_column_... | github_jupyter |
# Using Web Processing Service (WPS) with the Defra Earth Observation Data Service API
This notebook introduces the concept of the Web Processing Service (WPS) which enables users to submit instructions to the EO Data Service to return outputs. The instructions are contained in the XML files provided. Please ensure th... | github_jupyter |
```
import warnings
warnings.filterwarnings('ignore')
import re
import time
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import pandas as pd
pd.options.display.max_columns = None
pd.options.display.mpl_style = 'default'
from nltk.tokenize import word_tokenize
from util3 im... | github_jupyter |
## Normal Distribution
Normal Distribution will is the distribution which calculates popularity of the population. This will get discussed on including standard deviation to determine Z score of particular value.
*Screenshot taken from [Coursera](https://class.cours... | github_jupyter |
# Test CKA
```
import numpy as np
import pickle
import gzip
import cca_core
from CKA import linear_CKA, kernel_CKA
X = np.random.randn(100, 64)
Y = np.random.randn(100, 64)
print('Linear CKA, between X and Y: {}'.format(linear_CKA(X, Y)))
print('Linear CKA, between X and X: {}'.format(linear_CKA(X, X)))
print('RBF K... | github_jupyter |
```
%matplotlib inline
"""This simpy model mimics the arrival and treatment of patients in an
emergency department with a limited number of doctors. Patients are generated,
wait for a doc (if none available), take up the doc resources for the time of
consulation, and exit the ED straight after seeing doc. Patients are ... | github_jupyter |
# Seismic Cubeset tutorial
Welcome! This notebook shows how to use `SeismicCubeset` class to hold information about .sgy/.hdf5 cubes. Also, utilitary class `SeismicGeometry` is demonstrated.
```
# Necessary modules
import os
import sys
from glob import glob
sys.path.append('..')
from seismiqb.batchflow import FilesI... | github_jupyter |
# A study of bias in data on Wikipedia
The purpose of this study is to explore bias in data on Wikipedia by analyzing Wikipedia articles on politicians from various countries with respect to their populations. A further metric used for comparison is the quality of articles on politicians across different countries.
#... | github_jupyter |
# Ensemble sorting of a Neuropixels recording
This notebook reproduces figures 1 and 4 from the paper [**SpikeInterface, a unified framework for spike sorting**](https://www.biorxiv.org/content/10.1101/796599v2).
The data set for this notebook is available on the Dandi Archive: [https://gui.dandiarchive.org/#/dandise... | github_jupyter |
# Regresión lineal
En el siguiente archivo se va a desarrollar la regresión lineal para las combinaciones de cada una de las variables que se encuentran en los datos provistos, agrupada por departamentos. Los datos se pueden encontrar [acá](https://docs.google.com/spreadsheets/u/1/d/12h1Pk1ZO-BDcGldzKW-IA9VMkU9RlUOPopF... | github_jupyter |
# Webinar n°1: Pyleecan Basics
This notebook is the support of the first out of three webinars organized by the association [Green Forge Coop](https://www.linkedin.com/company/greenforgecoop/about/) and the UNICAS University.
The webinars schedule is:
- Friday 16th October 15h-17h (GMT+2): How to use pyleecan (basic... | github_jupyter |
```
import re
```
# Intro to Class: (if we really want to use a class for Book)
The real implementation of this class to real text should should requires more sophiscated discussion.
My suggestion is to write some methods to automatically convert Book class's instance attributes to tags (TEI, XML, or MARKUS format)... | github_jupyter |
0 0~6
1 6~12
2 12~18
3 18~24
시간대, 분, 수유량
0, 100, 50
1, 200, 30
2, 100, 30
```
param_window_size = 6
param_seq_length = 287
param_num_epoch = 10
param_lstm_units = 64
param_lstm_stack = 2
num_sample = param_seq_length - param_window_size
import numpy as np
import os
import pandas
import theano
from keras.models impo... | github_jupyter |
```
import numpy as np
import pandas as pd
import seaborn as sns
%matplotlib inline
import matplotlib.pyplot as plt
from matplotlib import style
train_data = pd.read_csv('train.csv')
train_data.info()
# Data Cleaning
#
#from the above output we can see that we are missing 177 values in the age column,
# 687 from the ... | github_jupyter |
# Initial Operations:
```
import pandas as pd
import os
import numpy as np
from sklearn.decomposition import PCA, TruncatedSVD
from sklearn.manifold import TSNE
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import RidgeClassifierCV
... | github_jupyter |
```
import os
import torch
import torch.nn as nn
import torch.optim as optim
import torch.backends.cudnn as cudnn
import torch.nn.init as init
import argparse
from torch.autograd import Variable
import torch.utils.data as data
#CHANGE
from data import v2, v1, AnnotationTransform, VOCDetection, detection_collate, VOC_CL... | github_jupyter |
# Operations on Word Vectors
Welcome to your first assignment of Week 2, Course 5 of the Deep Learning Specialization!
Because word embeddings are very computationally expensive to train, most ML practitioners will load a pre-trained set of embeddings. In this notebook you'll try your hand at loading, measuring simi... | github_jupyter |
# Variational Bayes Notes
## What is variational Bayes?
A technique that allows us to compute approximations to non-closed form Bayes update equations.
## Why would we use it?
In **Bayesian parameter estimation**, to compute the intractable integration of the denominator in the parameter posterior probability:
\begi... | github_jupyter |
# Example of Data Analysis with DCD Hub Data
First, we will install the Python SDK of DCD-hub and other libraries to gerate plot from the data.
In your project folder, create "requirements.txt" file and save the file with the text written below:
dcd-sdk>=0.0.22 <br />
paho-mqtt <br />
python-dotenv <br />
pyserial <... | github_jupyter |
# Presenting SOTA results on CIMA dataset
This notebook serves as visualisation for State-of-the-Art methods on CIMA dataset
_Note: In case you want to get some further evaluation related to new submission, you may contact JB._
```
%matplotlib inline
%load_ext autoreload
%autoreload 2
import os, sys
import glob, js... | github_jupyter |
# Latent Dirichlet Allocation for Text Data
In this assignment you will
* apply standard preprocessing techniques on Wikipedia text data
* use GraphLab Create to fit a Latent Dirichlet allocation (LDA) model
* explore and interpret the results, including topic keywords and topic assignments for documents
Recall that... | github_jupyter |
# Basic Functionality
ghoul version 0.1.0
## Collapsing symbols
The purpose of ghoul is to randomly generate internally-consistent python objects.
The basic unit in ghoul is the `Symbol`. A symbol is an object in a "superposition": it contains many possible states, until it "collapses" to a concrete value.
```
fro... | github_jupyter |
```
import sys, os, glob
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import logging
# from scipy.interpolate import UnivariateSpline, interp1d
from statsmodels.stats.multicomp import pairwise_tukeyhsd, MultiComparison
from statsmodels.stats.libqsturng i... | github_jupyter |
```
NAME = "Robina Shaheen"
DATE = "06242020"
COLLABORATORS = ""
```
# Wildfires in California: Causes and Consequences
The rising carbon dioxide in the atmosphere is contributing to constant increase in global temperatures. Over the last two decades, humanity has observed record-breaking extreme weather events. A co... | github_jupyter |
# Keras Basics
Welcome to the section on deep learning! We'll be using Keras with a TensorFlow backend to perform our deep learning operations.
This means we should get familiar with some Keras fundamentals and basics!
## Imports
```
import numpy as np
```
## Dataset
We will use the Bank Authentication Data Set t... | github_jupyter |
# Comparative Analysis with MELD
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scprep
import meld
import sklearn
import pickle
import cna
import time
# making sure plots & clusters are reproducible
np.random.seed(42)
```
# Test on Sepsis
```
# Import sepsis expression data
d = cn... | github_jupyter |
# Evolutionary Shape Prediction
An experiment in evolutionary software using *reinforcement learning* to discover interesting data objects within a given set of graph data.
```
import kglab
namespaces = {
"nom": "http://example.org/#",
"wtm": "http://purl.org/heals/food/",
"ind": "http://purl.org/heal... | github_jupyter |
## Вот и первая домашка! :)
Воспользуемся датасетом про цены на дома в Бостоне. Небольшая расшифровка:
* `crim` – уровень преступности на душу населения по городам;
* `zn` – доля земли под жилую застройку, зонированная под участки площадью более 25000 кв. футов;
* `indus` – доля акров, не связанных с розничной торгов... | github_jupyter |
# DQN Breakout
```
from __future__ import division
import gym
import numpy as np
import random
import tensorflow as tf
import tensorflow.contrib.slim as slim
import matplotlib.pyplot as plt
import scipy.misc
import os
%matplotlib inline
```
### Load the game environment
```
env = gym.make('BreakoutDeterministic-v4'... | github_jupyter |
<a href="https://kaggle.com/code/ritvik1909/siamese-network" target="_blank"><img align="left" alt="Kaggle" title="Open in Kaggle" src="https://kaggle.com/static/images/open-in-kaggle.svg"></a>
# Siamese Network
A Siamese neural network (sometimes called a twin neural network) is an artificial neural network that use... | github_jupyter |
```
import os
os.chdir('..')
```
<img src="flow_2.png">
```
from flows.flows import Flows
flow = Flows(2)
path = "./data/flow_2"
files_list = ["train.csv","test.csv"]
dataframe_dict, columns_set = flow.load_data(path, files_list)
dataframe_dict, columns_set= flow.flatten_json_data(dataframe_dict)
dataframe_dict, colu... | github_jupyter |
# UROP Code Stuff
#### The python translation of doit.pl
## Initializing
### Since this is a notebook, argv doesn't work how it normally would. Instead, place the values in this block
```
import sys
import numpy as np
sys.argv = ['doit.py', 1]
with open('framedist_vals', 'r') as f:
framedist = np.array(f.rea... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem import Descriptors
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import StandardScaler
from ... | github_jupyter |
# [Country Embedding](https://philippmuens.com/word2vec-intuition/)
```
import json
import pandas as pd
import seaborn as sns
import numpy as np
# prettier Matplotlib plots
import matplotlib.pyplot as plt
import matplotlib.style as style
style.use('seaborn')
```
# 1. Dataset
#### Download
```
%%bash
download=1
fo... | github_jupyter |
# 1. Regressão Linear
## 1.1. Univariada
Existem diversos problemas na natureza para os quais procura-se obter valores de saída dado um conjunto de dados de entrada. Suponha o problema de predizer os valores de imóveis de uma determinada cidade, conforme apresentado na Figura 1, em que podemos observer vários pontos q... | github_jupyter |
First we need to download the dataset. In this case we use a datasets containing poems. By doing so we train the model to create its own poems.
```
from datasets import load_dataset
dataset = load_dataset("poem_sentiment")
print(dataset)
```
Before training we need to preprocess the dataset. We tokenize the entries ... | github_jupyter |
[](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/20.SentenceDetectorDL_Healthcare.ipynb)
# ... | github_jupyter |
# Test post compute 3D
```
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import subprocess as sp
import sys
import os
import glob
import pickle
import itertools
from matplotlib.colors import LogNorm, PowerNorm, Normalize
from ipywidgets import *
%matplotlib widget
### Transformation functio... | github_jupyter |
```
# Imports / Requirements
import json
import numpy as np
import torch
import matplotlib.pyplot as plt
import torch.nn.functional as F
import torchvision
from torch import nn, optim
from torchvision import datasets, transforms, models
from torch.autograd import Variable
from collections import OrderedDict
from PIL ... | github_jupyter |
# Exercise 1
**(1)** Forecasting with linear models:
> **(a)** Estimate four linear models unsing the OLS estimator
> **(b)** Forecast n steps ahead using the estimated models
> **(c)** Forecast n steps ahead (recursively) using the estimated models
> **(d)** Compute confidence intervals for both **(b)** and **(c)*... | github_jupyter |
<a href="https://colab.research.google.com/github/IanCostello/tools/blob/ValidationTool/import-validation-helper/ImportValidatorMaster.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Import Validation Helper
This Colab notebook introduces a few to... | github_jupyter |
```
import pandas as pd
import os
import json
from functools import singledispatch
date = "2022-02-12"
STATE_NAMES = {
'AP': 'Andhra Pradesh',
'AR': 'Arunachal Pradesh',
'AS': 'Assam',
'BR': 'Bihar',
'CT': 'Chhattisgarh',
'GA': 'Goa',
'GJ': 'Gujarat',
'HR': 'Haryana',
'HP': 'Himachal Pradesh',
'JH':... | github_jupyter |
```
#import packages
import numpy as np
from numpy import loadtxt
import pylab as pl
from IPython import display
from RcTorch import *
from matplotlib import pyplot as plt
from scipy.integrate import odeint
import time
import matplotlib.gridspec as gridspec
#this method will ensure that the notebook can use multiproce... | github_jupyter |
<a href="https://colab.research.google.com/github/parekhakhil/pyImageSearch/blob/main/1402_opencv_template_matching.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
![logo_jupyter.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAABcCAYAAABA4uO... | github_jupyter |
# Regular Expressions in Python
> Some people, when confronted with a problem, think, "I know, I'll use regular expressions." Now they have two problems. - Jamie Zawinski
## First day: quick overview
This first day we will explore the basics of the `re` (standard libary) module so you can start adding this powerful ... | github_jupyter |
## Eng+Wales well-mixed example model
This is the inference notebook. There are various model variants as encoded by `expt_params_local` and `model_local`, which are shared by the notebooks in a given directory.
Outputs of this notebook:
* `ewMod-inf.pik` : result of inference computation
* `ewMod-hess.npy` : hessi... | github_jupyter |
# AllSides sources & bias crawler
Get and save a list of rated news sources as left or right and in between.
A CSV file will be created with the following columns:
- Source
- Label
- Agree
- Disagree
- Publisher URL
- Publisher site
```
!ipython -m pip install aiohttp bs4 requests
import asyncio
import csv
import l... | github_jupyter |
# Summary of transfer outcomes for practice M85092
**Context**
We would like to see a summary of transfer outcomes where the sending practice is M85092 for April data (if available), otherwise March data.
NB: Upon finding there were only 8 relevant transfers, we also used March data (which contained 2600 transfers)... | github_jupyter |
```
num_classes = 2
ultrasound_size = 128
data_folder = r"QueensToChildrens"
notebook_save_folder = r"SavedNotebooks"
model_save_folder = r"SavedModels"
ultrasound_file = r"ultrasound.npy"
segmentation_file = r"segmentation.npy"
test_ultrasound_file = r"ultrasound-test.npy"
test_segmentation_file = r"segmentation-te... | github_jupyter |
<a href="https://colab.research.google.com/github/mnslarcher/cs224w-slides-to-code/blob/main/notebooks/04-link-analysis-pagerank.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Link Analysis: PageRank
```
import random
from typing import Optional... | github_jupyter |
# Background
This notebook walks through the creation of a fastai [DataBunch](https://docs.fast.ai/basic_data.html#DataBunch) object. This object contains a pytorch dataloader for the train, valid and test sets. From the documentation:
```
Bind train_dl,valid_dl and test_dl in a data object.
It also ensures all the... | github_jupyter |
# Project 3: Smart Beta Portfolio and Portfolio Optimization
## Instructions
Each problem consists of a function to implement and instructions on how to implement the function. The parts of the function that need to be implemented are marked with a `# TODO` comment. After implementing the function, run the cell to tes... | github_jupyter |
# Generative Adversarial Network
In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits!
GANs were [first reported on](https://arxiv.org/abs/1406.2661) in 2014 from Ian Goodfellow and others in Yoshua Bengio'... | github_jupyter |
# Welcome to fastai
```
from fastai import *
from fastai.vision import *
from fastai.gen_doc.nbdoc import *
from fastai.core import *
from fastai.basic_train import *
```
The fastai library simplifies training fast and accurate neural nets using modern best practices. It's based on research in to deep learning best p... | github_jupyter |
```
print("hello world")
a = 10
print(a)
b = 10 * a
# jupyter will automatically print the last value in the block
# and by the way: this is how a comment looks.
b
if b > 50:
print("b is greater than 50")
```
Python can handle numbers of arbitrary length :)
What's actually $2^{2048}$?
```
2**2048
```
## define... | github_jupyter |
##################################################
## Assignment 1
## Problem 2(a)
## Samin Yeasar Arnob
## McGill ID: 260800927
## COMP 767- Reinforcement Learning Winter 2019
#################################################
# Grid World Environment
Original Grid world environment was taken from link: https:/... | github_jupyter |
```
import numpy as np
from sklearn.metrics import silhouette_score
from scipy.spatial.distance import euclidean
class Kmeans:
'''
K-means is a clustering algorithm that finds convex clusters.
The user specifies the number of clusters a priori.'''
def __init__(self, K=2, init='k++', rand... | github_jupyter |
```
from __future__ import division
import numpy as np
import epgcpmg as epg
import time
import matplotlib.pyplot as plt
%matplotlib inline
def numerical_gradient(myfun, myparams, e=1e-5):
initial_params = myparams.copy()
num_grad = np.zeros(initial_params.shape)
perturb = np.zeros(initial_params.shape)
... | github_jupyter |
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