code stringlengths 2.5k 150k | kind stringclasses 1
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# 5. Putting it all together
**Bring together all of the skills you acquired in the previous chapters to work on a real-life project. From connecting to a database and populating it, to reading and querying it.**
It's time to put all your effort so far to good use on a census case study.
### Census case study
The cas... | github_jupyter |
# Prepare environment
```
!pip install git+https://github.com/katarinagresova/ensembl_scraper.git@6d3bba8e6be7f5ead58a3bbaed6a4e8cd35e62fd
```
# Create config file
```
import yaml
config = {
"root_dir": "../../datasets/",
"organisms": {
"homo_sapiens": {
"regulatory_feature"
}
... | github_jupyter |
# Source detection with Gammapy
## Context
The first task in a source catalogue production is to identify significant excesses in the data that can be associated to unknown sources and provide a preliminary parametrization in term of position, extent, and flux. In this notebook we will use Fermi-LAT data to illustrat... | github_jupyter |
# Quasi-Laplace approximation for Poisson data
- toc: true
- badges: true
- comments: true
- categories: [jupyter]
### About
The [quasi-Laplace approximation]({% post_url 2020-06-22-intuition-for-quasi-Laplace %}) may be extended to approximate the posterior of a Poisson distribution with a Gaussian, as we will see... | github_jupyter |
```
import nltk
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from nltk.stem import WordNetLemmatizer
from nltk.corpus import wordnet
import re, collections
from collections import defaultdict
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import mean_squared_e... | github_jupyter |
# Hello Segmentation
A very basic introduction to using segmentation models with OpenVINO.
We use the pre-trained [road-segmentation-adas-0001](https://docs.openvinotoolkit.org/latest/omz_models_model_road_segmentation_adas_0001.html) model from the [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/)... | github_jupyter |
# TD - Implémentation des arbres en POO
On va dans ce TD créer une classe arbre binaire qui va nous permettre d'implémenter cette structure de données avec toutes ses caractéristiques.
On se souvient du cours sur les arbres https://pixees.fr/informatiquelycee/n_site/nsi_term_structDo_arbre.html dans lequel on définit... | github_jupyter |
```
from __future__ import print_function
import math
from IPython import display
from matplotlib import cm
from matplotlib import gridspec
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
from sklearn import metrics
import tensorflow as tf
from tensorflow.python.data import Dataset
tf.log... | github_jupyter |
# Linear_Reg
Author ~ Saurabh Kumar
Date ~ 05-Dec-21
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
#simple_linera_regression
class Simple_linear_regression:
def __init__(self,learning_rate=1e-3,n_steps=100... | github_jupyter |
# Collaboration and Competition
---
In this notebook, you will learn how to use the Unity ML-Agents environment for the third project of the [Deep Reinforcement Learning Nanodegree](https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893) program.
### 1. Start the Environment
We begin by import... | github_jupyter |
<i>Copyright (c) Microsoft Corporation. All rights reserved.</i>
<i>Licensed under the MIT License.</i>
# Testing different Hyperparameters and Benchmarking
In this notebook, we will cover how to test different hyperparameters for a particular dataset and how to benchmark different parameters across a group of datas... | github_jupyter |
```
import os
import torch
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import ToTensor, ToPILImage
from torchvision.models.detection import fasterrcnn_resnet50_fpn
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from PIL import Image
class PlayerDataset(Dataset):
... | github_jupyter |
```
!git clone https://github.com/GraphGrailAi/ruGPT3-ZhirV
cd ruGPT3-ZhirV
cd ..
!pip3 install -r requirements.txt
```
Обучение эссе
!python pretrain_transformers.py \
--output_dir=/home/jovyan/ruGPT3-ZhirV/ \
--overwrite_output_dir \
--model_type=gpt2 \
--model_name_or_path=sberbank-ai/rugpt3large_b... | github_jupyter |
```
# Copyright 2022 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 writing, s... | github_jupyter |
```
import torch
import torch.nn as nn
import onmt
import onmt.inputters
import onmt.modules
import onmt.utils
```
We begin by loading in the vocabulary for the model of interest. This will let us check vocab size and to get the special ids for padding.
```
vocab = dict(torch.load("../../data/data.vocab.pt"))
src_pa... | github_jupyter |
# 决策树
- 非参数学习算法
- 天然解决多分类问题
- 也可以解决回归问题
- 非常好的可解释性
```
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import cross_val_score
from sklearn import datasets
iris = datasets.load_iris()
print(iris.DESCR)
X = iris.data[:, 2:] # 取后两个特征
y = iris.target
plt.scatter(X[y==0, 0], X[y==0, 1])
pl... | github_jupyter |
```
import pandas as pd
import numpy as np
from PIL import Image
import os
import sys
!pip install ipython-autotime
%load_ext autotime
%matplotlib inline
```
1. Extract your dataset and split into train_x, train_y, test_x and test_y.
2. Execute the following cells
---
## Hybrid Social Group Optimization
---... | github_jupyter |
# Final Project
For the final project, you will need to implement a "new" statistical algorithm in Python from the research literature and write a "paper" describing the algorithm.
Suggested papers can be found in Sakai:Resources:Final_Project_Papers
## Paper
The paper should have the following:
### Title
Shoul... | github_jupyter |
# ClusterFinder Reference genomes reconstruction
This notebook validates the 10 genomes we obtained from NCBI based on the ClusterFinder supplementary table.
We check that the gene locations from the supplementary table match locations in the GenBank files.
```
from Bio import SeqIO
from Bio.SeqFeature import Featu... | github_jupyter |
# Realization of Recursive Filters
*This jupyter notebook is part of a [collection of notebooks](../index.ipynb) on various topics of Digital Signal Processing. Please direct questions and suggestions to [Sascha.Spors@uni-rostock.de](mailto:Sascha.Spors@uni-rostock.de).*
## Cascaded Structures
The realization of rec... | github_jupyter |
# Utilizing daal4py in Data Science Workflows
The notebook below has been made to demonstrate daal4py in a data science context. It utilizes a Cycling Dataset for pyworkout-toolkit, and attempts to create a linear regression model from the 5 features collected for telemetry to predict the user's Power output in the a... | github_jupyter |
# **JIVE: Joint and Individual Variation Explained**
JIVE (Joint and Individual Variation Explained) is a dimensional reduction algorithm that can be used when there are multiple data matrices (data blocks). The multiple data block setting means there are $K$ different data matrices, with the same number of observatio... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import check_lab05 as p
plt.rcParams.update({'font.size': 14})
plt.rcParams['lines.linewidth'] = 3
pi=np.pi
```
ME 3264 - Applied Measurements Laboratory
===========================================
Lab #5 - Linear Variable Differential Trans... | github_jupyter |
```
import tensorflow as tf
from tensorflow.keras import layers, Model
from tensorflow.keras.activations import relu
from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
from tensorflow.keras.losses import BinaryCrossentropy
from tensorflow.ker... | github_jupyter |
# 网络参数的初始化
[](https://gitee.com/mindspore/docs/blob/master/docs/mindspore/programming_guide/source_zh_cn/initializer.ipynb) [](https://obs.dualstack.c... | github_jupyter |
```
import numpy as np
import pandas as pd
# load the contents of a file into a pandas Dataframe
input_file = '/Users/aurelianosancho/Google Drive/Pre_Processing/train.csv'
df_titanic = pd.read_csv(input_file)
```
$\textbf{NOTE}$ Although it is not demonstrated in this section, you must ensure that any feature engin... | github_jupyter |
# The BioBB REST API
The **[BioBB REST API](https://mmb.irbbarcelona.org/biobb-api)** allows the execution of the **[BioExcel Building Blocks](https://mmb.irbbarcelona.org/biobb/)** in a remote server.
## Documentation
For an extense documentation section, please go to the **[BioBB REST API website help](https://mmb... | github_jupyter |
An illustration of the metric and non-metric MDS on generated noisy data.
The reconstructed points using the metric MDS and non metric MDS are slightly shifted to avoid overlapping.
#### New to Plotly?
Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloadi... | github_jupyter |
# Web Coverage Service (WCS) Download Example
## Introduction
We'll demonstrate how to download a GeoTIFF data file from a public WCS service using Python 3.
### WCS Data Service
For this demonstration we'll use Landfire (LF_1.4.0): https://www.landfire.gov/data_access.php
For Landfire LF_1.4.0 we see that the base U... | github_jupyter |
# Python datetime module
We will look at an important standard library, the [datetime library][1] which contains many powerful functions to support date, time and datetime manipulation. Pandas does not rely on this object and instead creates its own, a `Timestamp`, discussed in other notebooks.
The datetime library i... | github_jupyter |
```
import numpy as np
import pandas as pd
import time
import os
from pyspark.ml.clustering import KMeans
from pyspark.ml.evaluation import ClusteringEvaluator
from pyspark.ml.linalg import Vectors
from matplotlib import pyplot as plt
from pyspark.sql import SparkSession
# from pyspark.ml.clustering import KMeans, K... | github_jupyter |
# VacationPy
----
#### Note
* Keep an eye on your API usage. Use https://developers.google.com/maps/reporting/gmp-reporting as reference for how to monitor your usage and billing.
* Instructions have been included for each segment. You do not have to follow them exactly, but they are included to help you think throug... | github_jupyter |
# Hybrid Recommendations with the Movie Lens Dataset
__Note:__ It is recommended that you complete the companion __als_bqml.ipynb__ notebook before continuing with this __als_bqml_hybrid.ipynb__ notebook. This is, however, not a requirement for this lab as you have the option to bring over the dataset + trained model.... | github_jupyter |
# Introduction to XGBoost-Spark Cross Validation with GPU
The goal of this notebook is to show you how to levarage GPU to accelerate XGBoost spark cross validatoin for hyperparameter tuning. The best model for the given hyperparameters will be returned.
Here takes the application 'Mortgage' as an example.
A few libr... | github_jupyter |
<a href="https://colab.research.google.com/github/ryanlandvater/qIS/blob/main/QuantImmunoSubtraction.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Quantitative Immuno-Subtraction Project
---
```
# %matplotlib notebook
import sys ... | github_jupyter |
<div>
<h1 style="margin-top: 50px; font-size: 33px; text-align: center"> Homework 5 - Visit the Wikipedia hyperlinks graph! </h1>
<br>
<div style="font-weight:200; font-size: 20px; padding-bottom: 15px; width: 100%; text-align: center;">
<right>Maria Luisa Croci, Livia Lilli, Pavan Kumar Alikana</ri... | github_jupyter |
# Multi-wavelength maps
New in version `0.2.1` is the ability for users to instantiate wavelength-dependent maps. Nearly all of the computational overhead in `starry` comes from computing rotation matrices and integrals of the Green's basis functions, which makes it **really** fast to compute light curves at different ... | github_jupyter |
## Machine Learning- Exoplanet Exploration
#### Extensive Data Dictionary: https://exoplanetarchive.ipac.caltech.edu/docs/API_kepcandidate_columns.html
Highlightable columns of note are:
* kepoi_name: A KOI is a target identified by the Kepler Project that displays at least one transit-like sequence within Kepler ti... | github_jupyter |
```
path = "D:\\School\\Bank_uppg_mockdata.txt"
class DataSource:
def datasource_conn():
text_file = open(path)
if(text_file.readable):
text_file.close()
return [True, "Connection successful"]
text_file.close()
return[False, "Connection unsuccessful"]
... | github_jupyter |
# Fingerprint Generators
## Creating and using a fingerprint generator
Fingerprint generators can be created by using the functions that return the type of generator desired.
```
from rdkit import Chem
from rdkit.Chem import rdFingerprintGenerator
mol = Chem.MolFromSmiles('CC(O)C(O)(O)C')
generator = rdFingerprintG... | github_jupyter |
# Part 3: Launch a Grid Network Locally
In this tutorial, you'll learn how to deploy a grid network into a local machine and then interact with it using PySyft.
_WARNING: Grid nodes publish datasets online and are for EXPERIMENTAL use only. Deploy nodes at your own risk. Do not use OpenGrid with any data/models you w... | github_jupyter |
```
# import sys
# sys.path.append('https://github.com/alphaBenj/RoughCut/blob/master/files/data_iex.py')
%matplotlib inline
import matplotlib.pyplot as plt
from matplotlib import colors
import data_iex as IEX
dir(IEX)
# ?filter=symbol,volume,lastSalePrice
iex = IEX.API()
dir(iex)
iex.lastTrade(['AAPL', 'IBM', "FLR"])... | github_jupyter |
#### Reactions processing with AQME - substrates + TS
```
# cell with import, system name and PATHs
import os, glob, subprocess
import shutil
from pathlib import Path
from aqme.csearch import csearch
from aqme.qprep import qprep
from aqme.qcorr import qcorr
from rdkit import Chem
import pandas as pd
```
###### Step 1... | github_jupyter |
# Import all the necessary libraries
```
import cv2, time, pandas
from datetime import datetime
```
# Initialize the variables
```
first = None # This variable holds the value of the first frame
status_list = [None,None] # This variable holds the list of statuses - if Python has come across a frame greater than 1000... | github_jupyter |
```
import sys
sys.path.append('../../code/')
import os
import json
from datetime import datetime
import time
from math import *
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.stats as stats
import igraph as ig
import networkx as nx
from load_data import load_citation_network, c... | github_jupyter |
## Download and extract zip from web
- Specifies the source link, destination url and file name to download and extract data files
- Currently reading from external folder as github does not support large files
- To rerun function for testing before submission
- To add checks and conditions for the function
- ... | github_jupyter |
```
%matplotlib inline
```
# Linear classifier on sensor data with plot patterns and filters
Here decoding, a.k.a MVPA or supervised machine learning, is applied to M/EEG
data in sensor space. Fit a linear classifier with the LinearModel object
providing topographical patterns which are more neurophysiologically
in... | github_jupyter |
```
import numpy as np
from bokeh.plotting import figure, output_file, show
from bokeh.io import output_notebook
from nsopy import SGMDoubleSimpleAveraging as DSA
from nsopy.loggers import EnhancedDualMethodLogger
output_notebook()
%cd ..
from smpspy.oracles import TwoStage_SMPS_InnerProblem
```
# Solving dual mod... | github_jupyter |
# Энтропия и критерий Джини
$p_i$ - вероятность нахождения системы в i-ом состоянии.
Энтропия Шеннона определяется для системы с N возможными состояниями следующим образом
$S = - \sum_{i=1}^Np_ilog_2p_i$
Критерий Джини (Gini Impurity). Максимизацию этого критерия можно интерпретировать как максимизацию числа пар ... | github_jupyter |
```
import numpy as np
import keras
from keras.datasets import imdb
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.preprocessing.text import Tokenizer
import matplotlib.pyplot as plt
%matplotlib inline
np.random.seed(42)
```
## Loading the data
The dataset comes pr... | github_jupyter |
# Interpolation
### [Gerard Gorman](http://www.imperial.ac.uk/people/g.gorman), [Matthew Piggott](http://www.imperial.ac.uk/people/m.d.piggott), [Christian Jacobs](http://www.christianjacobs.uk)
## Interpolation vs curve-fitting
Consider a discrete set of data points
$$ (x_i, y_i),\quad i=0,\ldots,N,$$
and that w... | github_jupyter |
## Imperfect Tests and The Effects of False Positives
The US government has been widely criticized for its failure to test as many of its citizens for COVID-19 infections as other countries. But is mass testing really as easy as it seems? This analysis of the false positive and false negative rates of tests, using pub... | github_jupyter |
<h1>Model Deployment</h1>
Once we have built and trained our models for feature engineering (using Amazon SageMaker Processing and SKLearn) and binary classification (using the XGBoost open-source container for Amazon SageMaker), we can choose to deploy them in a pipeline on Amazon SageMaker Hosting, by creating an In... | github_jupyter |
## Preprocessing
```
# Import our dependencies
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import pandas as pd
import tensorflow as tf
from keras.callbacks import ModelCheckpoint
# Import and read the charity_data.csv.
import pandas as pd
application_df = p... | github_jupyter |
Goal of this notebook to test several classifiers on the data set with different features
And beforehand i want to thank Jose Portilla for his magnificent "Python for Data Science and Machine Learning" course on Udemy , which helped me to dive into ML =)
### Let's begin
First of all neccesary imports
```
import num... | github_jupyter |
# SimFin Test All Datasets
This Notebook performs automated testing of all the bulk datasets from SimFin. The datasets are first downloaded from the SimFin server and then various tests are performed on the data. An exception is raised if any problems are found.
This Notebook can be run as usual if you have `simfin` ... | github_jupyter |
```
# coding=utf-8
from __future__ import absolute_import, division, print_function, unicode_literals
import argparse
import logging
import os
from pathlib import Path
import random
from io import open
import pickle
import math
import numpy as np
import requests
logging.basicConfig(format='%(asctime)s - %(levelname... | github_jupyter |
### Importing required stuff
```
import time
import math
import random
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from datetime import timedelta
import scipy.misc
import glob
import sys
%matplotlib inline
```
### Helper files to load data
```
# Helper functions... | github_jupyter |
# Evaluation von Parsingtechniken
Das Parsing von Textdateien ist ein wichtiger Mechanismus, welcher wärend Informationsbearbeitung einen hohen Stellenwert innehält.
In order to be able to choose an adequate technique to be able to parse our custom DSL, we need to evaluate multiple of these techniques first.
*The f... | github_jupyter |
## Use the *Machine Learning Workflow* to process & transform Pima Indian data to create a prediction model.
### This model must predict which people are likely to develop diabetes with 70% accuracy!
##Import Libraries
```
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
#plot inline instead of... | github_jupyter |
[](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/streamlit_notebooks/healthcare/NER_LEGAL_DE.ipynb)
# **Detect legal entitie... | github_jupyter |
```
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from torch.utils.tensorboard import SummaryWriter
torch.manual_seed(42)
class RNNRegressor(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
s... | github_jupyter |
## Data Wrangling with Python: Intro to Pandas
Note: Notebook adapted from [here](https://github.com/EricElmoznino/lighthouse_pandas_tutorial/blob/master/pandas_tutorial.ipynb) & [here](https://github.com/sedv8808/LighthouseLabs/tree/main/W02D2) & from LHL's [21 Day Data Challenge](https://data-challenge.lighthouselabs... | github_jupyter |
# Mask R-CNN Demo
A quick intro to using the pre-trained model to detect and segment objects.
```
import os
import sys
import random
import math
import numpy as np
import skimage.io
import matplotlib
import matplotlib.pyplot as plt
# Root directory of the project
ROOT_DIR = os.path.abspath("../")
# Import Mask RCNN... | github_jupyter |
# Creating a Sentiment Analysis Web App
## Using PyTorch and SageMaker
_Deep Learning Nanodegree Program | Deployment_
---
Now that we have a basic understanding of how SageMaker works we will try to use it to construct a complete project from end to end. Our goal will be to have a simple web page which a user can u... | github_jupyter |
# **Tansfer Learning for Classification of Horses and Humans**
## **Abstract**
Aim of the notebook is to demonstrate the use of the transfer learning for improving the model accuracy for real-world images.
```
import os
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras import Model
fr... | github_jupyter |
Let's design a LNA using Infineon's BFU520 transistor. First we need to import scikit-rf and a bunch of other utilities:
```
import numpy as np
import skrf
from skrf.media import DistributedCircuit
import skrf.frequency as freq
import skrf.network as net
import skrf.util
import matplotlib.pyplot as plt
%matplotlib... | github_jupyter |
# Model Selection
## Model Selection
- The process of selecting the model among a collection of candidates machine learning models
### Problem type
- What kind of problem are you looking into?
- **Classification**: *Predict labels on data with predefined classes*
... | github_jupyter |
```
import numpy as np
arr = np.load('MAPS.npy')
print(arr)
print(np.shape(arr))
arr2 = np.empty((20426, 88), dtype = int)
for i in range(arr.shape[0]):
for j in range(arr.shape[1]):
if arr[i,j]==False:
arr2[i,j]=int(0)
int(arr2[i,j])
elif arr[i,j]==True:
arr2[i,... | github_jupyter |
```
# Necessary imports
import warnings
warnings.filterwarnings('ignore')
import re
import os
import numpy as np
import scipy as sp
from scipy.sparse import csr_matrix
from sklearn import datasets
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
fr... | github_jupyter |
# Running Plato in Google's Colab Notebooks
## 1. Preparation
### Use Chrome broswer
Since Colab is a product from Google, to take the most advantage of it, Chrome is the most recommended broswer here.
### Activating GPU support
If you need GPU support in your project, you may activate it in Google Colab by clicki... | github_jupyter |
# Tutorial 1: Instatiating a *scenario category*
In this tutorial, we will cover the following items:
1. Create *actor categories*, *activity categories*, and *physical thing categories*
2. Instantiate a *scenario category*
3. Show all tags of the *scenario category*
4. Use the `includes` function of a *scenario cate... | github_jupyter |
#### New to Plotly?
Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
<br>You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-fo... | github_jupyter |
# Python course Day 4
## Dictionaries
```
student = {"number": 570, "name":"Simon", "age":23, "height":165}
print(student)
print(student['name'])
print(student['age'])
my_list = {1: 23, 2:56, 3:78, 4:14, 5:67}
my_list[1]
my_list.keys()
my_list.values()
student.keys()
student.values()
student['number'] = 111
print(stu... | github_jupyter |
```
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import nltk
%matplotlib inline
nltk.download_shell()
messages = [line.rstrip() for line in open('SMSSpamCollection')] ## Put in your dataset here
len(messages)
messages[50]
for msg_no, message in enumerate(messages[:10]):
... | github_jupyter |
# Using Hyperopt to optimize XGB model hyperparameters
## Importing the libraries and loading the data
```
#!pip install --upgrade tables
#!pip install eli5
#!pip install xgboost
#!pip install hyperopt
import numpy as np
import pandas as pd
import xgboost as xgb
from sklearn.metrics import mean_absolute_error as ma... | github_jupyter |
## Statistical Analysis
We have learned null hypothesis, and compared two-sample test to check whether two samples are the same or not
To add more to statistical analysis, the follwoing topics should be covered:
1- Approxite the histogram of data with combination of Gaussian (Normal) distribution functions:
Gau... | github_jupyter |
```
#default_exp data.transforms
#export
from fastai2.torch_basics import *
from fastai2.data.core import *
from fastai2.data.load import *
from fastai2.data.external import *
from sklearn.model_selection import train_test_split
from nbdev.showdoc import *
```
# Helper functions for processing data and basic transfor... | github_jupyter |
```
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 torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from matplotlib import pyplot as plt
%matplotlib inline
class MosaicDa... | github_jupyter |
# TV Script Generation
In this project, you'll generate your own [Simpsons](https://en.wikipedia.org/wiki/The_Simpsons) TV scripts using RNNs. You'll be using part of the [Simpsons dataset](https://www.kaggle.com/wcukierski/the-simpsons-by-the-data) of scripts from 27 seasons. The Neural Network you'll build will gen... | github_jupyter |
# Parsing Inputs
In the chapter on [Grammars](Grammars.ipynb), we discussed how grammars can be
used to represent various languages. We also saw how grammars can be used to
generate strings of the corresponding language. Grammars can also perform the
reverse. That is, given a string, one can decompose the string into ... | github_jupyter |
<a href="http://landlab.github.io"><img style="float: left" src="../../landlab_header.png"></a>
# Using plotting tools associated with the Landlab NetworkSedimentTransporter component
<hr>
<small>For more Landlab tutorials, click here: <a href="https://landlab.readthedocs.io/en/latest/user_guide/tutorials.html">http... | github_jupyter |
The tanh-sinh (or double exponential) method.
We calculate an integral in the following fashion:
$$
I=\int_{-1}^{1} dx f(x) = \int_{-\infty}^{\infty} dt \; f(g(t)) \;g^{\prime}(t) \approx h \sum_{j=-N}^{N} w_j \; f(x_j)\; ,
$$
with $x_j= g(h \, t)$ and $w_j = g^{\prime}(h \, t) $. The functio $g(t)$ transorms the... | github_jupyter |
```
# Autoreload packages in case they change.
%load_ext autoreload
%autoreload 2
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import btk
import galsim
import warnings
```
# "Custom" tutorial
This tutorial is intended to showcase how to customize some elements of BTK, nam... | github_jupyter |
- Scipy의 stats 서브 패키지에 있는 binom 클래스는 이항 분포 클래스이다. n 인수와 p 인수를 사용하여 모수를 설정한다
```
N = 10
theta = 0.6
rv = sp.stats.binom(N, theta)
rv
```
- pmf 메서드를 사용하면, 확률 질량 함수 (pmf: probability mass function)를 계산할 수 있다.
```
%matplotlib inline
xx = np.arange(N + 1)
plt.bar(xx, rv.pmf(xx), align='center')
plt.ylabel('p(x)')
plt.tit... | github_jupyter |
<table style="float:left; border:none">
<tr style="border:none; background-color: #ffffff">
<td style="border:none">
<a href="http://bokeh.pydata.org/">
<img
src="assets/bokeh-transparent.png"
style="width:50px"
>
</a>
... | github_jupyter |
```
import pandas as pd
import numpy as np
import pickle as pk
file_name = '1_min'
df = pd.read_csv(file_name + '.csv')
df['behavior'] = np.zeros(len(df)).astype(np.int)
intention_2_action_delay = 3000
acc_threshold = 1
# 0 for changing to left
# 1 for changing to right
# 2 for following
next_lane_change_time = dict... | github_jupyter |
```
from os.path import exists
import openpyxl
import os
import pandas as pd
import re
from collections import Counter
import streamlit as st
pd.set_option('display.max_colwidth',None)
result = 'searchoutput.csv'
if exists(result):
os.remove(result)
# 创建结果文件
wbResult = openpyxl.Workbook()
wsResult = wbResult.... | github_jupyter |
## Gaussian processes with genetic algorithm for the reconstruction of late-time Hubble data
This notebook uses Gaussian processes (GP) with the genetic algorithm (GA) to reconstruct the cosmic chronometers and supernovae data sets ([2106.08688](https://arxiv.org/abs/2106.08688)). We shall construct our own GP class a... | github_jupyter |
[](https://www.pythonista.io)
# Declaraciones y bloques de código.
## Flujo de ejecución del código.
El intérprete de Python es capaz de leer, evaluar y ejecutar una sucesión de instrucciones línea por línea de principio a fin. A esto se le conoce copmo flujo de ejecución de... | github_jupyter |
### Testing accuracy of RF classifier for lightly loaded, testing and training with all the rotational speeds
```
from jupyterthemes import get_themes
import jupyterthemes as jt
from jupyterthemes.stylefx import set_nb_theme
set_nb_theme('chesterish')
import pandas as pd
data_10=pd.read_csv(r'D:\Acads\BTP\Lightly Loa... | 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 20K+ developers in learning how to responsibly <a href="https://madewithml.com/about/">deliver value</a> with ML.
... | github_jupyter |
# AWS. S3 Buckets
> 'Working with AWS S3 buckets'
- toc:true
- branch: master
- badges: false
- comments: false
- author: Alexandros Giavaras
- categories: [aws, s3-buckets, cloud-computing, data-storage, data-engineering, data-storage, boto3]
## Overview
In this notebook, we are going to have a brief view on AWS ... | github_jupyter |
```
%matplotlib inline
```
# Demo Axes Grid
Grid of 2x2 images with single or own colorbar.
```
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import ImageGrid
plt.rcParams["mpl_toolkits.legacy_colorbar"] = False
def get_demo_image():
import numpy as np
from matplotlib.cbook import get_sa... | github_jupyter |
#Importo librerie
```
import pandas as pd
import numpy as np
import concurrent.futures
import time
from requests.exceptions import ReadTimeout
!pip install -U -q PyDrive
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredent... | github_jupyter |
# Notebook use to check the result of the classifier, how well can you detect the nucleus .
You can click `shift` + `enter` to run one cell, you can also click run in top menu.
To run all the cells, you can click `kernel` and `Restart and run all` in the top menu.
```
# Some more magic so that the notebook will reloa... | github_jupyter |
### Generator States
Let's look at a simple generator function:
```
def gen(s):
for c in s:
yield c
```
We create an generator object by calling the generator function:
```
g = gen('abc')
```
At this point the generator object is **created**, but we have not actually started running it. To do so, we ca... | github_jupyter |
```
import tkinter as tk
from tkinter import filedialog
from tkinter import *
from PIL import ImageTk, Image
# Load your model
model = load_model('Saved_model.h5') # Path to your model
# Initialise GUI
top=tk.Tk()
# Window dimensions (800x600)
top.geometry('800x600')
# Window title
top.title('Traffic sign classifica... | github_jupyter |
# Notebook 2: Setup Domain
<img src="img/tab_start.png" alt="tab" style="width: 100px; margin:0;" />
Now that we have sshed into our virtual machine (as described in the 👈🏿 notebook [01-data-owners-login.ipynb](01-data-owners-login.ipynb)), let's move on to provision our Domain node.
**Note:** These steps are desi... | github_jupyter |
# Importing libraries
```
import pandas as pd
import random
import numpy as np
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.utils import to_categorical
from tensorflow.keras import regularizers
import matplotlib.pyplot... | github_jupyter |
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