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KnockoffDB
---
This class is responsible for building the tables and inserting the data into the database. It accomplishes this using a **KnockoffDatabaseService** provided to its \_\_init\_\_ to interact with the database for getting table definitions and uploading knockoff data. The **DefaultDatabaseService** is an ... | github_jupyter |
```
import xarray as xr
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#import seawater as sw
import cartopy.crs as ccrs # import projections
import cartopy.feature as cf # import features
from pandas import ExcelWriter
fig_dir='C:/Users/gentemann/Google Drive/... | github_jupyter |
# Solutions to Exercises
For each exercise, the solutions below show one possible way of solving it, but you might have used a different approach, and that's great! There is almost always more than one way to solve any particular problem in Python.
**Note**: To run this notebook, you'll need to either a) move it up o... | github_jupyter |
#### URI Converters (configurable)
[id_converter_link]:
https://github.com/Rothamsted/rdf2neo/blob/master/rdf2neo/src/main/java/uk/ac/rothamsted/rdf/neo4j/idconvert/DefaultIri2IdConverter.java
These converters allow us to simplify the representation of URIs. They can be configured
to perform custom conversion withi... | github_jupyter |
# K-mer comparisons!
This is a Jupyter Notebook using Python 3. You can use Shift-ENTER to run cells, and double click on code cells to edit them.
Contact: C. Titus Brown, ctbrown@ucdavis.edu
## Calculating Jaccard similarity and containment
Given any two collections of k-mers, we can calculate similarity and conta... | github_jupyter |
```
import random
import matplotlib.pyplot as plt
import math
import alignments as alg
import utils
human = utils.read_protein('data/alg_HumanEyelessProtein.txt')
fly = utils.read_protein('data/alg_FruitflyEyelessProtein.txt')
#print(len(human))
#print(len(fly))
scoring_matrix = utils.read_scoring_matrix('data/alg_PAM... | github_jupyter |
```
# Copyright 2021 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 |
**Chapter 16 – Natural Language Processing with RNNs and Attention**
_This notebook contains all the sample code in chapter 16._
<table align="left">
<td>
<a target="_blank" href="https://colab.research.google.com/github/ageron/handson-ml2/blob/master/16_nlp_with_rnns_and_attention.ipynb"><img src="https://www.... | github_jupyter |
# Description
This notebook computes predicted expression correlations between all genes in the MultiPLIER models.
It also has a parameter set for papermill to run on a single chromosome to run in parallel (see under `Settings` below).
# Modules
```
%load_ext autoreload
%autoreload 2
import numpy as np
from scipy.s... | github_jupyter |
```
"""
The MIT License (MIT)
Copyright (c) 2021 NVIDIA
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, pub... | github_jupyter |
# Cross Validation
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('../Data/Advertising.csv')
df.head()
```
----
----
----
## Train | Test Split Procedure
0. Clean and adjust data as necessary for X and y
1. Split Data in Train/Test for both X and y
... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Reducer/using_weights.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_blank" href="... | github_jupyter |
```
# First XGBoost model for Pima Indians dataset
import xgboost
from numpy import loadtxt
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# load data
dataset = loadtxt('pima-indians-diabetes.csv', delimiter=",")
# split data into X and ... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import requests
import csv
from bs4 import BeautifulSoup
from datetime import datetime, timedelta
import statsmodels.api as sm
from statsmodels.tsa import stattools
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.graphi... | github_jupyter |
In this lab, we will optimize the weather simulation application written in Fortran (if you prefer to use C++, click [this link](../../C/jupyter_notebook/profiling-c.ipynb)).
Let's execute the cell below to display information about the GPUs running on the server by running the nvaccelinfo command, which ships with t... | github_jupyter |
## Define the Convolutional Neural Network
After you've looked at the data you're working with and, in this case, know the shapes of the images and of the keypoints, you are ready to define a convolutional neural network that can *learn* from this data.
In this notebook and in `models.py`, you will:
1. Define a CNN w... | github_jupyter |
# Complete guide
## Introduction
This Notebook contains an overview of the basic functionality of the simulator. It introduces the simplest ways to get started with the simulator, and it dives into more advanced concepts that will allow you to get a sense of the flexibility of the system. At the end of this guide, yo... | github_jupyter |
```
import numpy as np
import pandas as pd
import pylab as plt
from matplotlib.font_manager import FontProperties
from collections import OrderedDict
import matplotlib.colors as colors
import matplotlib.cm as cmx
from mpl_toolkits.axes_grid1 import make_axes_locatable
# DDF summary on the COIN server:
file_extension ... | github_jupyter |
# Lab 1: Floating Point Numbers, Optimization & Gradient Descent
**Tomas Beuzen, January 2021**
In this lab, we'll work on solidifying concepts learned in Lecture 1 (floating point numbers) and Lecture 2 (optimization & gradient descent). This is the "vegetables" lab of the course - you're pr... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_parent" href="https://github.com/giswqs/geemap/tree/master/tutorials/ImageCollection/03_filtering_image_collection.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target... | github_jupyter |
```
import matplotlib.pyplot as plt
import numpy as np
import tifffile as tiff
import keras.backend as K
from keras.metrics import binary_crossentropy
from math import sqrt
from skimage.transform import resize
import logging
import sys
import tensorflow as tf
import sys; #sys.path.append('../')
from src.models.unet_dil... | github_jupyter |
```
import pandas as pd
from sklearn.mixture import GaussianMixture
import numpy as np
np.set_printoptions(precision=6)
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy.stats import multivariate_normal
data = pd.read_csv('for_tus.csv')
data.head()
data_np = data.to_numpy()
print(da... | github_jupyter |
```
import meds
import numpy as np
import galsim
import fitsio
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib notebook
import os
band = 'i'
tilename = 'DES2122+0001'
MEDS_DIR = 'outputs-%s' % tilename
MEDSCONF = 'y3v02'
meds_path = os.path.join(
MEDS_DIR,
'meds',
MEDSCONF,
tilenam... | github_jupyter |
### 1. EDA
___
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os.path
from sklearn.cluster import KMeans, DBSCAN, OPTICS
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
import seaborn as sns
import spectrai as spa
sns.set_context('notebook')
%load_ext autorel... | github_jupyter |
# Builder Tutorial number 6
The builder tutorials demonstrate how to build an operational GSFLOW model using `pyGSFLOW` from shapefile, DEM, and other common data sources. These tutorials focus on the `gsflow.builder` classes.
## Building modflow input files
In this tutorial, we demonstrate how to build modflow inpu... | github_jupyter |
# 吉布斯态的制备
<em> Copyright (c) 2021 Institute for Quantum Computing, Baidu Inc. All Rights Reserved. </em>
## 概览
在本案例中，我们将展示如何通过 Paddle Quantum 训练量子神经网络（quantum neural network, QNN）来制备量子吉布斯态。
### 背景
量子计算中的前沿方向包含量子机器学习和量子优化，在这两个方向中，特定量子态的制备是非常重要的问题。特别的，吉布斯态（Gibbs state）的制备是实现诸多量子算法所必须的步骤并且广泛应用于：
- 量子机器学习中受限波尔兹曼机的学习 ... | github_jupyter |
# Clustering Tutorial
This guide will show how to use Tribuo’s clustering models to find clusters in a toy dataset drawn from a mixture of Gaussians. We'll look at Tribuo's K-Means implementation and also discuss how evaluation works for clustering tasks.
## Setup
We'll load in some jars and import a few packages.
... | github_jupyter |
This notebook is part of the `nbsphinx` documentation: https://nbsphinx.readthedocs.io/.
# Code Cells
## Code, Output, Streams
An empty code cell:
Two empty lines:
```
```
Leading/trailing empty lines:
```
# 2 empty lines before, 1 after
```
A simple output:
```
6 * 7
```
The standard output stream:
```
pr... | github_jupyter |
# Machine Learning Engineer Nanodegree
## Unsupervised Learning
## Project: Creating Customer Segments
Welcome to the third project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and it will be your job to implement the additional functionality nece... | github_jupyter |
```
import sys
import os
import pandas as pd
import numpy as np
from rdkit import Chem
import rdkit.Chem.rdmolops as rdmolops
# If you installed the code from source and the import fails with the following error:
# (ModuleNotFoundError: No module named 'solvation_predictor')
# Make sure that you have activated the ... | github_jupyter |
### Import Modules
---
```
# TEMP
import os
print(os.getcwd())
import sys
import pickle
import numpy as np
import plotly.graph_objs as go
from sklearn.linear_model import LinearRegression
# #########################################################
from methods import get_df_features_targets
```
### Read Data
`... | github_jupyter |
```
"""
The MIT License (MIT)
Copyright (c) 2021 NVIDIA
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, pub... | github_jupyter |
# An RNN model to generate sequences
RNN models can generate long sequences based on past data. This can be used to predict stock markets, temperatures, traffic or sales data based on past patterns. They can also be adapted to [generate text](https://docs.google.com/presentation/d/18MiZndRCOxB7g-TcCl2EZOElS5udVaCuxnGzn... | github_jupyter |
# Pytorch Tutorial
### 1. Tensors and Dynamic Graphs
- Basic Operations involving ```torch.Tensor```.
- Introduction to the dynamic graphs of ```torch.Autograd```.
Setup torch and some variables
```
import torch
a = torch.ones(4,5) * 2
b = torch.ones(4,5) * 3
print('Tensor a:')
print(a)
print()
print('Tensor b:')
p... | github_jupyter |
# 基础因子实时计算
```
%matplotlib inline
import sys
sys.path.append('../')
sys.path.append('../../')
sys.path.append('../../../')
sys.path.append('../../../../')
sys.path.append('../../../../../')
import pandas as pd
import numpy as np
import seaborn as sns
from datetime import datetime
from matplotlib import pyplot as plt
... | github_jupyter |
<a href="https://colab.research.google.com/github/hinsley/colabs/blob/master/Pre_Swap_Gauss_Jordan_Elimination.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
This notebook is pretty old and may not be perfect.
```
import numpy as np
example_matri... | github_jupyter |
# An introduction to the project-k FORTH kernel
`project-k` is a very small FORTH programming language kernel supporting Javascript and Python open-sourced on GitHub https://github.com/hcchengithub/project-k. We are going to use this FORTH kernel to build our own tiny FORTH programming language system.
### Read only... | github_jupyter |
# Multiple Regression
Simple Linear Regression:
$$y = \beta_0 + \beta_1X$$
Multiple Linear Regression:
$$y = \beta_0 + \beta_1X_1 + \beta_2X_2 + ...$$
Well studied field in statistics
Focus will be on what is relevant for Data Science - practical and relevant for prediction
```
import numpy as np
import pandas a... | github_jupyter |
# Pandas TA ([pandas_ta](https://github.com/twopirllc/pandas-ta)) Strategies for Custom Technical Analysis
## Topics
- What is a Pandas TA Strategy?
- Builtin Strategies: __AllStrategy__ and __CommonStrategy__
- Creating Strategies
- Watchlist Class
- Strategy Management and Execution
- **NOTE:** The *... | github_jupyter |
This notebook was prepared by [Donne Martin](http://donnemartin.com). Source and license info is on [GitHub](https://github.com/donnemartin/data-science-ipython-notebooks).
# Functions
* Functions as Objects
* Lambda Functions
* Closures
* \*args, \*\*kwargs
* Currying
* Generators
* Generator Expressions
* itertools... | github_jupyter |
### SVM
```
import numpy as np
import matplotlib.pyplot as plt
import scipy.io as scio
from sklearn import svm
train_data = scio.loadmat("./ex6data1.mat")
# print(train_data)
X = train_data['X']
Y = train_data['y']
print(X.shape, Y.shape)
def plot_data(X, Y):
positive = X[Y[:,0] == 1]
negative = X[Y[:,0] == 0]... | 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 |
# بسم الله الرحمن الرحيم
```
# Load image
import cv2
image = cv2.imread("img/red_panda.jpg")
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow("Gray panda", gray_image)
cv2.imshow("Red panda", image)
cv2.waitKey(0)
cv2.destroyAllWindows()
# Save image
import cv2
image = cv2.imread("img/red_panda.jpg")... | github_jupyter |
### Program written by Scott Midgley, 2021
Scope: To train and test LR models for band gap energy screening in the configuraional space of MgO-ZnO solid solutions.
```
### USER INPUT REQUIRED ###
# Please paste in the path to the repositiory here an comment/uncomment as needed.
# E.g. rundir = r'C:\Users\<user>\Desk... | github_jupyter |
### Egeria Hands-On Lab
# Welcome to the Understanding Cohort Configuration Lab
## Introduction
Egeria is an open source project that provides open standards and implementation libraries to connect tools,
catal... | github_jupyter |
## **3. PCA**
### **a)**
```
import pandas as pd
from sklearn.datasets import load_breast_cancer
cancer_data = load_breast_cancer()
data = pd.DataFrame(cancer_data.data,columns=cancer_data.feature_names)
data.head(3)
```
<hr style = "border-top: 3px solid #000000 ; border-radius: 3px;">
<p style =" direction:rtl;te... | github_jupyter |
## Obligatory imports
```
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib
import seaborn as sns
import sklearn
%matplotlib inline
matplotlib.rcParams['figure.figsize'] = (10,6)
```
# MNIST Dataset
```
# Please click "Stay on page" on the popup!
import IPython.core.display
impor... | github_jupyter |
## Dependencies
```
import os, random, warnings
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
from sklearn.model_selection import train_test_split
from transformers import TFDistilBertModel
from tokenizers import BertWordPieceTokenizer
import tensorflow as tf
from te... | github_jupyter |
<table>
<tr align=left><td><img align=left src="./images/CC-BY.png">
<td>Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Kyle T. Mandli</td>
</table>
```
from __future__ import print_function
from __future__ import absolute_import
... | github_jupyter |
# Classification of Localizer Data
## Import necessary packages
```
%matplotlib inline
import glob
import os.path as op
import os as os
import nibabel as nib
import pandas as pd
import numpy as np
from nilearn.masking import compute_epi_mask
import matplotlib.pyplot as plt
import matplotlib as mpl
# Nilearn for ne... | github_jupyter |
```
import numpy as np
import pandas as pd
import time
import matplotlib.pyplot as plt
import seaborn as sns
import random
from bayes_opt import BayesianOptimization
sns.set()
BayesianOptimization?
np.around?
def get_state(data, t, n):
d = t - n + 1
block = data[d:t + 1] if d >= 0 else -d * [data[0]] + data[0:t... | github_jupyter |
# This is a live demo of video action recognition using two-stream architecture
This will clone my repo and download the models on drive and uses them to infer the output in a live frame-level demo. then an output video will be generated showing the output prediction for each frame accordingly.
I suggest running all... | github_jupyter |
# Gaussian Processes
## Introduction
[Gaussian Processes](https://en.wikipedia.org/wiki/Gaussian_process) have been used in supervised, unsupervised, and even reinforcement learning problems and are described by an elegant mathematical theory (for an overview of the subject see [1, 4]). They are also very attractive ... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import datetime
import os
import numpy as np
import matplotlib.pyplot as plt
import casadi as cas
import car_plotting as cp
%matplotlib inline
cp.plot_cars(x, x, x)
arr_img = plt.imread('red_car.png', format='png')
imagebox = OffsetImage(arr_img, zoom=1.0) #this zoom is to sc... | github_jupyter |
[](https://pythonista.io)
# Creación y lectura de archivos en formato *PDF*.
El formato *PDF* es un [estándar internacional](https://www.iso.org/standard/51502.html) para la creación de documentos que pueden ser desplegados o impresos de forma idéntica independientem... | github_jupyter |
## Проверка пайплайна обучения pix2pix для датасета MNIST
##### Задача image2image: научить сетку сдвигать числа на расстояние и в направлении, выбранными пользователем
```
import torch
from torch import nn
from torch.utils.data import Dataset, DataLoader
import torch.nn.funct... | github_jupyter |
#### Using SimpleElastix register all non class 1 CT scans and attempt to normalize them into a **healthy** CT scan.
```
import sys
import os
import csv
from collections import Counter
from configparser import ConfigParser
from glob import glob
import SimpleITK as sitk # pip install SimpleITK
from tqdm import tqdm # p... | github_jupyter |
### JaneStreet
### Plain NN
```
# Network for Jane Street Market Prediction on Kaggle
# https://www.kaggle.com/c/jane-street-market-prediction
# https://www.kaggle.com/wrinkledtime
# https://github.com/timestocome
# The Jane Street competition has blinded data and the goal is to predict stock market winners 6 months f... | github_jupyter |
```
import io
import sys
import random
import string
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import GRU
from keras.optimizers import RMSprop
def load_text(filename):
with open(filename, 'r') as f:
text = f.read()
return text
file_poem = '... | github_jupyter |
# <center> *mocalum* tutorial 4 <br><br> Monte-Carlo simulation for single-Doppler configuration <center>
A notebook by Nikola Vasiljević
## Introduction
In this section we will calculate wind speed uncertainty of a single-Doppler setup by means of Monte-Carlo simulations. We will consider a sector-scanning lida... | github_jupyter |
## A Wild Demo Grouper
```
为明确步骤，以下方法逻辑有冗余（包括代码先后顺序是不合理的），实际运行推荐使用C或Spark
```
### 0. Review
#### 0.0. DRG 基本概念
> **疾病诊断相关组(Diagnosis Related Groups，DRG)**是用于衡量 医疗服务质量效率以及进行医保支付的一个重要工具。DRG 实质上 是一种病例组合分类方案，即根据年龄、疾病诊断、合并症、并发症、 治疗方式、病症严重程度及转归和资源消耗等因素，将患者分入若干 诊断组进行管理的体系。
#### 0.1. DRG 付费适用范围
##### 适用范围
>DRG 是以划分医疗服务产出为目... | github_jupyter |
# Iris Training and Prediction with Sagemaker Scikit-learn
### Modified Version of AWS Example:
https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/scikit_learn_iris/Scikit-learn%20Estimator%20Example%20With%20Batch%20Transform.ipynb
Following modifications were made:
1. Incorpora... | github_jupyter |
# XFOIL
## Overview
XFOIL is a design and analysis tool for subsonic airfoils developed by Mark Drela at MIT.
The [XFOIL website](https://web.mit.edu/drela/Public/web/xfoil/) contains more info.
## Setup
As with the previous AVL tutorial, a copy of the XFOIL executable must be somewhere on your computer in order t... | github_jupyter |
# Convolutional Neural Networks: Application
Welcome to Course 4's second assignment! In this notebook, you will:
- Implement helper functions that you will use when implementing a TensorFlow model
- Implement a fully functioning ConvNet using TensorFlow
**After this assignment you will be able to:**
- Build and t... | github_jupyter |
# Chunking strategies for a Wide-ResNet
This tutorial shows how to utilize a hypernet container [HContainer](../hnets/hnet_container.py) and class [StructuredHMLP](../hnets/structured_mlp_hnet.py) (a certain kind of hypernetwork that allows *smart* chunking) in combination with a Wide-ResNet [WRN](../mnets/wide_resnet... | github_jupyter |
```
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from tqdm import tqdm
%matplotlib inline
from torch.utils.data import Dataset, DataLoader
import torch
import torchvision
import torch.nn as nn
import torch.optim as optim
from torch.nn import functional as F
device = torch.device("cuda" i... | github_jupyter |
# Sums of functions duplicated over a lattice
This example illustrates a method for evaluating sums of the form
$$
F(\mathbf{r}) =
\sum_{\mathbf{t}} \sum_{i=1}^{n_f} f\left(\mathbf{r}-\mathbf{t}-\mathbf{r}_i\right),
$$
where each $\mathbf{t}$ points to the origin of one cell in an infinite lattice and the vecto... | github_jupyter |
```
from database.market import Market
from database.sec import SEC
import pandas as pd
import pandas_datareader as pdr
from transformer.date_transformer import DateTransformer
from transformer.column_transformer import ColumnTransformer
from datetime import datetime
import matplotlib.pyplot as plt
from tqdm import tqd... | github_jupyter |
# <span style="color:green"> Numerical Simulation Laboratory (NSL) </span>
## <span style="color:blue"> Numerical exercises 8</span>
During this exercise you will variationally optimize the ground state of a single quantum particle in a one dimensional (1D) space confined by the following external potential:
$$
V(x) ... | github_jupyter |
```
%matplotlib inline
# Packages
import os, glob, scipy, sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.spatial.distance import pdist
# Project directory
base_dir = os.path.realpath('..')
print(base_d... | github_jupyter |
## Dataloaders for Machine Learning (Tensorflow & PyTorch)
This tutorial acts as a step by step guide for fetching, preprocessing, storing and loading the [MS-COCO](http://cocodataset.org/#home) dataset for image captioning using deep learning. We have chosen **image captioning** for this tutorial not by accident. For... | github_jupyter |
```
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier
from sklearn import svm
from sklearn.metrics import precision_score, recall_score
import matplotlib.pyplot as plt
#reading train.csv
data ... | github_jupyter |
```
%matplotlib inline
import numpy as np
from numpy.random import normal, randint
import matplotlib.pyplot as plt
import time
import sys
import gc
from sklearn.neural_network import MLPRegressor
from sklearn.model_selection import train_test_split
# Pyplot config
font = {'family': 'normal',
'weight': 'normal'... | github_jupyter |
# 10. Критерии однородности.
Есть 2 выборки $X_{[n]}$ и $Y_{[m]}$, нужно понять взяты ли они из одного распределения
## Критерий равенства матожиданий
[Теория](www.mathprofi.ru/proverka_statisticheskih_gipotez.html)
Статистика для равенства матожиданий
$Z = \frac{(\overline{X} - \overline{Y}) - (a_X - a_Y)}{\sqrt{\... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Objectives" data-toc-modified-id="Objectives-1"><span class="toc-item-num">1 </span>Objectives</a></span></li><li><span><a href="#Spark:-Getting-Started" data-toc-modified-id="Spark:-Getting-Star... | github_jupyter |
<a href="https://colab.research.google.com/github/nmningmei/Deep_learning_fMRI_EEG/blob/master/Han_et_al_2019_VAE_and_fMRI.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
from PIL import Image
```
# Introduction
1. understanding the human visu... | github_jupyter |
<a href="https://colab.research.google.com/github/RahulBarman101/Face-Gan/blob/master/gans.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!wget http://vis-www.cs.umass.edu/lfw/lfw.tgz
import tarfile
my_tar = tarfile.open('lfw.tgz')
my_tar.extra... | github_jupyter |
### Decision Tree Algorithm
* Implemented From Scratch
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import random
```
### Data Loading & Preparation
```
df=pd.read_csv('iris.csv')
df.rename(columns={'Name':'Label'},inplace=True)
df.head()
sns.lmplot(x='PetalWidth'... | github_jupyter |
# LinearSVR with Scale & Polynomial Features
This Code template is for the Classification task using Linear Support Vector Regression(LinearSVR) based on the Support Vector Machine algorithm with PolynomialFeatures as Feature Transformation Technique, rescaling technique scaling in a pipeline
# Required Packages
``... | github_jupyter |
# Goal
The goal of this notebook is to extract the **kkanji.tar** file which results in an extraction of 1 folder with 3.832 subfolders which are named with the correspondent unicode name. Each subfolder consists of png files of the Kanji characters.
To make it easier to process and share the data, i converted all ima... | github_jupyter |
# 9. Convolutional Neural Network with PyTorch
## 1. About Convolutional Neural Network
### 1.1 Transition From Feedforward Neural Network
#### 1 Hidden Layer Feedforward Neural Network
<img src="./images/nn1_new.png" alt="deeplearningwizard" style="width: 900px;"/>
#### Basic Convolutional Neural Network
- Addition... | github_jupyter |
# RadarCOVID-Report
## Data Extraction
```
import datetime
import json
import logging
import os
import shutil
import tempfile
import textwrap
import uuid
import matplotlib.pyplot as plt
import matplotlib.ticker
import numpy as np
import pandas as pd
import pycountry
import retry
import seaborn as sns
%matplotlib in... | github_jupyter |
# Sentiment Analysis: Primera Exploración
Ya tenemos un buen modelo. Queremos mejorarlo.
Las opciones son tantas que el enfoque es explorar superficialmente cada una.
```
%load_ext autoreload
%autoreload 2
from util import load_datasets
train, dev, test = load_datasets()
X_train, y_train = train
X_dev, y_dev = dev
X... | github_jupyter |
# AutoML for Text Classification
## Learning Objectives
1. Learn how to create a text classification dataset for AutoML using BigQuery
1. Learn how to train AutoML to build a text classification model
1. Learn how to evaluate a model trained with AutoML
1. Learn how to predict on new test data with AutoML
## Introdu... | github_jupyter |
# Scoring your trained model
In the cell below, please load your model into `model`. Also if you used an image size for your input images that *isn't* 224x224, you'll need to set `image_size` to the size you used. The scoring code assumes square input images.
For example, this is how I loaded in my checkpoint:
```py... | github_jupyter |
# Pedestrian example of subscribing to a DataSet
It is possible to *subscribe* to a dataset. Subscribing means adding a function to the dataset and having the dataset call that function every time a result is added to the dataset (or more rarely, see below).
### Call signature
The subscribing function must have the ... | github_jupyter |
# Post-training dynamic range quantization
**Learning Objectives**
1. We will learn how to train a TensorFlow model.
2. We will learn how to load the model into an interpreter.
3. We will learn how to evaluate the models.
## Introduction
[TensorFlow Lite](https://www.tensorflow.org/lite/) now supports
converti... | github_jupyter |
<p><font size="6"><b> CASE - Bike count data</b></font></p>
> *© 2021, Joris Van den Bossche and Stijn Van Hoey (<mailto:jorisvandenbossche@gmail.com>, <mailto:stijnvanhoey@gmail.com>). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*
---
<img src="https://static.nieuwsblad.... | github_jupyter |
```
import os
import numpy as np
import cv2
import random
coco_dict = {}
with open("coco.names", "r") as f:
ls = f.readlines()
i=0
for cls in ls:
coco_dict[cls.strip("\n")] = i
i=i+1
f.close()
imagespath = "./ExDark/"
annotationspath = "./ExDark_Annno/"
classes = os.listdir(images... | github_jupyter |
# Evaluation with ML
The point of this section is to check whether the generated data can be used to train new models. I will do this mostly by training a classifier on the generated data and then perform inference on the original data. The 'test set' data will in this case give a good indication of how usable the data... | github_jupyter |
# Create undersampled k-space
This demonstration shows how to create different undersampled k-space data which can be used either directly for image reconstruction or used to simulate MR data acquisition of a new object.
This demo is a 'script', i.e. intended to be run step by step in a
Python notebook such as Jupyter... | github_jupyter |
# Explainable AI
[作業說明投影片](https://docs.google.com/presentation/d/13xUwWArz0LROgyJBwGCf1Vili5u7l4K6WcyuRxxakAo/)
[Homework Introduction](https://docs.google.com/presentation/d/13xUwWArz0LROgyJBwGCf1Vili5u7l4K6WcyuRxxakAo/)
本作業不提供 python script 版本
There is no python script version for this homework
若有任何... | github_jupyter |
# Lab: Transfer Learning
Welcome to the lab on Transfer Learning! Here, you'll get a chance to try out training a network with ImageNet pre-trained weights as a base, but with additional network layers of your own added on. You'll also get to see the difference between using frozen weights and training on all layers.
... | github_jupyter |
```
from IPython.core.display import HTML
with open('style.css', 'r') as file:
css = file.read()
HTML(css)
```
# Die Bekehrung der Ungläubigen
Drei Missionare und drei Ungläubige wollen zusammen einen Fluss überqueren, denn die Ungläubigen sollen in der Kirche, die sich auf dem anderen Ufer befindet, getauft werd... | github_jupyter |
## Creating Landsat Timelapse
**Steps to create a Landsat timelapse:**
1. Pan and zoom to your area of interest, or click the globe icon at the upper left corner to search for a location.
2. Use the drawing tool to draw a rectangle anywhere on the map.
3. Adjust the parameters (e.g., start year, end year, title) if n... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Learn-to-Augment-Images-and-Multiple-Bounding-Boxes-for-Deep-Learning-in-4-Steps" data-toc-modified-id="Learn-to-Augment-Images-and-Multiple-Bounding-Boxes-for-Deep-Learning-in-4-Steps-1"><span class="toc-i... | github_jupyter |
## Load data files
```
import codecs
from keras.utils.np_utils import to_categorical
import numpy as np
def load_data(filename):
data = list(codecs.open(filename, 'r', 'utf-8').readlines())
x, y = zip(*[d.strip().split('\t') for d in data])
x = np.asarray(list(x))
y = to_categorical(y, 3)
ret... | github_jupyter |
```
from __future__ import print_function
import tensorflow as tf
from tensorflow import keras
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense,Dropout,Activation,Flatten,BatchNormalization
from keras.layers import Conv2D,MaxPooling2D
import os
... | github_jupyter |
## Requirements
Before using this tutorial, ensure that the following are on your system:
- <b>SteganoGAN is installed</b>. Install via pip or source code.
- <b>Training and Validation Dataset are available </b>. Download via data/download.sh or retrieve your own.
It is also suggested that you have the following:
... | github_jupyter |
# Los diccionarios
Son junto a las listas las colecciones más utilizadas. Se basan en una estructura mapeada donde cada elemento de la colección se encuentra identificado con una clave única. Por tanto, no puede haber dos claves iguales. En otros lenguajes se conocen como arreglos asociativos.
```
vacio = {}
vacio
```... | github_jupyter |
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