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# Generate QWI Statistics
## Python Setup
```
%pylab inline
# from __future__ import division
import pandas as pd
import psycopg2
# import seaborn as sns
# sns.set_style("white")
# sns.set_context("poster", font_scale=1.25, rc={"lines.linewidth":1.25, "lines.markersize":8})
```
## Connection to Database
```
db_nam... | github_jupyter |
# Gradient Centralization for Better Training Performance
**Author:** [Rishit Dagli](https://github.com/Rishit-dagli)<br>
**Date created:** 06/18/21<br>
**Last modified:** 06/18/21<br>
**Description:** Implement Gradient Centralization to improve training performance of DNNs.
## Introduction
This example implements ... | 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
torch.backends... | github_jupyter |
# Petfinder.my - Pawpularity Contest
Predict the popularity of shelter pet photos
<img src="https://storage.googleapis.com/kaggle-competitions/kaggle/25383/logos/header.png"></img>
Analyze raw images and metadata to predict the “Pawpularity” of pet photos. The Pawpularity Score is derived from each pet profile's page ... | github_jupyter |
<img alt="QuantRocket logo" src="https://www.quantrocket.com/assets/img/notebook-header-logo.png">
© Copyright Quantopian Inc.<br>
© Modifications Copyright QuantRocket LLC<br>
Licensed under the [Creative Commons Attribution 4.0](https://creativecommons.org/licenses/by/4.0/legalcode).
<a href="https://www.quantrocke... | github_jupyter |
```
from PIL import Image
import pandas as pd
import requests
import psycopg2
import io
from ratelimiter import RateLimiter
from tqdm import tqdm
from ast import literal_eval
#postgres connection
db_user = 'postgres'
db_password = ''
db_host = 'localhost'
db_port = 5432
database = 'met_data'
conn_str = f'postgresql:/... | github_jupyter |
# 附录A：关于布莱克-斯科尔斯-默顿模型的一些有用的推导式
BSM价格表达式如下:
$$C(S,K,\tau,\sigma,r)=SN(d_1)-Ke^{-r\tau}N(d_2)$$
$$d_1=\frac{\ln\left(\frac{S_F}{K}\right)+\frac{\sigma^2}{2}\tau}{\sigma\sqrt{\tau}}$$
$$d_2=\frac{\ln\left(\frac{S_F}{K}\right)-\frac{\sigma^2}{2}\tau}{\sigma\sqrt{\tau}}$$
$$S_F=e^{r\tau}S$$
$$N(x)=\frac{1}{\sqrt{2\pi}}... | github_jupyter |
```
#@title ##### License
# Copyright 2018 The GraphNets Authors. 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
#
# Un... | github_jupyter |
```
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import torchvision.utils as vutils
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=(0.5,), std=(0.5,))]... | github_jupyter |
# 1810 - Comparing CNN architectures
```
# Imports
import sys
import os
import time
import math
import random
# Add the path to the parent directory to augment search for module
par_dir = os.path.abspath(os.path.join(os.getcwd(), os.pardir))
if par_dir not in sys.path:
sys.path.append(par_dir)
# Plotting imp... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import requests
import datetime as dt
from sklearn.decomposition import PCA
from patsy import dmatrices
import statsmodels.api as sm
import statsmodels.formula.api as smf
import warnings
warnings.filterwarnings("ignore")
import pymc3 as pm
impor... | github_jupyter |
# Basics of Coding
In this chapter, you'll learn about the basics of objects, types, operations, conditions, loops, functions, and imports. These are the basic building blocks of almost all programming languages.
This chapter has benefited from the excellent [*Python Programming for Data Science*](https://www.tomasbe... | 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) Marc Spiegelman</td>
</table>
```
%matplotlib inline
import numpy as np
import scipy.linalg as la
import matpl... | github_jupyter |
# 10. Непрерывные случайные величины. Плотность.
<img src='data/r3.png' width=400/>
<img src='data/r1.png' width=400/>
<img src='data/lebeg.png' width=400/>
## Содержание
* [Теория](#chapter1)
* [Задачи на пару](#chapter2)
* [Домашнее задание](#chapter3)
* [Гробы](#chapter4)
## Теория <a class="anchor" id="chapte... | github_jupyter |
```
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
df = pd.read_csv(r"G:\Udemy Courses\Data Science\Refactored_Py_DS_ML_Bootcamp-master\13-Logistic-Regression\titanic_train.csv")
df.head()
df.describe()
df.columns
df.isnull().sum()
sns.heatmap(df.isnull(),cbar=False,ytickla... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import os,sys,inspect
current_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parent_dir = os.path.dirname(os.path.dirname(current_dir))
sys.path.insert(0, parent_dir)
import numpy as np
import torch
import torch.nn as nn
import torch.nn.function... | github_jupyter |
```
import numpy as np
import pandas as pd
#import matplotlib.pylab as plt
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import silhouette_score
from sklearn import cluster
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
import seaborn as ... | github_jupyter |
# Introduction to Data Visualization with Seaborn
### Data file : graduates.csv [https://think.cs.vt.edu/corgis/csv/graduates]
This file contains information about students graduating from American institutes for 11 years between 1993 and 2015. For every year and Major of graduation, there is information about the dist... | github_jupyter |
```
%matplotlib inline
import gym
import matplotlib
import numpy as np
import sys
from collections import defaultdict
if "../" not in sys.path:
sys.path.append("../")
from lib.envs.blackjack import BlackjackEnv
from lib import plotting
matplotlib.style.use('ggplot')
env = BlackjackEnv()
def mc_prediction(policy,... | github_jupyter |
## AI for Medicine Course 1 Week 1 lecture exercises
# Data Exploration
In the first assignment of this course, you will work with chest x-ray images taken from the public [ChestX-ray8 dataset](https://arxiv.org/abs/1705.02315). In this notebook, you'll get a chance to explore this dataset and familiarize yourself wit... | github_jupyter |
## **Git Routine 2**: Extend your current code and use Git, GitHub to keep track of changes and contribute to collaborative projects
In this exercise, we will use the personal forks as the repository where each of us will contribute.
## 1. Navigate into your folder named under your GitHub username
```
cd <my usernam... | github_jupyter |
# 6.1 量子搜索算法
## 练习 6.1
:::{admonition} 练习 6.1
证明对应于 Grover 迭代中，相移的酉算符是 $2 |0\rangle \langle0| - I$。
:::
这是比较显然的。一种理解方式是矩阵表示。另一种则是将该相移算符作用于具体的态 (下式的 $x > 0$)：
$$
\begin{align*}
(2 |0\rangle \langle0| - I) |0\rangle &= 2 |0\rangle \langle0|0\rangle - |0\rangle = |0\rangle \\
(2 |0\rangle \langle0| - I) |x\rangle &=... | github_jupyter |
<a id='start'></a>
# Decision Tree
In questo notebook viene spiegato cosa sono e come possiamo realizzare dei Decision Tree con Python. <br>
<br>
Il notebook è suddiviso nelle seguenti sezioni:<br>
- [Definizione](#section1)
- [Esempio](#section2)
- [Lavoriamo con Python](#section3)
- [I boosted decision trees](#secti... | github_jupyter |
<a href="https://colab.research.google.com/github/mc-robinson/random/blob/master/pytorch_and_fastai_molecular_tabular_learner.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Attempting to use molecular data with fastai/pytorch
```
# must use new ... | github_jupyter |
<center>
<img src="https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/images/IDSNlogo.png" width="300" alt="cognitiveclass.ai logo" />
</center>
# Simple Linear Regression
Estimated time needed: **15** minutes
## Objectives
After ... | github_jupyter |
# K-Nearest Neighbor Regressor with StandardScaler
This Code template is for the regression analysis using a simple KNeighborsRegressor based on the K-Nearest Neighbors algorithm and feature rescaling technique StandardScaler in a pipeline.
### Required Packages
```
import warnings
import numpy as np
import panda... | github_jupyter |
# Inception_V3 model summary
```
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
from keras.applications.inception_v3 import InceptionV3
model = InceptionV3()
p... | github_jupyter |
## Dependencies
```
!pip install --quiet efficientnet
# !pip install --quiet image-classifiers
import warnings, json, re, glob, math
# from scripts_step_lr_schedulers import *
from melanoma_utility_scripts import *
from kaggle_datasets import KaggleDatasets
from sklearn.model_selection import KFold
import tensorflow.k... | github_jupyter |
```
import torch
import numpy as np
x= torch.tensor([[1,2,3],[4,5,6]])
y= torch.tensor([[7,8,9], [10,11,12]])
f= 2*x + y
print(f)
shape=[2,3]
xzeros =torch.zeros(shape)
xones = torch.ones(shape)
xrnd = torch.rand(shape)
print(xzeros)
print(xones)
print(xrnd)
torch.manual_seed(42)
print(torch.rand([2,3]))
import numpy ... | github_jupyter |
##### Copyright 2019 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
# χ² DISTRIBUSION AND χ² TEST
2020.01.23
```
"""
_______________________________
Chi-squere distribusion
_______________________________
* SciPy https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chi2.html
* Returned Value: sum of squared x-e... | github_jupyter |
# Multitask GP Regression
## Introduction
Multitask regression, introduced in [this paper](https://papers.nips.cc/paper/3189-multi-task-gaussian-process-prediction.pdf) learns similarities in the outputs simultaneously. It's useful when you are performing regression on multiple functions that share the same inputs, e... | github_jupyter |
<a id="section0"></a>
## O que vamos aprender nessa aula:
1. [Paradigmas de Programação](#1.-Paradigmas-de-Programação)<br>
1.1. [Programação Imperativa](#1.1.-Programação-Imperativa)<br>
1.2. [Programação Orientada a Objetos](#1.2.-Programação-Orientada-a-Objetos)<br>
... | github_jupyter |
|<img style="float:left;" src="http://pierreproulx.espaceweb.usherbrooke.ca/images/usherb_transp.gif" > |Pierre Proulx, ing, professeur|
|:---|:---|
|Département de génie chimique et de génie biotechnologique |** GCH200-Phénomènes d'échanges I **|
### Section 10.7, Conduction de la chaleur dans une ailette
<img src='h... | github_jupyter |
```
import pandas as pd
import numpy as np
import os
raw_data_path=os.path.join(os.path.pardir,'data','raw')
train_file_path=os.path.join(raw_data_path,'train.csv')
test_file_path=os.path.join(raw_data_path,'test.csv')
#read the data with all default params
train_df=pd.read_csv(train_file_path,index_col='PassengerId')
... | github_jupyter |
# Imports
```
import pandas as pd
import numpy as np
import pathlib
import calendar
import pickle
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
```
# Data Connection
```
from sqlalchemy import create_engine
database_filename = 'tdpdata.db'
table_name = 'tdpsheet'
engine = creat... | github_jupyter |
# Re-creating [Capillary Hysteresis in Neutrally Wettable Fibrous Media: A Pore Network Study of a Fuel Cell Electrode](http://link.springer.com/10.1007/s11242-017-0973-2)
# Part C: Purcell Meniscus Model
## Introduction
In the final part of this series we take a deeper look at the Purcell meniscus model, which is ce... | github_jupyter |
```
# Standard library imports
from argparse import ArgumentParser
import os, sys
THIS_DIR = os.path.abspath('')
PARENT_DIR = os.path.dirname(os.path.abspath(''))
sys.path.append(PARENT_DIR)
# Third party imports
import torch
from torch.nn import functional as F
from torch.utils.data import DataLoader
import pytorch_l... | github_jupyter |
```
!pip install transformers==4.11.2 datasets soundfile sentencepiece torchaudio pyaudio
from transformers import *
import torch
import soundfile as sf
# import librosa
import os
import torchaudio
# model_name = "facebook/wav2vec2-base-960h" # 360MB
model_name = "facebook/wav2vec2-large-960h-lv60-self" # 1.18GB
proce... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Automated Mach... | github_jupyter |
# Python
```
파이썬은 모든 것이 객체이다.
객체는 어떠한 속성값과 행동을 가지고 있는 데이터이다.
```
## [파이썬 인터프리터]
```
우리가 파이썬 코드를(스크립트를) 작성하고 실행하면 이 코드는 먼저 바이트 코드라는 것으로 변환되어 어딘가에 저장되고
이 바이트 코드는 파이썬 가상 머신(python virtual machine : PVM) 위에서 실행이 된다.
실제 파이썬의 프로그램 실행 주체는 PVM이고 PVM에 의해 가비지 컬렉션도 진행이 된다.
파이썬 코드 변환기와 가상 머신, 기본적으로 포함되는 각종 라이브러리들을 묶어서 파이썬 인터프... | github_jupyter |
# Introduction
## A quick overview of batch learning
If you've already delved into machine learning, then you shouldn't have any difficulty in getting to use incremental learning. If you are somewhat new to machine learning, then do not worry! The point of this notebook in particular is to introduce simple notions. W... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
% matplotlib inline
plt.rcParams["figure.dpi"] = 300
np.set_printoptions(precision=3, suppress=True)
import pandas as pd
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing impo... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
from childes_mi.utils.paths import DATA_DIR, FIGURE_DIR
from childes_mi.utils.general import flatten,save_fig
from childes_mi.information_theory import model_fitting as mf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from tqdm.autonotebook import tqdm
MI_... | github_jupyter |
```
%%html
<style>
table {float:left}
</style>
```
# Jinja2 Simple YAML Example
---
We're now going to take a look at grabbing a file from the hard drive written in [YAML](http://www.yaml.org/)
syntax. YAML is arguably the most human readable data serialization format which makes it really easy for coders and non-co... | github_jupyter |
```
%run notebook_setup
```
# Getting started with The Joker
*The Joker* (pronounced Yo-kurr) is a highly specialized Monte Carlo (MC) sampler that is designed to generate converged posterior samplings for Keplerian orbital parameters, even when your data are sparse, non-uniform, or very noisy. This is *not* a genera... | github_jupyter |
## Protodash Explanations for Text data
In the example shown in this notebook, we train a text classifier based on [UCI SMS dataset](https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection) to distinguish 'SPAM' and 'HAM' (i.e. non spam) SMS messages. We then use the ProtodashExplainer to obtain spam and ham proto... | github_jupyter |
PyGSLIB
========
Declustering
---------------
This is how declustering works
```
#general imports
import matplotlib.pyplot as plt
import pygslib
import numpy as np
#make the plots inline
%matplotlib inline
```
Getting the data ready for work
---------
If the data is in GSLIB format you can use the function `p... | github_jupyter |
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-59152712-8"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-59152712-8');
</script>
# The Spinning Effective One-Body Factorized Modes
## Auth... | github_jupyter |
```
install.packages('gbm')
library(gbm)
```
# Read training data file
```
train_raw <- read.table("../data/train_FD001.txt",
sep=" ",
colClasses=c(rep("numeric", 2), rep("double", 24), rep("NULL", 2)),
col.name=c("id", "cycle", "setting1", "setting2", "setting3",
"s1", "s2", "s3", "s4", ... | github_jupyter |
# Change-over-Time
Give emphasis to changing trends. These can be short (intra-day) movements or extended series traversing decades or centuries: Choosing the correct time period is important to provide suitable context for the reader.
```
import pandas as pd
import numpy as np
#ggplot equivalent: plotnine
from plotni... | github_jupyter |
##### Copyright 2020 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | github_jupyter |
# Text Generation
## Introduction
Markov chains can be used for very basic text generation. Think about every word in a corpus as a state. We can make a simple assumption that the next word is only dependent on the previous word - which is the basic assumption of a Markov chain.
Markov chains don't generate text as ... | github_jupyter |
# Training with SageMaker Pipe Mode and TensorFlow using the SageMaker Python SDK
SageMaker Pipe Mode is an input mechanism for SageMaker training containers based on Linux named pipes. SageMaker makes the data available to the training container using named pipes, which allows data to be downloaded from S3 to the con... | github_jupyter |
# Project Report: bci4als
#### Authors: Evyatar Luvaton, Noam Siegel
This software was developed for the course Brain-Computer-Interface for ALS Patients, December 2020.
Over the mid-semester project we have integrated the different parts of BCI which been discussed in the course.
We introduce bci4als, a complete pip... | github_jupyter |
<a href="https://colab.research.google.com/github/pablo-arantes/making-it-rain/blob/main/AlphaFold2%2BMD.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# **Hello there!**
This is a Jupyter notebook for running Molecular Dynamics (MD) simulations u... | github_jupyter |
<a href="https://colab.research.google.com/github/arangoml/networkx-adapter/blob/doc_updates_nx/examples/batch_graph_pre_processing.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## Get Raw Data for Processing
```
%%capture
!git clone -b doc_updat... | github_jupyter |
```
info = {
"title": "North Pole",
"author": "Alex Carney",
"github_username": "alcarney",
"stylo_version": "0.9.0",
"dimensions": (1920, 1080)
}
import numpy as np
import numpy.random as npr
from math import pi
from stylo.domain.transform import translate
from stylo.color import FillColor
from stylo.shape impor... | github_jupyter |
<a href="https://colab.research.google.com/github/OUCTheoryGroup/colab_demo/blob/master/Change_detection_PCA_KM.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## 基于 PCA 和 K-Means 的SAR图像变化检测
T. Celik, Unsupervised Change Detection in Satellite Imag... | github_jupyter |
```
import numpy as np
import pandas as pd
from vtk import *
from vtk.util.numpy_support import vtk_to_numpy
import matplotlib.pyplot as plt
```
### Memory quad: 184 MB, lin: 132 MB
### Runtime quad: 24.9 s, lin: 8.4 s
```
plt.rcParams['legend.fontsize']=12
plt.rcParams['font.size'] = 14
#plt.rcParams['lines.linewidt... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datareader import read_data, algs, display_ranks, combine_all_metrics
```
# Reading data
```
data, scores, test_users = read_data('Results for MovieLen', 'ML')
all_metrics = combine_all_metrics(scores, data)
all_me... | github_jupyter |
```
import os
import numpy as np
import matplotlib.pyplot as plt
```
Computations performed in this notebook may take hours. Therefore, we have implemented notebook extension called `skip_cell` which could be used to omit certain cells. We defined global variables `IMPATIENCE` which should be set to `True` when you wa... | github_jupyter |
# Self Organizing Map (SOM)
Notebook ini berdasarkan kursus __Deep Learning A-Z™: Hands-On Artificial Neural Networks__ di Udemy. [Lihat Kursus](https://www.udemy.com/deeplearning/).
## Informasi Notebook
- __notebook name__: `taruma_udemy_som`
- __notebook version/date__: `1.0.1`/`20190729`
- __notebook server__: Go... | github_jupyter |
<table class="ee-notebook-buttons" align="left"><td>
<a target="_blank" href="https://colab.research.google.com/github/giswqs/qgis-earthengine-examples/blob/master/Folium/export-ee-data.ipynb">
<img src="https://www.tensorflow.org/images/colab_logo_32px.png" /> Run in Google Colab</a>
</td><td>
<a target="_blank" ... | github_jupyter |
```
# Scripts and doodles for getting Fortnite data.
# Refactor to save to db and make calls daily or weekly.
# External API calls commented out to prevent calls when viewed on GitHub.
# All Fortnite data and rights belong to Epic Games.
# Fortnite API - https://fortniteapi.com/
# Description of Fortnite Item Shop
# h... | github_jupyter |
```
### This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I... | github_jupyter |
## Price Predictor
```
import pandas as pd
import numpy as np
housing = pd.read_csv("data.csv")
housing.head()
housing.info()
housing.describe()
housing['CRIM']
housing['CHAS']
housing['CHAS'].value_counts()
%matplotlib inline
import matplotlib as plt
housing.hist(bins = 50, figsize=(20,15))
```
## Train-Test Splitti... | github_jupyter |
# 旷视AI智慧交通开源赛道-交通标志识别
* 队伍：仙交小分队
* 初赛得分：
本次的比赛是交通标志检测，交通标志本身种类众多，大小不定，并且在交通复杂的十字路口场景下，由于光照、天气等因素的影响，使其被精确检测变得更加困难。通过反复实验，我们选择了今年旷视新出的YOLOX目标检测框架，megengine版本的yolox代码相对于pyotorch版本的bug较多，通过反复调试，我们最终的方案是：YOLOX_L + P6 + Focalloss + Inputsize2048 + 双线性插值上采样 + dataaug30，以下是整个调试的过程。
## 数据分析
根据官方提供的示例代码，我们主要是在预训练的模型上面做微调。... | github_jupyter |
```
# default_exp optimizer
#export
from fastai.torch_basics import *
#hide
from nbdev.showdoc import *
```
# Optimizer
> Define the general fastai optimizer and the variants
## `_BaseOptimizer` -
```
#export
class _BaseOptimizer():
"Common functionality between `Optimizer` and `OptimWrapper`"
def all_param... | github_jupyter |
# Pearson's Product-Moment Correlation
In this tutorial, we explore
- The theory behind the Pearson test statistic and p-value
- The features of the implementation
## Theory
The following description is adapted from [[1]](https://arxiv.org/abs/1907.02088):
Pearson's product-moment correlation is a measure of the l... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
from IPython.display import Image
```
Chapter 7 n-step Bootstrapping
=================
MC <-- n-step TD --> one-step TD
```
Image('./res/fig7_1.png')
```
Monte Carlo return:
$G_t \doteq R_{t+1} + \gamma R_{t+2} + \gamma^2 R_{t+3} + \cdo... | github_jupyter |
```
# This allows multiple outputs from a single jupyter notebook cell:
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
%matplotlib inline
import pandas as pd
```
---
## There will be three (or four) kwargs:
- `hline=` ... specify one or a list of prices to ... | github_jupyter |
```
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim.lr_scheduler import StepLR, MultiStepLR
import numpy as np
# import matplotlib.pyplot as plt
from math import *
import time
torch.cuda.set_device(2)
torch.set_default_t... | github_jupyter |
## Dependencies
```
!pip install --quiet /kaggle/input/kerasapplications
!pip install --quiet /kaggle/input/efficientnet-git
import warnings, glob
from tensorflow.keras import Sequential, Model
import efficientnet.tfkeras as efn
from cassava_scripts import *
seed = 0
seed_everything(seed)
warnings.filterwarnings('ig... | github_jupyter |
# Working with Bag of Words
-------------------------------------
In this example, we will download and preprocess the ham/spam text data. We will then use a one-hot-encoding to make a bag of words set of features to use in logistic regression.
We will use these one-hot-vectors for logistic regression to predict if... | github_jupyter |
### Sample code for Comparing NILM algorithms
```
from __future__ import print_function, division
import time
from matplotlib import rcParams
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from six import iteritems
%matplotlib inline
rcParams['figure.figsize'] = (13, 6)
from nilmtk import Da... | github_jupyter |
# Collaboration and Competition
---
You are welcome to use this coding environment to train your agent for the project. Follow the instructions below to get started!
### 1. Start the Environment
Run the next code cell to install a few packages. This line will take a few minutes to run!
```
!pip -q install ./pyth... | github_jupyter |
# Grid Search in REP
This notebook demonstrates tools to optimize classification model provided by __Reproducible experiment platform (REP)__ package:
* __grid search for the best classifier hyperparameters__
* __different optimization algorithms__
* __different scoring models__ (optimization of arbirtary figure o... | github_jupyter |
# Styling
*New in version 0.17.1*
<span style="color: red">*Provisional: This is a new feature and still under development. We'll be adding features and possibly making breaking changes in future releases. We'd love to hear your feedback.*</span>
This document is written as a Jupyter Notebook, and can be viewed or d... | github_jupyter |
# Normalization
```
#load packages
import pandas as pd
import numpy as np
from my_functions import *
import pyRserve
import os.path
import gc
from sklearn.experimental import enable_iterative_imputer
from sklearn.impute import KNNImputer
from sklearn.impute import SimpleImputer
from sklearn.impute import IterativeImpu... | github_jupyter |
# Lesson 8
## 00:01:58 - What we've learned so far
### 00:02:14 - Differentiable programming
* Should be called deep learning, should be called "differentiable programming".
* "Stacks of differentiable non-linear functions with lots of parameters solve nearly any predictive modeling problem".
* Can experiment wi... | github_jupyter |
# Class Diagrams
This is a simple viewer for class diagrams. Customized towards the book.
**Prerequisites**
* _Refer to earlier chapters as notebooks here, as here:_ [Earlier Chapter](Debugger.ipynb).
```
import bookutils
```
## Synopsis
<!-- Automatically generated. Do not edit. -->
To [use the code provided in... | github_jupyter |
# 3. Data Modelling: Systolic and Diastolic Failure Classification
```
# Import libraries
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import psycopg2
import getpass
%matplotlib inline
plt.style.use('ggplot')
from sklearn.model_selection import train_test_split
from sk... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import subprocess
from IPython.display import clear_output
title = "Gestorbene in Österreich nach Kalenderwoche"
subtitle = "Quelle: Statistik Austria (Stand 23. Dezember 2021)"
csv = pd.read_csv('https://data.statistik.... | github_jupyter |
```
from rdkit import Chem, DataStructs
from rdkit.Chem import AllChem
from rdkit.Chem import rdMolDescriptors as rdmd
import pandas as pd
from tqdm import tqdm
import time
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.cluster import MiniBatchKMeans
import matplotlib.pyplot as plt
mito=pd.rea... | github_jupyter |
<h2>CREAZIONE SERIE STORICHE DEI DECESSI COVID ACCERATI SU BASE REGIONALE IN BASE ALLA PERCENTUALE DELLA COPERTURA COMUNALE DEI DECESSI TOTALE
```
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('csv/decessi_covid19_regioni.csv')
df.head()
df.drop(['stato', 'codice_regione', 'l... | 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 |
```
import argparse
from typing import Dict
import logging
import torch
from torch import optim
from datasets import TemporalDataset
from optimizers import TKBCOptimizer, IKBCOptimizer
from models import ComplEx, TComplEx, TNTComplEx
from regularizers import N3, Lambda3
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_... | github_jupyter |
# pyckmeans
pyckmeans is a Python package for [Consensus K-Means](https://doi.org/10.1023/A:1023949509487) and [Weighted Ensemble Consensus of Random (WECR) K-Means](https://doi.org/10.1109/TKDE.2019.2952596) clustering, especially in the context of DNA sequence data. To evaluate the quality of clusterings, pyckmeans ... | github_jupyter |
# Setup
## Imports
```
from tqdm import tqdm_notebook
import gym
```
PyTorch modules
```
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from vai.torch.utils import cuda
```
## Define Useful Features
```
env = gym.make('Pong-ram-v... | github_jupyter |
<a href="https://colab.research.google.com/github/manashpratim/Urban-Sound-Classification/blob/master/Urban_Sound_Classification.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
#Downloading the Datasets
!wget --no-check-certificate \
"http... | github_jupyter |
```
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV, RandomizedSearchCV
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler,LabelEncoder
from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegre... | github_jupyter |
```
import sys
import os
from pprint import pprint
sys.path.append(os.path.abspath("../../../"))
import warnings
warnings.filterwarnings('ignore')
from IPython.display import display, HTML
from IPython.display import Markdown
import ipywidgets as iw
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt... | github_jupyter |
Hasta ahora, la forma en que hemos ido ejecutando las cosas ha sido total y completamente secuencial. Se ejecuta la linea 1, luego la 2, luego la 3... así hasta terminar la ejecución del programa. Pero usualmente queremos que ciertas piezas de código solamente se ejecuten si se dan ciertas condiciones, o que se ejecute... | github_jupyter |
# Truncated Gaussians examples
It is well known that the normal / Gaussian distribution is the distribution with maximum entropy subject to constraints on the first two moments. In this example we show the shapes of some distributions that arise when we place a bounds constraint in addition to constraints on the first... | github_jupyter |
```
%matplotlib inline
#%%writefile segmentation_3D_thirdversion.py
from segmentation_functions import resample,grow
from .finding_biggest_lung import arrange_slices, normalization, get_pixels_hu,creating_mask
from seed_evaluation import evaluate_seed
import scipy.ndimage.interpolation as inter
import numpy as np
impo... | github_jupyter |
# Language Translation
In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French.
## Get the Data
Since translating the whole lan... | github_jupyter |
# 向量
> 有值和方向的量
假设在二维平面有一个点 x = 2, y = 1
\begin{equation}\vec{v} = \begin{bmatrix}2 \\ 1 \end{bmatrix}\end{equation}
```
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "last_expr"
# %matplotlib inline
import math
import numpy as np
import matplotlib.pyplot as plt
... | github_jupyter |
```
import torch
import torch.nn.functional as F
import torchsde
import math
import matplotlib.pyplot as plt
import numpy as np
from tqdm.notebook import tqdm
from torch import _vmap_internals
from cfollmer.objectives import log_g, relative_entropy_control_cost
from cfollmer.sampler_utils import FollmerSDE,
from c... | github_jupyter |
```
import os, time
import pandas as pd
import numpy as np
from tqdm import tqdm_notebook
from selenium import webdriver as wd
from selenium.webdriver.common.by import By
train = pd.read_csv('../data/train.csv', usecols = ['id','latitude','longitude'])
test = pd.read_csv('../data/test.csv', usecols = ['id','latitude',... | github_jupyter |
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