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# Value-at-Risk for Stocks: Delta-Normal Approach, EWMA
### Lecture Notes by Jakov Ivan S. Dumbrique (jdumbrique@ateneo.edu)
MATH 100.2: Topics in Financial Mathematics II \
First Semester, S.Y. 2021-2022 \
Ateneo de Manila University
```
import numpy as np # Numerical Computing
import pandas as pd # Data wrangling
... | github_jupyter |
```
import keras
import tensorflow as tf
print('TensorFlow version:', tf.__version__)
print('Keras version:', keras.__version__)
import os
from os.path import join
import json
import random
import itertools
import re
import datetime
import cairocffi as cairo
import editdistance
import numpy as np
from scipy import ndim... | github_jupyter |
### Imports
```
from datetime import datetime
import time
from contracts_lib_py.account import Account
from common_utils_py.agreements.service_types import ServiceTypesIndices
from nevermined_sdk_py import Config, Nevermined
from nevermined_sdk_py.nevermined.keeper import NeverminedKeeper as Keeper
CONSUMER_ADDRES... | github_jupyter |
# ODYM Example no. 5. Estimating the material content of the global vehicle fleet
ODYM was designed to handle extensive MFA systems by covering multiple aspects (time, age-cohort, region, material, chemical elements, processes, goods, components, ...) in a systematic manner. Its data format is used to structure and st... | github_jupyter |
```
# for use in tutorial and development; do not include this `sys.path` change in production:
import sys ; sys.path.insert(0, "../")
```
# Statistical Relational Learning with `pslpython`
In this section we'll explore one form of
[*statistical relational learning*](../glossary/#statistical-relational-learning)
cal... | github_jupyter |
```
from utils import *
from defense import *
from skimage.measure import compare_ssim
import argparse
import imutils
import cv2
config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
sess = tf.Session(config=config)
def ssim_score(cleandata,data):
# cleandata = (cleandata * 255).astype('uint8')
# ... | github_jupyter |
# Black Scholes Model
The Black Scholes model is considered to be one of the best ways of determining fair prices of options. It requires five variables: the strike price of an option, the current stock price, the time to expiration, the risk-free rate, and the volatility.
## Black and Scholes componets
- C = call opt... | github_jupyter |
# Tratamento de Dados Radioativos
Importação das bibliotecas utilizadas
```
import re
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
```
Leitura de arquivos contendo os dados
```
enviroment = open('enviroment.txt')
radioactive_source = open('radioactive_source.txt')
uranite = open('uranite.t... | github_jupyter |
# SSD
This is to go through each important step of SSD.
Firstly, load the model. You only need to do this one time.
```
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (10, 10)
plt.rcParams['image.interpolation'] = 'nearest'
import numpy as np
import os
os.chdir('..')
caffe_root ... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
from OpenGoddard.optimize import Problem, Guess, Condition, Dynamics
from rocket import Rocket
r = Rocket()
r
def og_dynamics(prob, obj, section):
#extract states and controls
s = tuple([prob.states(i, section) for i in range(5)])
u = tuple([prob.... | github_jupyter |
```
%pylab inline
import pandas as pd
import os
# Just use 1 GPU
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = ""
import pandas as pd
from pyvirchow.io import WSIReader
from pyvirchow.morphology import TissuePatch
from matplotlib.patches import Polygon
from sh... | github_jupyter |
# Spark Learning Note - MLlib
Jia Geng | gjia0214@gmail.com
<a id='directory'></a>
## Directory
- [Data Source](https://github.com/databricks/Spark-The-Definitive-Guide/tree/master/data/)
- [1. Some Machine Learning Examples](#sec1)
- [2. Classic ML Developmental Stages](#sec2-1)
- [3. Spark MLlib Overview](#sec3)
... | github_jupyter |
## Aplicando Pipeline na base de dados adult.data disponivel em: https://archive.ics.uci.edu/ml/datasets/Adult
#### Resumo : Preveja se a renda excede US $ 50 mil / ano com base nos dados do censo. Também conhecido como conjunto de dados "Renda do Censo".
* Informações sobre atributos:
* Listagem de atributos:
* re... | github_jupyter |
# TORCHVISION.TRANSFORMS
```
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import torch
from matplotlib.pyplot import imshow
from torchvision.transforms import ToPILImage
def get_transform(centercrop, resize, totensor, normalize, normalize2):
options = []
if centercrop:
... | github_jupyter |
## 01. Object-oriented programming
In __procedural programming__ paradigm, the focus is on writing functions or procedures which operate on data. While in __object-oriented programming__ the focus is on the creation of objects which contain both data and functionality together.
## 02. User Defined Classes
If the firs... | github_jupyter |
# Ensembles and Predictions Clipping
The combination of predictions from several methods to one forecast often leads to great performance improvements.
## Simple Ensembles
The most common strategy just takes an average of all the forecast, which often leads to surprisingly good results, for more on this topic, see fo... | github_jupyter |
# `Практикум по программированию на языке Python`
<br>
## `Занятие 4: Основы ООП, особенности ООП в Python`
<br><br>
### `Мурат Апишев (mel-lain@yandex.ru)`
#### `Москва, 2020`
### `Парадигмы проектирования кода`
Императивное программирование (язык ассемблера)
`mov ecx, 7`
Декларативное программирование (SQL)
`... | github_jupyter |
```
!curl -s https://course.fast.ai/setup/colab | bash
from google.colab import drive
drive.mount('/content/gdrive', force_remount=True)
root_dir = "/content/gdrive/My Drive/"
base_dir = root_dir + 'fastai-v3/'
```
**Important note:** You should <mark>always work on a duplicate of the course notebook</mark>. On the pa... | github_jupyter |
# Load Packages
```
import sys
sys.path.append('..')
from numpy_fracdiff import fracdiff
import numpy as np
import scipy.special
import matplotlib.pyplot as plt
%matplotlib inline
#!pip install memory_profiler
import memory_profiler
%load_ext memory_profiler
```
# Load Demo Data
```
with np.load('data/demo1.npz') as... | github_jupyter |
```
import pandas as pd
from ast import literal_eval
import matplotlib.pyplot as plt
import matplotlib
plt.style.use('fivethirtyeight')
%matplotlib inline
!ls
#Importing the data
df = pd.read_csv('readable_cleaned.csv')
del df['Date.1']
df.index = pd.to_datetime(df['Date'], format='%Y-%m-%d %H:%M:%S')
df.info()
```
# ... | github_jupyter |
# Churn Prediction
This notebook will introduce the use of the churn dataset to create churn prediction model using deep kernel learning.
The dataset used to ingest is from SIDKDD 2009 competition.
The pipeline is composed using Azure ML pipeline and trained on Azure ML compute with hyper parameters of the gaussian... | github_jupyter |
```
from tensorflow.keras.preprocessing.text import Tokenizer
sentences = [
'i love my dog',
'I, love my cat',
'You love my dog!'
]
tokenizer = Tokenizer(num_words = 100)
tokenizer.fit_on_texts(sentences)
word_index = tokenizer.word_index
print(word_index)
import tensorflow as tf
from tensorflow import ke... | github_jupyter |
```
!pip install autokeras
!pip install git+https://github.com/keras-team/keras-tuner.git@1.0.2rc4
```
In this tutorial we are making use of the
[AutoModel](/auto_model/#automodel-class)
API to show how to handle multi-modal data and multi-task.
## What is multi-modal?
Multi-modal data means each data instance has... | github_jupyter |
```
#hide
#skip
! [ -e /content ] && pip install -Uqq mrl-pypi # upgrade mrl on colab
# default_exp core
```
# Core
> Core functions for MRL, mostly low level plumbing and parallel processing
```
#hide
from nbdev.showdoc import *
%load_ext autoreload
%autoreload 2
# export
from mrl.imports import *
from multiproces... | github_jupyter |
## Creating a Convolutional Neural Network-Dogs-v-Cats
### Imports
```
import os
import numpy as np
import cv2
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
```
### Creating a NN
```
class Net(nn.Module):
def __init__(self):
super().__init__()
... | github_jupyter |
# Proglearn: Scene Segmentation of ISIC using Scikit-Image
*Neuro Data Design II: Spring 2022*
This tutorial provides a walkthrough to applying a Random Forest model to perform scene segmentation on images taken from the International Skin Imaging Collaboration (ISIC) dataset from 2016 using Scikit-Image.
**Contri... | github_jupyter |
<a href="https://colab.research.google.com/github/lucianogaldino/ENEM-2019-SP/blob/main/Enem_2019_SP.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# **PROJETO ENEM 2019**
## Este projeto analisa os resultados do ENEM no estado de São Paulo no ano... | github_jupyter |
```
%reload_ext autoreload
%autoreload 2
import logging
import numpy as np
# Make analysis reproducible
np.random.seed(0)
# Enable logging
logging.basicConfig(level=logging.INFO)
from replay_trajectory_classification import make_track_graph, plot_track_graph
import matplotlib.pyplot as plt
node_positions = [(40, 80... | github_jupyter |
# Plagiarism Detection Model
Now that you've created training and test data, you are ready to define and train a model. Your goal in this notebook, will be to train a binary classification model that learns to label an answer file as either plagiarized or not, based on the features you provide the model.
This task wi... | github_jupyter |
## 1. Introduction
We will reimplement the methodology of the paper in Python.
## 2. Preliminary Concepts
Initially, we will recreate the basic variables defined in the paper. To make calculations easier, we will use NaNs instead of zeros if a movie is not rated by a user.
```
import numpy as np
m = 6040 # users
n ... | github_jupyter |
# MNIST Image Classification with TensorFlow on Cloud ML Engine
This notebook demonstrates how to implement different image models on MNIST using Estimator.
Note the MODEL_TYPE; change it to try out different models
```
import os
PROJECT = 'cloud-training-demos' # REPLACE WITH YOUR PROJECT ID
BUCKET = 'cloud-traini... | github_jupyter |
# Batch Normalization
One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train.
One idea along these lines is batch normalization which was proposed... | github_jupyter |
There was some problem in building the past .ttl file. I'll redo the steps and debug.
Now that the entities are on Wikidata, while there is no has_positive_marker property there, we can make a local RDF file using Wikidata IDs.
```
import pandas as pd
gene_reference = pd.read_csv("../results/human_gene_reference_fr... | github_jupyter |
## Import our modules. Remember it is always good to do this at the begining of a notebook.
If you don't have seaborn, you can install it with `conda install seaborn`.
```
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
```
### Use the notebook magic to render matplotlib figures inline wit... | github_jupyter |
```
import os
import pandas as pd
import numpy as np
import json
import pickle
from pprint import pprint
from collections import defaultdict
from pathlib import Path
import matplotlib.pyplot as plt
import seaborn as sns
import torch
import os, sys
parentPath = os.path.abspath("..")
if parentPath not in sys.path:
... | github_jupyter |
# Load packages
```
%matplotlib inline
from ifis_tools import database_tools as db
from ifis_tools import asynch_manager as am
from ifis_tools import auxiliar as aux
from wmf import wmf
import pandas as pd
import numpy as np
import os
import pylab as pl
from string import Template
from param_ident import core
f... | github_jupyter |
## Herramientas
En este taller usamos pandas, sklearn y OpenCV, las siguientes celdas muestran algunos metodos que usaremos
[Pandas](https://pandas.pydata.org/)
Es una librería muy útil para trabajar con datos tabulares.
Es muy común encontrarla en el análisis de datos y en procesos de Machine Learning.
En este talle... | github_jupyter |
<a href="https://colab.research.google.com/github/wileyw/DeepLearningDemos/blob/master/handwriting_generator/IBM_Transformer%2BTimeEmbedding.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Notebook
Original code from here: [code](https://github.co... | github_jupyter |
<span style="color:red; font-family:Helvetica Neue, Helvetica, Arial, sans-serif; font-size:2em;">An Exception was encountered at '<a href="#papermill-error-cell">In [2]</a>'.</span>
```
# Parameters
msgs = "Ran from Airflow at 2022-03-20T18:04:11.892055+00:00!"
```
<span id="papermill-error-cell" style="color:red; f... | github_jupyter |
## Thermally driven Convection -pt 2
Analysis of the convection run, and more advanced behaviour
**New concepts:** Advection-diffusion solver template, thermal boundary conditions, Rayleigh number, analysis functions, interpolation
**NOTE:** I saved all the python setup of the previous notebook in a file so we don... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
file = pd.read_csv("mamographic.csv",sep=',', na_values=["?"])
print(file.tail())
file.isnull().values.any()
file.columns[file.isnull().any()]
file.describe()
file['BI-RADS'].fillna(file['BI-RADS'].mean(),inplace=True)
file['Age'].fillna(file['... | github_jupyter |
# UBI-FIT (flat income tax)
For each level of a UBI, calculate the required flat income tax rate, and associated measures like poverty rate (depth) and inequality.
* Disruption: average decrease to after-tax income (also per person)
* Gini per person
*Data: CPS | Tax year: 2018 | Type: Static | Author: Max Ghe... | github_jupyter |
## datasets
This module has the necessary functions to be able to download several useful datasets that we might be interested in using in our models.
```
from fastai.gen_doc.nbdoc import *
from fastai.datasets import *
from fastai.datasets import Config
from pathlib import Path
show_doc(URLs)
```
This contains all... | github_jupyter |
[Deep Learning Summer School 2019](http://2019.dl-lab.eu) in Gdansk, Poland
Ordinal Regression Tutorial by [Sebastian Raschka](https://sebastianraschka.com)
GitHub Repository: https://github.com/rasbt/DL-Gdasnk2019-tutorial
```
%load_ext watermark
%watermark -a 'Sebastian Raschka' -v -p torch
```
# Modifying the ... | github_jupyter |
```
import numpy as np
from astropy.table import Table
from scipy.sparse import lil_matrix
from sklearn.cluster import DBSCAN
from sklearn.neighbors import NearestNeighbors
import time
def jaccard(a,b):
"""
Calculate Jaccard distance between two arrays.
:param a: array
array of neighbors
:param ... | github_jupyter |
```
%load_ext watermark
%watermark -d -u -a 'Andreas Mueller, Kyle Kastner, Sebastian Raschka' -v -p numpy,scipy,matplotlib,scikit-learn
```
# SciPy 2016 Scikit-learn Tutorial
# Model Evaluation, Scoring Metrics, and Dealing with Class Imbalances
In the previous notebook, we already went into some detail on how to ... | github_jupyter |
# Creating a Bathymetric Surface from ICESAT-2 data
The spaceborne ICESAT-2 LiDAR instrument is a photo counting LiDAR which has a wavelength of 532 nm. At this wavelength the signal penetrates into waterbodies and therefore point samples of water depths can be retrived (e.g., Thomas et al., 2021) down to 40 m in dept... | github_jupyter |
```
# MATH FUNCTIONS IN PYTHON
# SOURCE - https://docs.python.org/3/library/math.html
#--------------------------------------------------------------------------------------------------
# first we need to import the math module
# This module provides access to the mathematical functions defined by the C standard.
#
# ... | github_jupyter |
```
from sklearn.preprocessing import LabelBinarizer
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential, model_from_json
from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau
from keras.constraints import maxnorm
from ... | github_jupyter |
# Cats and Dogs Problem Solution
The inspiration for this script comes from a beautiful [keras blog](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html).
```
#Imports
import os
from random import shuffle
#Keras imports
from keras.preprocessing.image import ImageDataGenera... | github_jupyter |
# PageRank
In this notebook, we will use both NetworkX and cuGraph to compute the PageRank of each vertex in our test dataset. The NetworkX and cuGraph processes will be interleaved so that each step can be compared.
Notebook Credits
* Original Authors: Bradley Rees and James Wyles
* Created: 08/13/2019
* Updated:... | github_jupyter |
# Итерационные методы для собственных значений
## PINVIT
- Идея - минимизировать отношение Релея
- Используем градиентный спуск предобусловленный матрицей $(A - \sigma I)$
```
import numpy as np
import scipy.sparse.linalg as spsplin
import scipy.sparse as spsp
def pinvit(A, x0, sigma, tau, num_iter, tol, inexact=Tr... | github_jupyter |
# Accessing and Processing the Optical Absorption and Attenuation (OPTAA) Data from OOI
OOI uses the [Sea-Bird Electronics, AC-S In-Situ Spectrophotometer](https://www.seabird.com/ac-s-spectral-absorption-and-attenuation-sensor/product?id=60762467715) to measure the in situ absorption and beam attenuation coefficients... | github_jupyter |
# Pricing assets with the risk-free metric
## Vanilla assets
1. Based on mainly observations select a microscopic process that generates the price path of the asset or its underlier.<br/>
For example, in the simplest case this microscopic process is a normalized random walk with a constant drift.
2. Generate paths wit... | github_jupyter |
```
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import deepthought, mne, os
from deepthought.util.logging_util import configure_custom
configure_custom(debug=False)
mne.set_log_level('INFO')
### TODO: change this for each subject
subject = 'P01'
from deepthought.datasets.... | github_jupyter |
```
import csv
import pandas as pd
from collections import Counter
from collections import defaultdict
from matplotlib import pyplot as plt
from sklearn.feature_extraction import DictVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.preprocessing import OneHotEncoder
from sklearn.metri... | github_jupyter |
This notebook was prepared by [Donne Martin](http://donnemartin.com). Source and license info is on [GitHub](https://github.com/donnemartin/interactive-coding-challenges).
# Challenge Notebook
## Problem: Add two numbers whose digits are stored in a linked list in reverse order.
* [Constraints](#Constraints)
* [Test... | github_jupyter |
# Keyboard BCI
The name "brain-computer interface" suggests that you're using your brain to control a computer. In this notebook, we build a BCI whose `action` is to send keystrokes to the computer.
You can probably think of a number of different applications for something like this. One example would be to use your ... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive', force_remount = True)
%tensorflow_version 2.x
!pip uninstall keras -y
!pip uninstall keras-nightly -y
!pip uninstall keras-Preprocessing -y
!pip uninstall keras-vis -y
!pip uninstall tensorflow -y
!pip install napari[all]
!pip install tensorflow==2.2.0
!p... | github_jupyter |
<h1>gcForest Algorithm</h1>
<p>The gcForest algorithm was suggested in Zhou and Feng 2017 ( https://arxiv.org/abs/1702.08835 , refer for this paper for technical details) and I provide here a python3 implementation of this algorithm.<br>
I chose to adopt the scikit-learn syntax for ease of use and hereafter I present ... | github_jupyter |
# 회귀분석
## 검증하고자 하는 것 : 맛집 프로그램별 SNS채널(네이버 블로그)에 미치는 영향력
### 분석계획
### 1. 독립변수에 방송 프로그램 외 변수들을 추가하면서 R^2가 높아지는지 확인 & R^2가 가장 높은 회귀식 도출
### 2. 방송 프로그램별 회귀식을 만들어 포스팅 증가에 가장 영향을 미치는 요인 찾아보기
### 3. 2017년 데이터(train set)로 회귀식을 만든 후, 2018년 데이터(test set)로 예측해보고 정확도 확인
```
import pandas as pd
import numpy as np
import statsmo... | github_jupyter |
# Swedes without any close friends
This notebook explores and visualizes the proportion of Swedes stating they have no close friends.
- Date: 2019-04-04
- Source: [SCB: Undersökningarna av levnadsförhållanden](http://www.statistikdatabasen.scb.se/pxweb/sv/ssd/START__LE__LE0101__LE0101R/LE0101R07/?rxid=710c09ba-1e21-4... | github_jupyter |
#### Copyright 2017 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 writin... | github_jupyter |
# Sampler statistics
When checking for convergence or when debugging a badly behaving
sampler, it is often helpful to take a closer look at what the
sampler is doing. For this purpose some samplers export
statistics for each generated sample.
```
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sb... | github_jupyter |
```
# Load Packages
import pandas as pd
import numpy as np
import random
import sklearn
from sklearn.model_selection import LeaveOneOut
from sklearn import preprocessing
from matplotlib import pyplot as plt
%matplotlib inline
# load window methylation data
A = pd.read_csv("Window_Meth.csv")
# load window methylation da... | github_jupyter |
# t-test
___
There may be situations where the standard deviation of the population is unknown, and the sample size is small. In all such cases, we use the T-distribution. This distribution is also called *Student’s T distribution*.
The following are the chief characteristics of the T-distribution:
+ The T-distribut... | github_jupyter |
```
import sys
# Add the path to system, local or mounted S3 bucket, e.g. /dbfs/mnt/<path_to_bucket>
sys.path.append('./secrets.py')
import logging
import math
import os
from influxdb import DataFrameClient
import numpy as np
import matplotlib.mlab as mlab
import pandas as pd
import matplotlib.pyplot as plt
from tabu... | github_jupyter |
# Math - Algebra
[](https://colab.research.google.com/github/rhennig/EMA6938/blob/main/Notebooks/4.Math_Algebra.ipynb)
(Based on https://online.stat.psu.edu/stat462/node/132/ and https://www.geeksforgeeks.org/ml-normal-equation-in-linear-regres... | github_jupyter |
# An intro to Python & Jupyter notebooks
This is a jupyter notebook! It is actually running in your browser and translating it into Python! Super neat. It allows us to write text AND code in the same place. For example, this is a markdown cell where I can write myself notes.
First we'll take a tour of jupyter not... | github_jupyter |
# N-grams
## Overview
An *n-gram* -- in the context of parsing natural languages such as English -- is a sequence of *n* consecutive *tokens* (which we might define as characters separated by whitespace) from some passage of text. Based on the following passage:
> I really really like cake.
We have the following 2-... | github_jupyter |
# Wavelets and sweeps
This notebook looks at the convolutional model of a seismic trace — first with an impulse-type wavelet, such as a Ricker — then with a simulated Vibroseis sweep.
First, the usual preliminaries.
```
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
```
## Load geophysical d... | github_jupyter |
```
import syft as sy
```
# Part 1: Launch a Duet Server
```
duet = sy.launch_duet(loopback=True)
```
# Part 2: Upload data to Duet Server
```
import torch as th
# Data owner has age data of 6 people
age_data = th.tensor([25, 32, 49, 65, 88, 22])
# Data owner names the data with tag "ages"
age_data = age_data.tag... | github_jupyter |
```
from abc import ABCMeta, abstractmethod, abstractproperty
import enum
import numpy as np
np.set_printoptions(precision=3)
np.set_printoptions(suppress=True)
import pandas
from matplotlib import pyplot as plt
%matplotlib inline
```
## Bernoulli Bandit
We are going to implement several exploration strategies for... | github_jupyter |
```
from pathlib import Path
import numpy as np
import pandas as pd
from gensim.models import Doc2Vec
from gensim.models.doc2vec import TaggedDocument
import logging
import warnings
from random import shuffle
import lightgbm as lgb
from sklearn.model_selection import train_test_split
from sklearn.linear_model import Lo... | github_jupyter |
## 02. Multiple Parameters
In this tutorial, you will learn how to:
* Optimize the Objective Function with Multiple HyperParameters
* Define different types of Search Space
在本教程中，您将学习如何：
* 优化多超参数的目标函数
* 定义不同类型的搜索空间
### Optimizing Multi Parameters Objective function
```
# import fmin interface from UltraOpt
from u... | github_jupyter |
# Comparative analysis
## Imports & Parameters
```
import os, sys
import json
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import rasterio
from tqdm import tqdm_notebook as tqdm
from sklearn.model_selection import train_test_split
from itertools import product
from functools import partial
f... | github_jupyter |
```
#Importing Environment and ImpStates
from env_2_stochastic_high import Environment2,StartandGoal,ImportDynamics
from SophAgent import SophAgentActions
from QlearningAgent import QAgent
[startstate,goalstate]=StartandGoal()
#Btrue is only used for plotting-model Accuracy
Btrue=ImportDynamics()
import numpy as np
imp... | github_jupyter |
##### Copyright 2019 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License");
```
#@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.o... | github_jupyter |
# Desenhos de Curvas a partir da Curvatura
**Autor:** Leonardo Dantas
Este trabalho explora o uso da computação simbólica e numérica no estudo da curvatura. Como decorrência do Teorema Fundamental da Teoria Local das Curvas Planas, curvas planas podem ser desenhadas puramente a partir de suas funções de curvatura, des... | github_jupyter |
```
# 챗봇, 번역, 이미지 캡셔닝등에 사용되는 시퀀스 학습/생성 모델인 Seq2Seq 을 구현해봅니다.
# 영어 단어를 한국어 단어로 번역하는 프로그램을 만들어봅니다.
import tensorflow as tf
import numpy as np
# S: 디코딩 입력의 시작을 나타내는 심볼
# E: 디코딩 출력을 끝을 나타내는 심볼
# P: 현재 배치 데이터의 time step 크기보다 작은 경우 빈 시퀀스를 채우는 심볼
# 예) 현재 배치 데이터의 최대 크기가 4 인 경우
# word -> ['w', 'o', 'r', 'd']
# to... | github_jupyter |
# Building Fast Queries on a CSV
Skills: Object Oriented Programming, Time and Space Complexity Analysis
We will imagine that we own an online laptop store and want to build a way to answer a few different business questions about our inventory.
```
# Open and explore the dataset
import csv
with open('laptops.csv') ... | github_jupyter |
```
from IPython.core.display import display, HTML, Markdown, clear_output, Javascript
from string import Template
import pandas as pd
import json, random
import yaml
import copy
import networkx as nx
import math
import xml.etree.ElementTree as ET
import ipywidgets as widgets
import os
import time
import os.path
from o... | github_jupyter |
# Sample Hangul RNN
```
# -*- coding: utf-8 -*-
# Import Packages
import numpy as np
import tensorflow as tf
import collections
import string
import argparse
import time
import os
from six.moves import cPickle
from TextLoader import *
from Hangulpy import *
print ("Packages Imported")
```
# Load dataset using TextLoa... | github_jupyter |
# Tutorial 2 for Python
## Make a scenario of Dantzig's Transport Problem using the *ix modeling platform* (ixmp)
<img style="float: right; height: 80px;" src="_static/python.png">
### Aim and scope of the tutorial
This tutorial uses teh transport problem scenario developed in the first tutorial and illustrates how... | github_jupyter |
```
%%writefile morse.py
# A lookup dictionary which, given a letter will return the morse code equivalent
_letter_to_morse = {'a':'.-', 'b':'-...', 'c':'-.-.', 'd':'-..', 'e':'.', 'f':'..-.',
'g':'--.', 'h':'....', 'i':'..', 'j':'.---', 'k':'-.-', 'l':'.-..', 'm':'--',
'n':'-.'... | github_jupyter |
# Data Augmentation
We'll show you examples of data augmentation with various techniques such as [MixUp](https://openreview.net/pdf?id=r1Ddp1-Rb), [CutMix](http://openaccess.thecvf.com/content_ICCV_2019/papers/Yun_CutMix_Regularization_Strategy_to_Train_Strong_Classifiers_With_Localizable_Features_ICCV_2019_paper.pdf),... | github_jupyter |
```
import numpy as np
def CSR_to_DNS(data, col, rowptr, shape):
A = np.zeros(shape)
counter = 0
row = 0
for i in range(len(data)):
while counter >= rowptr[row+1]:
row += 1
A[row][col[i]] = data[i]
counter += 1
return A
def DNS_to_CSR(A):
data = []
col = [... | github_jupyter |
```
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
```
# Gibbs sampling for a one sample t-test
Chapter 3.2.1: Gibbs sampling
Assume $Y_i \mid \mu,\sigma^2\sim\mbox{Normal}(\mu,\sigma^2)$ for $i=1,\dots,n$ and let the prior distributions be $\mu\sim\mbox{Normal}\left(0,\frac{\sigma^2}{m}... | github_jupyter |
# ***Video da apresentação:***
---
# https://youtu.be/-5xjHpiqnL0 **bold text**
```
from google.colab import drive
drive.mount('/gdrive')
%cd /gdrive
!pip install icc_rt
#!pip uninstall icc_rt
import pandas as pd
import numpy as np
import gensim
import multiprocessing
import sklearn.preprocessing as pp
import warn... | github_jupyter |
# Classifying Bangla Fake News with HuggingFace Transformers and Fastai
- toc: true
- branch: master
- badges: true
- comments: true
- categories: [fastpages, jupyter]
- image: images/some_folder/your_image.png
- hide: false
- search_exclude: true
- metadata_key1: metadata_value1
- metadata_key2: metadata_value2
![]... | github_jupyter |
# 线性回归的简洁实现
随着深度学习框架的发展，开发深度学习应用变得越来越便利。实践中，我们通常可以用比上一节更简洁的代码来实现同样的模型。在本节中，我们将介绍如何使用tensorflow2.0推荐的keras接口更方便地实现线性回归的训练。
## 生成数据集
我们生成与上一节中相同的数据集。其中`features`是训练数据特征，`labels`是标签。
```
import tensorflow as tf
num_inputs = 2
num_examples = 1000
true_w = [2, -3.4]
true_b = 4.2
features = tf.random.normal(shape=(num_... | github_jupyter |
# Getting Started With VerifyML
A quickstart guide to documenting your model findings in a VerifyML Model Card.
## Installation
```
!pip install verifyml
!pip install seaborn
```
## Imports
```
import pandas as pd
import verifyml.model_card_toolkit as mctlib
import verifyml.model_tests.utils as utils
import seabor... | github_jupyter |
# ARAS Datasets
H. Alemdar, H. Ertan, O.D. Incel, C. Ersoy, ARAS Human Activity Datasets in Multiple Homes with Multiple Residents, Pervasive Health, Venice, May 2013.
```
import sys
sys.path.append("../..")
import pandas as pd
import matplotlib.pyplot as plt
import pyadlml
import requests
import plotly
plotly.offline... | github_jupyter |
## Computer Vision Learner
[`vision.learner`](/vision.learner.html#vision.learner) is the module that defines the [`cnn_learner`](/vision.learner.html#cnn_learner) method, to easily get a model suitable for transfer learning.
```
from fastai.gen_doc.nbdoc import *
from fastai.vision import *
```
## Transfer learning... | github_jupyter |
##### Copyright 2019 Qiyang Hu
```
#@title Licensed under MIT License (the "License");
# You may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://huqy.github.io/idre_learning_machine_learning/LICENSE.md
#
# Unless required by applicable law or agreed to in ... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib notebook
```
# Conditional Entropy: Can Information Theory Beat the L-S Periodogram?
**Version 0.1**
***
By AA Miller 5 June 2019
In this lecture we will examine alternative methods to search for periodic signals in astronomical ... | github_jupyter |
<a href="https://colab.research.google.com/github/ikonushok/My_projects/blob/main/%D0%A0%D0%B0%D0%B7%D0%B1%D0%BE%D1%80_HW4_UltraPro_%D0%A3%D0%B3%D0%BB%D1%83%D0%B1%D0%BB%D0%B5%D0%BD%D0%B8%D0%B5_%D0%B2_RNN.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a... | github_jupyter |
# Ensembling Feature Overview
Ensembling is a fancy name for sub-sampling the data and generating $n_\text{models}$ from regressing onto each of these sub-samples. In practice this helps to robustify the regressions against outliers and other issues. We highly recommend checking out the following paper for understandin... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
from awave.experimental.filters import gabor_filter, edge_filter, curve_filter
from awave.experimental.filters_agg import *
import awave.experimental.viz as viz
from tqdm import tqdm
```
# look at base filters
```
filter_size = ... | github_jupyter |
# Classification on Iris dataset with sklearn and DJL
In this notebook, you will try to use a pre-trained sklearn model to run on DJL for a general classification task. The model was trained with [Iris flower dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set).
## Background
### Iris Dataset
The dataset c... | github_jupyter |
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