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# RidgeClassifier with StandardScaler & Polynomial Features
This Code template is for the Classification tasks using RidgeClassifier, feature rescaling using StandardScaler and feature transformation using Polynomial features in a pipeline.
### Required Packages
```
!pip install imblearn
import warnings
import num... | github_jupyter |
# Neural Machine Translation
Let's load all the packages we will need for this model.
```
from keras.layers import Bidirectional, Concatenate, Permute, Dot, Input, LSTM, Multiply
from keras.layers import RepeatVector, Dense, Activation, Lambda
from keras.optimizers import Adam
from keras.utils import to_categorical
f... | github_jupyter |
```
# MIT License
#
# Copyright (c) 2019 Mohamed-Achref MAIZA
#
# 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, modi... | github_jupyter |
<a href="https://cognitiveclass.ai"><img src = "https://ibm.box.com/shared/static/9gegpsmnsoo25ikkbl4qzlvlyjbgxs5x.png" width = 400> </a>
<h1 align=center><font size = 5>Pie Charts, Box Plots, Scatter Plots, and Bubble Plots</font></h1>
## Introduction
In this lab session, we continue exploring the Matplotlib librar... | github_jupyter |
# Activity 7: Optimizing a deep learning model
In this activity we optimize our deep learning model. We aim to achieve greater performance than our model `bitcoin_lstm_v0`, which is off at about 6.8% from the real Bitcoin prices. We explore the following topics in this notebook:
* Experimenting with different layers a... | github_jupyter |
JibanCat: If you are not sure about the regular expression is correct or not, you can use this https://regexr.com website to see the real-time response.
Classical Chinese DH: Regular expressions
=====
*By [Donald Sturgeon](https://dsturgeon.net/about)*
\[[View this notebook online](https://digitalsinology.org/classi... | 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 |
TSG060 - Persistent Volume disk space for all BDC PVCs
======================================================
Description
-----------
Connect to each container and get the disk space used/available for each
Persisted Volume (PV) mapped to each Persisted Volume Claim (PVC) of a
Big Data Cluster (BDC)
Steps
-----
###... | github_jupyter |
```
import pandas as pd
import numpy as np
```
### Step 1: Importing Data
```
df= pd.read_csv('train.csv')
df.head()
```
### Step 2: Cleaning Data
```
df.info()
df.isnull().values.any()
df.isnull().sum()
df[df['V4'].isnull()]
df.dropna(inplace=True)
df.isnull().values.any()
```
### Step 3: Data Preprocessing
```
... | github_jupyter |
## Triage Demonstration Using Elasticsearch / Kibana
Some helper functions below needed to insert office document prediction results into a local Elasticsearch instance. This was tested with ES / Kibana 5.1.2
```
import mmbot as mmb
from elasticsearch import Elasticsearch
import time
import requests
import json
def... | github_jupyter |
# Using Tensorflow DALI plugin: DALI and tf.data
### Overview
DALI offers integration with [tf.data API](https://www.tensorflow.org/guide/data). Using this approach you can easily connect DALI pipeline with various TensorFlow APIs and use it as a data source for your model. This tutorial shows how to do it using well... | github_jupyter |
# Predictive maintenance
## Part 1: Data Preparation
The original data can be [downloaded from this link.](https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/#turbofan) Since the content in the train and test datasets is different, we are making it uniform before we start the data exploration an... | github_jupyter |
```
import os
import glob
import math
import numpy as np
import pandas as pd
import matplotlib as mpl
import xml.etree.ElementTree as ET
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
%matplotlib inline
data_path = os.path.join('data', 'motion_data', 'files_motions_589')
all_files = glob.glob(... | github_jupyter |
# Arbres
On utilisera la structure suivante.
```
class Arbre():
def __init__(self, x, enfants = None): # constructeur
if enfants is None:
enfants = []
self.valeur = x
self.enfants = enfants
def __repr__(self): # affichage
s = str(self.valeur) + str(sel... | github_jupyter |
### The purpose of this notebook is to complete a data cleaning workflow from start to finish in order to validate the core functionality our package
#### TO DO:
- Add in complete PubChem data
- Write PubChem function
- Organize code modules & tests
- Clean up/finish writing tests
- Write main script wrapper function
... | github_jupyter |
```
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import json
import seaborn as sb
plt.rcParams['figure.figsize'] = 8, 4
df = pd.read_json(open('data/nobel_winners_cleaned.json'))
df.info()
# convert the date columns to a usable form
df.date_of_birth = pd.to_datetime(df.date... | github_jupyter |
```
from pathlib import Path
import matplotlib.pyplot as plt
import pandas as pd
import imageio
%matplotlib inline
```
## Download
youtube-dl --rm-cache-dir
youtube-dl -f bestvideo https://youtu.be/Fkadv0VnZkI
youtube-dl -f bestvideo https://www.youtube.com/playlist?list=PLAPUEAObdbMb747QUFsjQ2e9MPz1FkDnQ
youtube-... | github_jupyter |
# COVID19 Exposure Notification System Risk Simulator
kpmurphy@google.com, serghiou@google.com
(broken link)
Last update: 22 August 2020
## References
We base our approach on these papers
* [Quantifying SARS-CoV-2-infection risk withing the Apple/Google exposure notification framework to inform quarantine reco... | github_jupyter |
```
import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
# !pip install --upgrade pip
# !pip install -U --ignore-installed wrapt enum34 simplejson netaddr imageio setuptools
# !pip install matplotlib==2.2.3
# !pip install pyyaml h5py
# !pip install --upgrade s... | github_jupyter |
<img src="http://hilpisch.com/tpq_logo.png" alt="The Python Quants" width="35%" align="right" border="0"><br>
# Derivatives Analytics with Python
**_Chapters 2 & 3_**
**Wiley Finance (2015)**
<img src="http://hilpisch.com/images/derivatives_analytics_front.jpg" alt="Derivatives Analytics with Python" width="30%" al... | github_jupyter |
# Advanced topic: Ice albedo feedback in the EBM
This notebook is part of [The Climate Laboratory](https://brian-rose.github.io/ClimateLaboratoryBook) by [Brian E. J. Rose](http://www.atmos.albany.edu/facstaff/brose/index.html), University at Albany.
*These notes and the companion [Advanced topic: Snowball Earth and ... | github_jupyter |
# Greatness Rating for NBA players
### Observations:
1. Great players tend to lead the league annually in 'primary' categories.
2. Great players tend to lead the league annually in 'secondary' categories. However, these categories are subordinate to 'primary' categories for the reasons below, and should not carry the... | github_jupyter |
# Housing Data Visual Analysis
Although we previously got a pretty good R squared quality metric for predicting house values using a linear regression model (see here https://www.ibm.com/developerworks/community/blogs/JohnBoyer/entry/Measuring_the_Quality_of_a_TensorFlow_Regression_Model), there still may be a lot of ... | github_jupyter |
```
# Binary Classification Example
import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import S... | github_jupyter |
... ***CURRENTLY UNDER DEVELOPMENT*** ...
## RBFs reconstruction of historical and synthetic data
inputs required:
* Synthetic offshore waves - emulator output
* Sea and swell **SWAN simulated cases**
in this notebook:
* RBF reconstruction simulated storms
* Generation of hourly nearshore waves with Intrad... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Visualization/styled_layer_descriptors.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 |
## Setup Notebook and Libraries
```
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
plt.rcParams['figure.figsize'] = (8,6)
plt.rcParams['font.size'] = 14
plt.style.use("fivethirtyeight")
%matplot... | github_jupyter |
# PhysioNet/Computing in Cardiology Challenge 2020
## Classification of 12-lead ECGs
### Synthetic Noise Generation
# Setup Notebook
```
# Import 3rd party libraries
import os
import sys
import json
import numpy as np
import pandas as pd
import matplotlib.pylab as plt
from ipywidgets import interact, fixed
# Import ... | github_jupyter |
```
# import sys
# !{sys.executable} -m pip install --upgrade c-lasso
from classo import random_data, classo_problem
import numpy as np
import matplotlib.pyplot as plt
# this is the path of the directory where one want to save its figures
path = '../../figures/'
```
## Basic example
The c-lasso package includes
the... | github_jupyter |
```
# 1. Create shapes in external drawing apps.
# 2. Write a code to identify your shapes.
import cv2 as cv
import matplotlib.pyplot as plt
import numpy as np
# Load the image and convert it to grayscale:
img = cv.imread("assets/shapes2.png")
imgGray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
plt.figure(figsize=(20,15))... | github_jupyter |
```
import cv2 as cv
import matplotlib.pyplot as plt
net=cv.dnn.readNetFromTensorflow("graph_opt.pb")
BODY_PARTS = { "Nose": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4,
"LShoulder": 5, "LElbow": 6, "LWrist": 7, "RHip": 8, "RKnee": 9,
"RAnkle": 10, "LHip": 11, "LKnee": 12, "LAnk... | github_jupyter |
<img alt="QuantRocket logo" src="https://www.quantrocket.com/assets/img/notebook-header-logo.png">
<a href="https://www.quantrocket.com/disclaimer/">Disclaimer</a>
# Dimensionality Reduction with PCA
Given the poor result of our first walk-forward optimization, we want to explore whether reducing the number of featu... | github_jupyter |
## Coronary Heart Disease Prediction
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
# Sklearn
from sklearn.preprocessing import normalize
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection imp... | github_jupyter |
```
import torch
from torchvision import datasets,transforms
from torch import nn,optim
import torch.nn.functional as F
transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))])
dataset=datasets.FashionMNIST('./fashion_mnist',download=True,train=True,transform=transform)
tr... | github_jupyter |
```
import mlflow
import matplotlib.pyplot as plt
import matplotlib
from matplotlib.ticker import NullFormatter, FormatStrFormatter, ScalarFormatter
import tikzplotlib
import pandas as pd
from matplotlib import rc
rc('font',**{'family':'libertine'})
rc('text', usetex=True)
import seaborn as sns
import sys
sys.path.... | github_jupyter |
# Semantic Similarity with BERT
**Author:** [Mohamad Merchant](https://twitter.com/mohmadmerchant1)<br>
**Date created:** 2020/08/15<br>
**Last modified:** 2020/08/29<br>
**Description:** Natural Language Inference by fine-tuning BERT model on SNLI Corpus.
## Introduction
Semantic Similarity is the task of determini... | github_jupyter |
```
import itertools
import xml.etree.cElementTree as et
import networkx as nx
import pandas as pd
import numpy as np
def trackmate_peak_import(trackmate_xml_path, get_tracks=False):
"""Import detected peaks with TrackMate Fiji plugin.
Parameters
----------
trackmate_xml_path : str
TrackMate... | github_jupyter |
# Generate JayChou Text
# 1. load dataset
```
with open('jaychou_lyrics.txt' ,'r', encoding='utf-8') as fr:
corpus_chars = fr.read()
corpus_chars[:40]
```
# 2. pre-process
```
corpus_chars = corpus_chars.replace('\n', ' ').replace('\r', ' ')
corpus_chars = corpus_chars[:10000]#只取前10000个词
len(corpus_chars)
```
... | github_jupyter |
# Neural networks with PyTorch
Next I'll show you how to build a neural network with PyTorch.
```
# Import things like usual
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import torch
import helper
import matplotlib.pyplot as plt
from torchvision import datasets, transforms
... | github_jupyter |
# User's Guide, Chapter 29: Spanners 1 (Slurs)
In `music21`, a ":class:`~music21.spanner.Spanner`" is a :class:`~music21.base.Music21Object` that denotes a relationship among other elements, such as Notes, Chords, or even Streams, which may or may not be separated in a hierarchy, such as `Note` objects in different me... | github_jupyter |
##### Copyright 2019 The TensorFlow Hub Authors.
Licensed under the Apache License, Version 2.0 (the "License");
Created by @[Adrish Dey](https://github.com/captain-pool) for [Google Summer of Code](https://summerofcode.withgoogle.com/) 2019
```
# Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.
#
# Li... | github_jupyter |
##### Copyright 2020 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 |
[](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/jupyter/transformers/HuggingFace%20in%20Spark%20NLP%20-%20XlmRoBertaForTokenClassification.ipynb)
## Import XlmRoBertaForTokenClassification models from Hugg... | github_jupyter |
<img src="../../images/qiskit-heading.gif" alt="Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook" width="500 px" align="left">
Welcome Qiskitters to Quantum Information Science with Qiskit Terra! Here we have a collection of great tutorials from our fantastic Qi... | github_jupyter |
# Example Gate Characterization
```
import os
from qcodes import Station, load_or_create_experiment
from qcodes.dataset.plotting import plot_dataset
from qcodes.dataset.data_set import load_by_run_spec
import nanotune as nt
from nanotune.tuningstages.gatecharacterization1d import GateCharacterization1D
from nanotun... | github_jupyter |
```
import sys
import os
import numpy as np
BIN = '../'
sys.path.append(BIN)
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
import pickle
import my_matplotlib_style as ms
from scipy import stats
import utils
import torch
import torch.nn as nn
import torch.utils.data
from torch.utils.data... | github_jupyter |
# `mle-logging`: A Lightweight Logger for ML Experiments
### Author: [@RobertTLange](https://twitter.com/RobertTLange) [Last Update: August 2021] [](https://colab.research.google.com/github/RobertTLange/mle-logging/blob/main/examples/getting_started.ipyn... | github_jupyter |
<br>
<center>
<font size='7' style="color:#0D47A1"> <b>CHEMICAL GNNs</b> </font>
</center>
<hr style= "height:3px;">
<br>
<hr style= "height:1px;">
<font size='6' style="color:#000000"> <b>Content</b> </font>
<a name="content"></a>
<br>
<br>
1. [Abstract](#abstract)
<br>
2. [Setup](#setup)
<br>
3. [Loading Da... | github_jupyter |
# Naive Bayes Simple Male or Female
author: Nicholas Farn [<a href="sendto:nicholasfarn@gmail.com">nicholasfarn@gmail.com</a>]
This example shows how to create a simple Gaussian Naive Bayes Classifier using pomegranate. In this example we will be given a set of data measuring a person's height (feet) and try to class... | github_jupyter |
```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
np.random.seed(1789)
from IPython.core.display import HTML
def css_styling():
styles = open("styles/custom.css", "r").read()
return HTML(styles)
css_styling()
```
# An Introduction to Bayesian Statistical Analysis
... | github_jupyter |
# Introduction
Data science is cool! It's not only because the technology it implements is fancy,but also because it stretches our vision to broader scope. In addition to explore new and advanced data science tools and algorithms, we expand our access to new data types as well,including image data, audio data and video... | github_jupyter |
# Preface
The locations requiring configuration for your experiment are commented in capital text.
# Setup
**Installations**
```
!pip install apricot-select
!pip install sphinxcontrib-napoleon
!pip install sphinxcontrib-bibtex
!git clone https://github.com/decile-team/distil.git
!git clone https://github.com/circu... | github_jupyter |
<h1 style="color:red">IF YOU ARE DOING THE EXERCISE DO NOT READ THIS! TRY TO FIND A GOOD SOLUTION WITHOUT KNOWING THE PROBLEM PERFECTLY.</h1>
After you have run out of ideas, you can see how I created the data and then adapt your Kalman-Filter to fit this perfectly.
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... | github_jupyter |
# Understand the Normal Curve
## Mini-Lab: Characteristics of the Normal Curve
Welcome to your next mini-lab! Go ahead an run the following cell to get started. You can do that by clicking on the cell and then clickcing `Run` on the top bar. You can also just press `Shift` + `Enter` to run the cell.
```
from datascie... | github_jupyter |
# Automated Machine Learning
#### Forecasting away from training data
## Contents
1. [Introduction](#Introduction)
2. [Setup](#Setup)
3. [Data](#Data)
4. [Prepare remote compute and data.](#prepare_remote)
4. [Create the configuration and train a forecaster](#train)
5. [Forecasting from the trained model](#forecasti... | github_jupyter |
# 独立成分分析 Lab
在此 notebook 中，我们将使用独立成分分析方法从三个观察结果中提取信号，每个观察结果都包含不同的原始混音信号。这个问题与 ICA 视频中解释的问题一样。
## 数据集
首先看看手头的数据集。我们有三个 WAVE 文件，正如我们之前提到的，每个文件都是混音形式。如果你之前没有在 python 中处理过音频文件，没关系，它们实际上就是浮点数列表。
首先加载第一个音频文件 **[ICA mix 1.wav](ICA mix 1.wav)** [点击即可聆听该文件]：
```
import numpy as np
import wave
# Read the wave file
mix_1_wa... | 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 |
# Simulations for multi-resolution deblending
In this notebook I test multi-resolution on simulated images using the galsim package.
```
import scarlet
import galsim
from astropy import wcs as WCS
import time
from mr_tools import galsim_compare_tools as gct
from mr_tools.simulations import Simulation, load_surveys, c... | github_jupyter |
```
import numpy as np
import tensorflow as tf
from tensorflow import keras
def split_sequence(sequence, n_steps):
X, y = list(), list()
for i in range(len(sequence)):
end_ix = i + n_steps
if end_ix > len(sequence) - 1:
break
seq_x, seq_y = sequence[i:end_ix], sequence[end_ix... | github_jupyter |
BiLSTM+CRF模型训练，使用100维随机初始化词嵌入
```
# 显卡查看
! nvidia-smi
# 依赖安装
! pip install fastNLP
```
加载数据集
```
import sys
from fastNLP.core import Const
from fastNLP.io import PeopleDailyNERLoader
from fastNLP.io import PeopleDailyPipe
sys.path.insert(0, '/content/drive/My Drive/my_framework/qyt_clue/') # 定义搜索路径的优先顺序，序号从0开始，表示... | github_jupyter |
## Using Chicago Open Data Portal
download data Car Crahses
https://data.cityofchicago.org/Public-Safety/Crimes-2018/3i3m-jwuy
* export button, save as a csv file.
## Objective where the worst place to park in Chicago.
+ to learning basic sci kit learn preprocessing
+ learn k means clustering
+ to install run from c... | github_jupyter |
```
import dask
import dask.dataframe as dd
import warnings
import datetime
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import category_encoders as ce
import lightgbm as lgb
from sklearn.model_selection import train_test_split, StratifiedKFold, GridSearchCV
from sklearn.... | github_jupyter |
# Step 2 - Transcript Quant Into Gene Quant
## Introduction
## Things I do below
1. I used tximport to aggregate transcript-level quantification into gene-level quantification
2. I aggregated all gene-level quantification from all 16 samples into a single large table
## Use R and Python in the same notebook
To ac... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.backends.backend_pdf as pdf
import matplotlib.patches as pch
import eleanor_constants as EL
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
%matplotlib inline
savename = "./... | github_jupyter |
# ML 101
Unsupervised learning is where you only have input data $(X)$ and no corresponding output variables.
The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data.
These are called unsupervised learning because unlike supervised lea... | 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 |
# Categorical Naive Bayes Classifier with MinMaxScaler and Quantile Transformer
## Required Packages
```
!pip install imblearn
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as se
from imblearn.over_sampling import RandomOverSampler
from sklearn.naive_bayes ... | github_jupyter |
# RMSProp
:label:`sec_rmsprop`
One of the key issues in :numref:`sec_adagrad` is that the learning rate decreases at a predefined schedule of effectively $\mathcal{O}(t^{-\frac{1}{2}})$. While this is generally appropriate for convex problems, it might not be ideal for nonconvex ones, such as those encountered in dee... | github_jupyter |
# REINFORCE
---
In this notebook, we will train REINFORCE with OpenAI Gym's Cartpole environment.
### 1. Import the Necessary Packages
```
import gym
import gym.spaces
gym.logger.set_level(40) # suppress warnings (please remove if gives error)
import numpy as np
from collections import deque
import matplotlib.pyplo... | github_jupyter |
```
import matplotlib.pyplot as plt
%matplotlib inline
from bayes_implicit_solvent.constants import beta
def unreduce(value):
"""Input value is in units of kB T, turn it into units of kilocalorie_per_mole"""
return value / (beta * unit.kilocalorie_per_mole)
from bayes_implicit_solvent.continuous_parameter_expe... | github_jupyter |
# Cement Strength Neural Network
## Goal
- Develop a neural network that can predict cement strength based on various features.
## Approach
- We will primarily be using Python and the deep learning library Keras to develop such solution.
## Performance Evaluation
- For the evaluation of our model, we will be using... | github_jupyter |
# Load Constrained Layout Optimization
[Try this yourself](https://colab.research.google.com/github/DTUWindEnergy/TopFarm2/blob/master/docs/notebooks/layout_and_loads.ipynb) (requires google account)
## Install TopFarm and PyWake
```
%%capture
try:
import py_wake
except:
!pip install git+https://gitlab.winde... | github_jupyter |
# Exploratory data analysis in Python with pandas and seaborn
In this workshop we are going to explore a dataset using two powerful Python libraries: [pandas](http://pandas.pydata.org/) and [seaborn](http://seaborn.pydata.org/). We will see that pandas provides us with flexible data structures and powerful methods to m... | github_jupyter |
## Impulse Control Algorithm for HFT Market Making
For my homie Pontus <3
### Algorithmic design
The algorithm seeks to optimize the bid ask spread + hedging decisions for each time $t<T$ such that the net Profit and Loss of trading the spread is maximized at the end of the trading session time $T$. We also want to c... | github_jupyter |
# Imports
```
import warnings
warnings.filterwarnings(action='ignore')
import tensorflow as tf
from tensorflow import keras
import sklearn
from sklearn.metrics import roc_curve, auc, log_loss, precision_score, f1_score, recall_score, confusion_matrix
from sklearn.model_selection import KFold, StratifiedKFold
import ... | github_jupyter |
# Image segmentation with a U-Net-like architecture
**Author:** [fchollet](https://twitter.com/fchollet)<br>
**Date created:** 2019/03/20<br>
**Last modified:** 2020/04/20<br>
**Description:** Image segmentation model trained from scratch on the Oxford Pets dataset.
## Download the data
```
!curl -O http://www.robot... | github_jupyter |
```
model_folder='/home/mara/multitask_adversarial/results/F_CORRELATION/'
## Loading OS libraries to configure server preferences
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import warnings
warnings.filterwarnings("ignore")
import setproctitle
SERVER_NAME = 'ultrafast'
EXPERIMENT_TYPE='test_guidedCNN'
import ti... | github_jupyter |
```
import pandas as pd
import numpy as np
import os
import re
root_path = os.getcwd()
data_path = os.path.join(root_path, 'IMDb 2017')
os.listdir(data_path)
F1_path = os.path.join(data_path, 'Combinded_raw_file_2017.csv')
F2_path = os.path.join(data_path, 'movie_list_2017.csv')
```
# F1
```
df1 = pd.read_csv(F1_path... | github_jupyter |
Taking the datframes and putting .png in place of .tif
```
import os
import pandas as pd
import numpy as np
import cv2
import matplotlib.pyplot as plt
directory = '/content/drive/MyDrive/CovCT/images'
filename = '137covid_patient15_SR_2_IM00016.tif'
image = cv2.imread(os.path.join(directory, filename),-1)
img_scaled =... | github_jupyter |
##### Copyright 2018 The TF-Agents 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 a... | github_jupyter |
<a href="https://colab.research.google.com/github/BrunaMedeiroos/PYTHON-MYSQL-POWER-BI/blob/main/Analise_Completa.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Análise de Dados
Empresa de telefonia e tem clientes de vários serviços diferentes... | github_jupyter |
# The following notebook shows the implementation of LinearSVC a part of scikit learns SVM package on the csv file that we created on the Resume Text
## Background Details
### Working with resume data stored in .csv file job_desc
<ul>
<li>reading the given data from csv file</li>
<li>lemmatization and transfo... | github_jupyter |
## Dataset Tutorial
Let's first load several packages from DeepPurpose
```
# if you are using source version, uncomment the next two lines:
#import os
#os.chdir('../')
from DeepPurpose import utils, DTI, dataset
```
There are mainly three types of input data for DeepPurpose.
1. Target Sequence and its name to be re... | github_jupyter |
# Logic
```
## Based on logic code of Book: *Artificial Intelligence: A Modern Approach*
# We also do an example on course selection
```
Chapter 6 Logical Agents, Chapter 7 First-Order Logic and Chapter 8 Inference in First-Order Logic of the book *Artificial Intelligence: A Modern Approach*. We make use of the imple... | github_jupyter |
# Preprocessing of density data for Python Colormap Tutorial
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import fiona
from shapely.geometry import Polygon, MultiPolygon, shape
from descartes.patch import PolygonPatch
from scipy.stats import gaussian_kde
from scipy.ndimage.filters import ... | github_jupyter |
<small><small><i>
All of these python notebooks are available at [ https://github.com/milaan9/Python4DataScience ]
</i></small></small>
## Scientific Python
Scientific python refers to a large collection of libraries that can be used with python for a range of numerical and scientific computing tasks. Most of these a... | github_jupyter |
# Convolutional Neural Networks
## Foundations of Convolutional Neural Networks
### Computer Vision
Computer vision is one of the applications that are rapidly active thanks to deep learning. Some of the applications of computer vision that are using deep learning includes self driving cars and face recognition.
Ra... | github_jupyter |
# Table of Contents
<p><div class="lev1 toc-item"><a href="#An-example-of-a-small-Single-Player-simulation" data-toc-modified-id="An-example-of-a-small-Single-Player-simulation-1"><span class="toc-item-num">1 </span>An example of a small Single-Player simulation</a></div><div class="lev2 toc-item"><a href="... | github_jupyter |
## Incorporating Neural Networks
author: Jacob Schreiber <br>
contact: jmschreiber91@gmail.com
Neural networks have become exceedingly popular recently due, in part, to their ability to achieve state-of-the-art performance on a variety of tasks without requiring complicated feature extraction pipelines. These models ... | github_jupyter |
# An Astronomical Application of Machine Learning:
Separating Stars and Galaxies from SDSS
====
##### Version 0.1
***
By AA Miller 2018 Nov 06
The problems in the following notebook develop an end-to-end machine learning model using actual astronomical data to separate stars and galaxies. There are 5 steps in this ... | github_jupyter |
# Exploratory Data Analysis
Preparing the BRFSS dataset
Allen Downey
[MIT License](https://en.wikipedia.org/wiki/MIT_License)
```
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='white')
import utils
from utils import decorate
from dis... | github_jupyter |
# Manifold Learning: t-SNE and UMAP for Equity Return
This notebook explores how [t-SNE](https://lvdmaaten.github.io/tsne/) and UMAP perform on equity returns.
## Imports & Settings
```
%matplotlib inline
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.decom... | 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 |
##### 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 |
# Scikit-learn Pipeline Persistence and JSON Serialization Part II
By Chris Emmery, 14-04-2016, 5 minute read
---
*This is a follow-up to [this](./serialize) post.*
In my last entry, I wrote about several hurdles on the way to replacing pickle
with JSON for storing scikit-learn pipelines. While my previous solut... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import torch
from scipy.special import factorial
from IPython.display import display, Latex
def get_D_Coeffs(s,d=2):
'''
Solve arbitrary stencil points s of length N with order of derivatives d<N
can be obtained from equation on MIT website
http://w... | github_jupyter |
```
f = 'text.txt'
file = open(f,'r')
text = ''
for line in file.readlines():
text+=str(line)
text+=" "
file.close()
print(text)
import nltk
from nltk import word_tokenize
import string
text1 = word_tokenize(text) #tokenize by word
case_insensitive_text = word_tokenize(text.lower()) #lowercase
#Segmentating... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import mpl_toolkits
%matplotlib inline
data = pd.read_csv("kc_house_data.csv")
data.head()
data.describe()
data['bedrooms'].value_counts().plot(kind='bar')
plt.title('number of Bedroom')
plt.xlabel('Bedrooms')
plt.ylabel('C... | github_jupyter |
```
from graphblas import Matrix, Vector
from graphblas import unary, binary, monoid, semiring, dtypes
from graphblas import io as gio
```
### Basic syntax
Let's examine some basic graphblas syntax
```
A = Matrix.from_values(
[0, 0, 1, 2, 2, 3, 4],
[1, 2, 3, 3, 4, 4, 0],
[1.1, 9.8, 4.2, 7.1, 0.2, 6.9, 2.... | github_jupyter |
```
import numpy as np
import h5py
# Helper function to help read the h5 files.
def simple_read_data(fileName):
print(fileName)
hf = h5py.File('{}.h5'.format(fileName), 'r')
# We'll return a dictionary object.
results = {}
results['rs_glob_acc'] = np.array(hf.get('rs_glob_acc')[:])
re... | github_jupyter |
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