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# Soccerstats Predictions v1.2
The changelog from v1.1:
* Train on `train` data, and validate using `test` data.
## A. Data Cleaning & Preparation
### 1. Read csv file
```
# load and cache data
stat_df = sqlContext.read\
.format("com.databricks.spark.csv")\
.options(header = True)\
.load("data/teamFixtu... | github_jupyter |
# Credits
Updated to detectwaste by:
* Sylwia Majchrowska
```
%matplotlib inline
import sys
from pycocotools.coco import COCO
import json
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
import os
import skimage
import skimage.io as io
import copy
def show_values... | github_jupyter |
## In this notebook we are going to Predict the Growth of Google Stock using LSTM Model and CRISP-DM.
```
#importing the libraries
import math
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, LSTM
import matplotlib... | github_jupyter |
# Training a dense neural network
The handwritten digit recognition is a classification problem. We will start with the simplest possible approach for image classification - a fully-connected neural network (which is also called a *perceptron*). We use `pytorchcv` helper to load all data we have talked about in the pr... | github_jupyter |
---
# Pandas Introduction
### with Stock Data and Correlation Examples
**Author list:** Alexander Fred-Ojala & Ikhlaq Sidhu
**References / Sources:**
Includes examples from Wes McKinney and the 10min intro to Pandas
**License Agreement:** Feel free to do whatever yo... | github_jupyter |
| Name | Surname | Student No | Department |
|---|---|---|---|
| Emin | Kartci | S014877 | EE Engineering |
## Emin Kartci
#### Student ID: S014877
#### Department : Electrical & Electronics Engineering
---
### Semester Project - Foursquare & Restaurant Report
---
#### This module is prepared for GUI
---
``... | github_jupyter |
```
import argparse
import glob
import io
import os
import random
import numpy
from PIL import Image, ImageFont, ImageDraw
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter
SCRIPT_PATH = os.path.dirname(os.path.abspath('./hangul-WR'))
# Default data paths.
DEFAU... | github_jupyter |
```
from os import environ
environ['optimizer'] = 'Adam'
environ['num_workers']= '2'
environ['batch_size']= str(2048)
environ['n_epochs']= '1000'
environ['batch_norm']= 'True'
environ['loss_func']='MAPE'
environ['layers'] = '600 350 200 180'
environ['dropouts'] = '0.1 '* 4
environ['log'] = 'False'
environ['weight_deca... | github_jupyter |
# Hyperparameter Optimization (HPO) of Machine Learning Models
L. Yang and A. Shami, “On hyperparameter optimization of machine learning algorithms: Theory and practice,” Neurocomputing, vol. 415, pp. 295–316, 2020, doi: https://doi.org/10.1016/j.neucom.2020.07.061.
### **Sample code for regression problems**
**Data... | github_jupyter |
## 1. Winter is Coming. Let's load the dataset ASAP!
<p>If you haven't heard of <em>Game of Thrones</em>, then you must be really good at hiding. Game of Thrones is the hugely popular television series by HBO based on the (also) hugely popular book series <em>A Song of Ice and Fire</em> by George R.R. Martin. In this n... | github_jupyter |
## CNN on MNIST digits classification
This example is the same as the MLP for MNIST classification. The difference is we are going to use `Conv2D` layers instead of `Dense` layers.
The model that will be costructed below is made of:
- First 2 layers - `Conv2D-ReLU-MaxPool`
- 3rd layer - `Conv2D-ReLU`
- 4th layer - `... | github_jupyter |
<a href="https://colab.research.google.com/github/BNN-UPC/ignnition/blob/ignnition-nightly/notebooks/shortest_path.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# IGNNITION: Quick start tutorial
### **Problem**: Find the shortest path in graphs w... | github_jupyter |
# Lecture 3: Optimize, print and plot
[Download on GitHub](https://github.com/NumEconCopenhagen/lectures-2019)
[<img src="https://mybinder.org/badge_logo.svg">](https://mybinder.org/v2/gh/NumEconCopenhagen/lectures-2019/master?urlpath=lab/tree/03/Optimize_print_and_plot.ipynb)
1. [The consumer problem](#The-consumer... | github_jupyter |
# Step 1) Data Preparation
```
%run data_prep.py INTC
import pandas as pd
df = pd.read_csv("../1_Data/INTC.csv",infer_datetime_format=True, parse_dates=['dt'], index_col=['dt'])
trainCount=int(len(df)*0.4)
dfTrain = df.iloc[:trainCount]
dfTest = df.iloc[trainCount:]
dfTest.to_csv('local_test/test_dir/input/data/tr... | github_jupyter |
This notebook is part of the `nbsphinx` documentation: https://nbsphinx.readthedocs.io/.
# Installation
Note that some packages may be out of date.
You can always get the newest `nbsphinx` release from [PyPI](https://pypi.org/project/nbsphinx) (using `pip`).
If you want to try the latest development version, have a l... | github_jupyter |
# Building your Deep Neural Network: Step by Step
Welcome to your week 4 assignment (part 1 of 2)! You have previously trained a 2-layer Neural Network (with a single hidden layer). This week, you will build a deep neural network, with as many layers as you want!
- In this notebook, you will implement all the functio... | github_jupyter |
```
import time
import numpy as np
import random
def write_table2sql(table, engine, sql=None):
def select_col_agg(mask):
"""
select col agg pair
:return:
"""
col_num = len(table['header'])
sel_idx = np.argmax(np.random.rand(col_num) * mask)
sel_type = table[... | github_jupyter |
###### Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Daniel Koehn based on Jupyter notebooks by Marc Spiegelman [Dynamical Systems APMA 4101](https://github.com/mspieg/dynamical-systems) and Kyle Mandli from his course [Introductio... | github_jupyter |
<i>Copyright (c) Microsoft Corporation. All rights reserved.</i>
<i>Licensed under the MIT License.</i>
# Vowpal Wabbit Deep Dive
<center>
<img src="https://github.com/VowpalWabbit/vowpal_wabbit/blob/master/logo_assets/vowpal-wabbits-github-logo.png?raw=true" height="30%" width="30%" alt="Vowpal Wabbit">
</center>
... | github_jupyter |
# Peak Detection
Feature detection, also referred to as peak detection, is the process by which local maxima that fulfill certain criteria (such as sufficient signal-to-noise ratio) are located in the signal acquired by a given analytical instrument.
This process results in “features” associated with the analysis of ... | github_jupyter |
```
import os
import numpy as np
from glob import glob
from deformation_functions import *
from menpo_functions import *
from logging_functions import *
from data_loading_functions import *
from time import time
from scipy.misc import imsave
%matplotlib inline
dataset='training'
img_dir='/Users/arik/Dropbox/a_mac_thes... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import cython
import timeit
import math
%load_ext cython
```
# Native code compilation
We will see how to convert Python code to native compiled code. We will use the example of calculating the pairwise distance between a set of vectors, a $O(n^2)$ operation.
F... | github_jupyter |
# MSOA Mapping - England
```
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
import numpy as np
from shapely.geometry import Point
from sklearn.neighbors import KNeighborsRegressor
import rasterio as rst
from rasterstats import zonal_stats
%matplotlib inline
path = r"[CHANGE THIS PATH]\Eng... | github_jupyter |
<font size = "5"> **Chapter 4: [Spectroscopy](CH4-Spectroscopy.ipynb)** </font>
<hr style="height:1px;border-top:4px solid #FF8200" />
# Analysis of Core-Loss Spectra
<font size = "5"> **This notebook does not work in Google Colab** </font>
[Download](https://raw.githubusercontent.com/gduscher/MSE672-Introduct... | 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 |
## Problem
Given a sorted list of integers of length N, determine if an element x is in the list without performing any multiplication, division, or bit-shift operations.
Do this in `O(log N)` time.
## Solution
We can't use binary search to locate the element because involves dividing by two to get the middle elemen... | github_jupyter |
# Feature processing with Spark, training with BlazingText and deploying as Inference Pipeline
Typically a Machine Learning (ML) process consists of few steps: gathering data with various ETL jobs, pre-processing the data, featurizing the dataset by incorporating standard techniques or prior knowledge, and finally tra... | github_jupyter |
# Polynomial Regression
```
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['axes.titlesize'] = 14
plt.rcParams['legend.fontsize'] = 12
plt.rcParams['figure.figsize'] = (8, 5)
%config InlineBackend.figure_format = 'retina'
```
### Linear models
$y = \beta_0 + \beta... | github_jupyter |
```
import pickle
import pandas as pd
import numpy as np
import os, sys, gc
from plotnine import *
import plotnine
from tqdm import tqdm_notebook
import seaborn as sns
import warnings
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm
import matplotlib as mpl
from matplotlib import rc
import re
from... | github_jupyter |
# Python Functions
```
import numpy as np
```
## Custom functions
### Anatomy
name, arguments, docstring, body, return statement
```
def func_name(arg1, arg2):
"""Docstring starts wtih a short description.
May have more information here.
arg1 = something
arg2 = somehting
Returns ... | github_jupyter |
```
# Require the packages
require(ggplot2)
library(repr)
options(repr.plot.width=15, repr.plot.height=4.5)
ladder_results_dir <- "../resources/results/ladder_results_sensem/140"
bootstrap_results_dir <- "../resources/results/results_semisupervised_sensem_7k/140"
lemma_data <- data.frame(iteration=integer(), sense=cha... | github_jupyter |
<a href="https://colab.research.google.com/github/Tessellate-Imaging/monk_v1/blob/master/study_roadmaps/4_image_classification_zoo/Classifier%20-%20Weed%20Species%20Classification%20-%20Hyperparameter%20Tuning%20using%20Monk.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt... | github_jupyter |
## $k$-means clustering: An example implementation in Python 3 with numpy and matplotlib.
The [$k$-means](https://en.wikipedia.org/wiki/K-means_clustering) algorithm is an unsupervised learning method for identifying clusters within a dataset. The $k$ represents the number of clusters to be identified, which is specif... | github_jupyter |
# Biological question: Are there differences in the binding distance of the same TF-pair in different clusters? - PART2
This notebook can be used to analyse if there are differences in the binding distance of the same TF-pair in two different clusters.
In "Outline of this notebook" the general steps in the notebook a... | github_jupyter |
```
#!pwd
import pandas as pd
import os
import string
from nltk.corpus import stopwords
from nltk import word_tokenize, WordNetLemmatizer
from nltk import stem, pos_tag
from nltk.corpus import wordnet as wn
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
import os
import re
cwd = os.getcwd(... | github_jupyter |
# TV Script Generation
In this project, you'll generate your own [Seinfeld](https://en.wikipedia.org/wiki/Seinfeld) TV scripts using RNNs. You'll be using part of the [Seinfeld dataset](https://www.kaggle.com/thec03u5/seinfeld-chronicles#scripts.csv) of scripts from 9 seasons. The Neural Network you'll build will ge... | github_jupyter |
### Requirement
```
aliyun-python-sdk-core==2.13.25
aliyun-python-sdk-ocr==1.0.8
Flask==1.1.2
imutils==0.5.3
json5==0.9.5
Keras==2.4.3
Keras-Preprocessing==1.1.2
matplotlib==3.3.0
numpy==1.18.5
opencv-python==4.4.0.40
oss2==2.12.1
Pillow==7.0.0
sklearn==0.0
tensorflow==2.3.0
trdg==1.6.0
```
### Import Aliyun python S... | github_jupyter |
```
%matplotlib inline
%run utils.ipynb
import matplotlib.pyplot as plt
from matplotlib import colors, ticker
# import cartopy.crs as ccrs
import pandas as pd
import numpy as np
import scipy as sp
from astropy.table import Table
import astropy.units as u
import astropy.coordinates as coord
import arviz as az
import se... | github_jupyter |
# Scaling up ML using Cloud AI Platform
In this notebook, we take a previously developed TensorFlow model to predict taxifare rides and package it up so that it can be run in Cloud AI Platform. For now, we'll run this on a small dataset. The model that was developed is rather simplistic, and therefore, the accuracy of... | github_jupyter |
# Riskfolio-Lib Tutorial:
<br>__[Financionerioncios](https://financioneroncios.wordpress.com)__
<br>__[Orenji](https://www.orenj-i.net)__
<br>__[Riskfolio-Lib](https://riskfolio-lib.readthedocs.io/en/latest/)__
<br>__[Dany Cajas](https://www.linkedin.com/in/dany-cajas/)__
<a href='https://ko-fi.com/B0B833SXD' target='... | github_jupyter |
# 多层感知机
:label:`sec_mlp`
在 :numref:`chap_linear`中，
我们介绍了softmax回归（ :numref:`sec_softmax`），
然后我们从零开始实现softmax回归（ :numref:`sec_softmax_scratch`），
接着使用高级API实现了算法（ :numref:`sec_softmax_concise`），
并训练分类器从低分辨率图像中识别10类服装。
在这个过程中，我们学习了如何处理数据，如何将输出转换为有效的概率分布，
并应用适当的损失函数，根据模型参数最小化损失。
我们已经在简单的线性模型背景下掌握了这些知识，
现在我们可以开始对深度神经网络的探索，这... | github_jupyter |
```
import numpy as np
from numpy import loadtxt
import pylab as pl
from IPython import display
from RcTorchPrivate import *
from matplotlib import pyplot as plt
from scipy.integrate import odeint
%matplotlib inline
#this method will ensure that the notebook can use multiprocessing on jupyterhub or any other linux base... | github_jupyter |
# Using TensorNet (Basic)
This notebook will demonstrate some of the core functionalities of TensorNet:
- Creating and setting up a dataset
- Augmenting the dataset
- Creating and configuring a model and viewing its summary
- Defining an optimizer and a criterion
- Setting up callbacks
- Training and validating the m... | github_jupyter |
# Exploring Datasets with Python
In this short demo we will analyse a given dataset from 1978, which contains information about politicians having affairs.
To analyse it, we will use a Jupyter Notebook, which is basically a REPL++ for Python. Entering a command with shift executes the line and prints the result.
```... | github_jupyter |
```
import torch
from transformers import MT5ForConditionalGeneration, MT5Config, MT5EncoderModel, MT5Tokenizer, Trainer, TrainingArguments
from progeny_tokenizer import TAPETokenizer
import numpy as np
import math
import random
import scipy
import time
import pandas as pd
from torch.utils.data import DataLoader, Rando... | github_jupyter |
# Part 1 - 2D mesh tallies
So far we have seen that neutron and photon interactions can be tallied on surfaces or cells, but what if we want to tally neutron behaviour throughout a geometry? (rather than the integrated neutron behaviour over a surface or cell).
A mesh tally allows a visual inspection of the neutron b... | github_jupyter |
```
from fastai.text import *
from fastai.tabular import *
path = Path('')
data = pd.read_csv('good_small_dataset.csv', engine='python')
data.head()
df = data.dropna()
df.to_csv('good_small_dataset_drop_missing.csv')
data_lm = TextLMDataBunch.from_csv(path, 'good_small_dataset_drop_missing.csv', text_cols = 'content', ... | github_jupyter |
Taken from fastai NLP "8-translation-transformer"
FastText embeddings: https://fasttext.cc/docs/en/crawl-vectors.html
```
from fastai2.text.all import *
from fastai2.callback.all import *
from fastai2.basics import *
import seaborn as sns
from einops import rearrange
import gc
import csv
path = Path('../data/irish/c... | github_jupyter |
# Deep Markov Model
## Introduction
We're going to build a deep probabilistic model for sequential data: the deep markov model. The particular dataset we want to model is composed of snippets of polyphonic music. Each time slice in a sequence spans a quarter note and is represented by an 88-dimensional binary vector... | github_jupyter |
```
!pip install --upgrade language-check
import numpy as np
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import CountVectorizer,_preprocess,TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel,cosine_similarity
from nltk.stem.snowball imp... | 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 |
```
import os
import numpy as np
import pandas as pd
import json
import pickle
from scipy import sparse
import scipy.io
dataset_name = 'dblp'
data_path = os.path.join('../dataset/raw/{}'.format(dataset_name))
citations = []
incomming = {}
for i in range(4):
fn = os.path.join(data_path, 'dblp-ref-{}.json'.format(i... | github_jupyter |
# Introduction to PyCaret - An open source low-code ML library
## This notebook consists 2 parts
- Classification part using Titanic DataSet
- Regression part using House Price Regression DataSet
You can reach pycaret website and documentation from ... | github_jupyter |
## Boxplot plots
_______
tg: @misha_grol and anna.petrovskaia@skoltech.ru
Boxplots for features based on DEM and NDVI
```
# Uncomment for Google colab
# !pip install maxvolpy
# !pip install clhs
# !git clone https://github.com/EDSEL-skoltech/maxvol_sampling
# %cd maxvol_sampling/
import csv
import seaborn as ... | github_jupyter |
```
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.applications.resnet50 import ResNet50, preprocess_input
from tensorflow.python.keras.preprocessing import image
from tensorflow.python.keras.layers import Conv2D, GlobalAveragePooling2D, Input, Dropout, Dense
from tensorflow.python.keras.... | github_jupyter |
```
import csv
import numpy as np
import tensorflow as tf
from sklearn.model_selection import train_test_split
RANDOM_SEED = 42
```
# 各パス指定
```
dataset = 'model/point_history_classifier/point_history_allkeypoints.csv'
model_save_path = 'model/point_history_classifier/point_history_classifier_allkeypoints.hdf5'
```
... | github_jupyter |
# Shor's algorithm, fully classical implementation
```
%matplotlib inline
import random
import math
import itertools
def period_finding_classical(a,N):
# This is an inefficient classical algorithm to find the period of f(x)=a^x (mod N)
# f(0) = a**0 (mod N) = 1, so we find the first x greater than 0 for which ... | github_jupyter |
```
%reset
```
# Simulate particles translating through OAM beam
Liz Strong 4/17/2020
```
import sys
sys.path.append('../slvel')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from calc_intensity import calculate_e_field_intensity
from scattering_particle import Particle
import scattering_... | github_jupyter |
# 02. Custom Dataset 만들어보기
- Dataset Generation!
- 폴더별로 사진들이 모여있다면, 그 dataset을 우리가 원하는 형태로 바꿔봅시다!
```
import numpy as np
import os
from scipy.misc import imread, imresize
import matplotlib.pyplot as plt
%matplotlib inline
print ("Package loaded")
cwd = os.getcwd()
print ("Current folder is %s" % (cwd) )
# 학습할 폴더 경로... | 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 |
**Principal Component Analysis (PCA)** is widely used in Machine Learning pipelines as a means to compress data or help visualization. This notebook aims to walk through the basic idea of the PCA and build the algorithm from scratch in Python.
Before diving directly into the PCA, let's first talk about several import ... | github_jupyter |
```
# Visualization of the KO+ChIP Gold Standard from:
# Miraldi et al. (2018) "Leveraging chromatin accessibility for transcriptional regulatory network inference in Th17 Cells"
# TO START: In the menu above, choose "Cell" --> "Run All", and network + heatmap will load
# NOTE: Default limits networks to TF-TF edges i... | github_jupyter |
[this doc on github](https://github.com/dotnet/interactive/tree/master/samples/notebooks/polyglot)
# Visualizing the Johns Hopkins COVID-19 time series data
**This is a work in progress.** It doesn't work yet in [Binder](https://mybinder.org/v2/gh/dotnet/interactive/master?urlpath=lab) because it relies on HTTP commu... | github_jupyter |
<a href="https://colab.research.google.com/github/ai-fast-track/icevision-gradio/blob/master/IceApp_pets.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# IceVision Deployment App: PETS Dataset
This example uses Faster RCNN trained weights using the... | github_jupyter |
<div class="alert alert-info" role="alert">
This tutorial contains a lot of bokeh plots, which may take a little while to load and render.
</div>
``Element``s are the basic building blocks for any HoloViews visualization. These are the objects that can be composed together using the various [Container](Containers.... | github_jupyter |
# Testing Click-Through-Rates for Banner Ads (A/B Testing)
* Lets say we are a new apparel store; after thorough market research, we decide to open up an <b> Online Apparel Store.</b> We hire Developers, Digital Media Strategists and Data Scientists, who help develop the store, place products and conduct controlled ex... | github_jupyter |
<a href="https://colab.research.google.com/github/Tessellate-Imaging/monk_v1/blob/master/study_roadmaps/1_getting_started_roadmap/5_update_hyperparams/1_model_params/5)%20Switch%20deep%20learning%20model%20from%20default%20mode.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" ... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import gpplot as gpp
from poola import core as pool
import anchors
import core_functions as fns
gpp.set_aesthetics(palette='Set2')
def run_guide_residuals(lfc_df, paired_lfc_cols=[]):
'''
Calls get_guide_residuals... | github_jupyter |
```
from path import Path
from PIL import Image
import cv2
import random
import pandas as pd
import pickle
def arg_parse():
parser = argparse.ArgumentParser()
parser = argparse.ArgumentParser(
prog="annotation.py",
usage="annotation.py -n <<num_of_evaluation>>", ... | github_jupyter |
# Extra Trees Classifier with MinMax Scaler
### Required Packages
```
import numpy as np
import pandas as pd
import seaborn as se
import warnings
import matplotlib.pyplot as plt
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.preprocessing import LabelEncoder, MinMaxScaler
from sklearn.model_selection ... | github_jupyter |
```
% matplotlib inline
import matplotlib.pyplot as plt
from matplotlib import colors, cm
import numpy as np
from numpy import matmul
from scipy.spatial.distance import pdist, squareform
from sklearn.datasets import load_diabetes
import pandas as pd
from scipy.linalg import cholesky
from scipy.linalg import solve
from ... | github_jupyter |
# Implementing doND using the dataset
```
from functools import partial
import numpy as np
from qcodes.dataset.database import initialise_database
from qcodes.dataset.experiment_container import new_experiment
from qcodes.tests.instrument_mocks import DummyInstrument
from qcodes.dataset.measurements import Measureme... | github_jupyter |
```
!pip install seaborn
!pip install newspaper3k
import nltk
nltk.download('stopwords')
```
The next two lines are required to load files from your Google drive.
```
from google.colab import drive
drive.mount('/content/drive')
```
# SCRAPER
```
from newspaper import Article
from newspaper import ArticleException
i... | github_jupyter |
# Cross-asset skewness
This notebook analyses cross-asset cross-sectional skewness strategy. The strategy takes long positions on contracts with most negative historical skewness and short positions on ones with most positive skewness.
```
%matplotlib inline
from datetime import datetime
import logging
import warning... | github_jupyter |
# GFA Zero Calibration
GFA calibrations should normally be updated in the following sequence: zeros, flats, darks.
This notebook should be run using a DESI kernel, e.g. `DESI master`.
```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import os
import sys
import json
import collections
from pa... | github_jupyter |
```
import numpy as np
#UNITS
#A = mol/cm^3 -s
#n = none
#Ea = kcal/k*mol
#c =
#d =
#f =
six_parameter_fit_sensitivities = {'H2O2 + OH <=> H2O + HO2':{'A':np.array([-13.37032086, 32.42060027, 19.23022032, 6.843287462 , 36.62853824 ,-0.220309785 ,-0.099366346, -4.134352081]),
... | github_jupyter |
```
from music21 import *
import numpy as np
import torch
import pretty_midi
import os
import sys
import pickle
import time
import random
import re
class MusicData(object):
def __init__(self, abc_file, culture= None):
self.stream = None
self.metadata = dict()
self.description = None
... | github_jupyter |
```
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm
%matplotlib inline
import datetime
import cPickle as pickle
import csv
import numpy as np
import random
import sys
maxInt = sys.maxsize
decrement = True
while decrement:
# decrease the maxInt value by factor 10
# as long as the Overfl... | github_jupyter |
<table>
<tr>
<td ><h1><strong>NI SystemLink Analysis Automation</strong></h1></td>
</tr>
</table>
This notebook is an example for how you can analyze your data with NI SystemLink Analysis Automation. It forms the core of the analysis procedure, which includes the notebook, the query, and the execution ... | github_jupyter |
<a href="https://colab.research.google.com/github/Serbeld/ArtificialVisionForQualityControl/blob/master/Copia_de_Yolo_Step_by_Step.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
**Outline of Steps**
+ Initialization
+ Download COCO dete... | github_jupyter |
```
import numpy as np
import librosa
import os
import random
import tflearn
import tensorflow as tf
lr = 0.001
iterations_train = 30
bsize = 64
audio_features = 20
utterance_length = 35
ndigits = 10
def get_mfcc_features(fpath):
raw_w,sampling_rate = librosa.load(fpath,mono=True)
mfcc_features = librosa.fe... | github_jupyter |
```
import sys
sys.path.append('../../../GraphGallery/')
sys.path.append('../../../GraphAdv/')
import tensorflow as tf
import numpy as np
import networkx as nx
import scipy.sparse as sp
from graphgallery.nn.models import GCN
from graphgallery.nn.functions import softmax
from graphadv.attack.targeted import IGA
impo... | github_jupyter |
... ***CURRENTLY UNDER DEVELOPMENT*** ...
## Synthetic simulation of historical TCs parameters using Gaussian copulas (Rueda et al. 2016) and subsequent selection of representative cases using Maximum Dissimilarity (MaxDiss) algorithm (Camus et al. 2011)
inputs required:
* Historical TC parameters that affect the... | github_jupyter |
# Preprocessing Part
## Author: Xiaochi (George) Li
Input: "data.xlsx" provided by the professor
Output: "processed_data.pickle" with target variable "Salary" as the last column. And all the missing value should be imputed or dropped.
### Summary
In this part, we read the data from the file, did some exploratory d... | github_jupyter |
# MPIJob and Horovod Runtime
## Running distributed workloads
Training a Deep Neural Network is a hard task. With growing datasets, wider and deeper networks, training our Neural Network can require a lot of resources (CPUs / GPUs / Mem and Time).
There are two main reasons why we would like to distribute our Dee... | github_jupyter |
# Amazon Augmented AI(A2I) Integrated with AWS Marketplace ML Models
Sometimes, for some payloads, machine learning (ML) model predictions are just not confident enough and you want more than a machine. Furthermore, training a model can be complicated, time-consuming, and expensive. This is where [AWS Marketplace](htt... | github_jupyter |
# NLP - Using spaCy library
- **Created by Andrés Segura Tinoco**
- **Created on June 04, 2019**
- **Updated on October 29, 2021**
**Natural language processing (NLP):** is a discipline where computer science, artificial intelligence and cognitive logic are intercepted, with the objective that machines can read and u... | github_jupyter |
# Tutorial Part 11: Learning Unsupervised Embeddings for Molecules
In this example, we will use a `SeqToSeq` model to generate fingerprints for classifying molecules. This is based on the following paper, although some of the implementation details are different: Xu et al., "Seq2seq Fingerprint: An Unsupervised Deep... | github_jupyter |
```
import argparse
import logging
from operator import mul
import time
import os
import pubweb.singlecell # import AnnDataSparse
from pubweb.hdf5 import Hdf5
from pubweb.commands.convert.singlecell.anndata import ImportAnndata
from pubweb.commands.convert.singlecell.cellranger import ImportCellRanger
from pubweb.comm... | github_jupyter |
# Best-practices for Cloud-Optimized Geotiffs
**Part 2. Multiple COGs**
This notebook goes over ways to construct a multidimensional xarray DataArray from many 2D COGS
```
import dask
import s3fs
import intake
import os
import xarray as xr
import pandas as pd
# use the same GDAL environment settings as we did for th... | github_jupyter |
Script delete Cassandra en cluster multidomain
```
!pip install mysql-connector==2.1.7
!pip install pandas
!pip install sqlalchemy
#requiere instalación adicional, consultar https://github.com/PyMySQL/mysqlclient
!pip install mysqlclient
!pip install numpy
!pip install pymysql
import pandas as pd
import numpy as np
im... | github_jupyter |
```
%matplotlib inline
from mpl_toolkits.mplot3d import Axes3D
import scipy.io as io
import numpy as np
import matplotlib.pyplot as plt
from math import ceil
from scipy.optimize import curve_fit
realization = 1000
import seaborn as sns
from matplotlib import cm
from array_response import *
import itertools
mat = io.l... | github_jupyter |
<h1 align="center">Introduction to SimpleITKv4 Registration</h1>
<table width="100%">
<tr style="background-color: red;"><td><font color="white">SimpleITK conventions:</font></td></tr>
<tr><td>
<ul>
<li>Dimensionality and pixel type of registered images is required to be the same (2D/2D or 3D/3D).</li>
<li>Supported ... | github_jupyter |
# Fit $k_{ij}$ and $r_c^{ABij}$ interactions parameter of Ethanol and CPME
---
Let's call $\underline{\xi}$ the optimization parameters of a mixture. In order to optimize them, you need to provide experimental phase equilibria data. This can include VLE, LLE and VLLE data. The objective function used for each equilibr... | github_jupyter |
<table width=60%>
<tr style="background-color: white;">
<td><img src='https://www.creativedestructionlab.com/wp-content/uploads/2018/05/xanadu.jpg'></td>></td>
</tr>
</table>
---
<img src='https://raw.githubusercontent.com/XanaduAI/strawberryfields/master/doc/_static/strawberry-fields-text.png'>
---
... | github_jupyter |
This notebook is part of the $\omega radlib$ documentation: https://docs.wradlib.org.
Copyright (c) $\omega radlib$ developers.
Distributed under the MIT License. See LICENSE.txt for more info.
# How to use wradlib's ipol module for interpolation tasks?
```
import wradlib.ipol as ipol
from wradlib.util import get_wr... | github_jupyter |
## Exploratory Data Analysis
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
# read dataset
df = pd.read_csv('../datasets/winequality/winequality-red.csv',sep=';')
# check data dimensions
print(df.shap... | github_jupyter |
# Tutorial for Geoseg
> __version__ == 0.1.0
> __author__ == Go-Hiroaki
# Overview:
## 1. Evaluating with pretrained models
> Test model performance by providing pretrained models
## 2. Re-training with provided dataset
> Trained new models with provide training datastet
## 3. Training with personal dataset
>... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
##### Functions
# 1st function: to graph time series based on TransactionDT vs the variable selected
def scatter(column):
fr,no_fr = (train[train['isFraud'] == 1], tra... | github_jupyter |
# Dropout
Dropout [1] is a technique for regularizing neural networks by randomly setting some features to zero during the forward pass. In this exercise you will implement a dropout layer and modify your fully-connected network to optionally use dropout.
[1] [Geoffrey E. Hinton et al, "Improving neural networks by pr... | github_jupyter |
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