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# Distributed Quantum Phase Estimation Algorithm
Quantum phase estimation is a quantum algorithm which is used to estimate the phase (or eigenvalue) of an eigenvector of a unitary operator. If we consider a unitary matrix $U$ and a quantum state $|\psi \rangle$ such that $U|\psi \rangle =e^{2\pi i\theta }$, the algo... | github_jupyter |
### Response Figure 1
Since this manuscript's main advance regarding variation in paternal age effect over the Rahbari, et al. result is greater statistical power, more robust statistical analyses of this pattern would strengthen the paper. Figure 3 presents a commendable amount of raw data in a fairly clear way, yet ... | github_jupyter |
# Train a QA model
The [Hugging Face Model Hub](https://huggingface.co/models) has a wide range of models that can handle many tasks. While these models perform well, the best performance is often found when fine-tuning a model with task-specific data.
Hugging Face provides a [number of full-featured examples](https... | github_jupyter |
```
# code edited by Tanvi Deora on 28th Sept
# decomposes the x y coordinate tracks of each visits into PCA components
# output 1) Do we see a separation acorss light or visits
# output 2) what are PCA or SVD components that get pulled up as the first few vectors
import cv2
import matplotlib.pyplot as plt
import glob
... | github_jupyter |
```
import random
import torch,torchvision
import cv2
import os
from tqdm import tqdm
import matplotlib.pyplot as plt
import wandb
import numpy as np
from torch.nn import *
from torch.optim import *
from torchvision.transforms import *
torch.manual_seed(42)
np.random.seed(42)
random.seed(42)
IMG_SIZE = 224
PROJECT_NAME... | github_jupyter |
<h1 align='center' style="margin-bottom: 0px"> An end to end implementation of a Machine Learning pipeline </h1>
<h4 align='center' style="margin-top: 0px"> SPANDAN MADAN</h4>
<h4 align='center' style="margin-top: 0px"> Visual Computing Group, Harvard University</h4>
<h4 align='center' style="margin-top: 0px"> Computer... | github_jupyter |
```
import os
import sys
import subprocess
import shlex
import operator
```
## Transmission-Distribution Power Flow Co-simulation
This script runs a transmission-distribution power flow. The network is assumed to consist of a single transmission network connected to distribution feeders at each load bus. The number of... | github_jupyter |
# Developing an AI application
Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall appli... | github_jupyter |
# 加载模型用于推理或迁移学习
## 概述
在模型训练过程中保存在本地的CheckPoint文件，或从MindSpore Hub下载的CheckPoint文件，都可以帮助用户进行推理或迁移学习使用，提高效率。
以下通过示例来介绍如何通过本地加载加载模型，用于推理验证和迁移学习。
> 本文档适用于CPU、GPU和Ascend环境。
## 整体流程
1. 准备环节。下载数据集，配置运行信息。
2. 数据处理。创建可用于网络训练的数据集，可视化数据集图像。
3. 预训练模型。生成CheckPoint文件。
4. 本地加载模型用于推理验证。
5. 本地加载模型用于迁移学习。
## 准备环节
### 下载数据集
运行以下... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
```
This notebook will closely follow the [Simulating Molecules using VQE Qiskit Tutorial](https://qiskit.org/textbook/ch-applications/vqe-molecules.html).
Our goal is to estimate the ground state energy $\lambda_{\text{min}}$ of some Hamiltonian $H$.
By the variational princi... | github_jupyter |
# Machine Learning Test
You have 3 days to solve the test from the moment you receive it.
Show us your skills !
## Problem description
You are hired as a Data Scientist at a top real state company in California, and you first job is to develop an ML model to predict house prices. This model will then be used as an... | github_jupyter |
<h1><font size=12>
Weather Derivates </h1>
<h1> Precipitation Bogota Exploration - El Dorado Airport<br></h1>
Developed by [Jesus Solano](mailto:ja.solano588@uniandes.edu.co) <br>
31 Julio 2018
```
# Configure path to read txts.
path = '../datasets/ideamBogota/'
# Download the update dataset.
import os
if not os.... | github_jupyter |
This demo provides examples of `ImageReader` class from `niftynet.io.image_reader` module.
What is `ImageReader`?
The main functionality of `ImageReader` is to search a set of folders, return a list of image files, and load the images into memory in an iterative manner.
A `tf.data.Dataset` instance can be initialise... | github_jupyter |
#### Metode i primjena vjestacke inteligencije
#### Laboratorijska vjezba 2
#### Student: Masovic Haris
#### Index: 1689/17993
## 0. Dependencies
```
import sys
!{sys.executable} -m pip install tabletext
```
## 2. KNN algoritam
### 2.1 Implementacija KNN algoritma
```
from collections import Counter
from tabletex... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# Plot style
sns.set()
%pylab inline
pylab.rcParams['figure.figsize'] = (4, 4)
%%html
<style>
.pquote {
text-align: left;
margin: 40px 0 40px auto;
width: 70%;
font-size: 1.5em;
font-style: italic;
display: block;
line-height: 1.... | github_jupyter |
# Regression Week 4: Ridge Regression (interpretation)
In this notebook, we will run ridge regression multiple times with different L2 penalties to see which one produces the best fit. We will revisit the example of polynomial regression as a means to see the effect of L2 regularization. In particular, we will:
* Use ... | github_jupyter |
# Appendix A - A Crash Course in Python
## IPython and Jupyter
Basic notebook operations.
```
a = 'hello'
b = 'world'
a + ' ' + b
```
Referencing output from previous cells.
```
Out[3]
```
Referencing input from previous cells.
```
In[3]
a
```
# Data Types and Collections
## Numeric Data Types
```
type(100)
t... | github_jupyter |
# <div style="text-align: center">Tutorial on Ensemble Learning </div>
<img src='https://data-science-blog.com/wp-content/uploads/2017/12/ensemble-learning-stacking.png' width=400 height=400 >
### <div style="text-align: center"> Quite Practical and Far from any Theoretical Concepts </div>
<div style="text-align:cent... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/W2D1-postcourse-bugfix/tutorials/W2D2_LinearSystems/W2D2_Tutorial2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Neuromatch Academy 2020, Week 2, Day 2, Tuto... | github_jupyter |
# Land Cover Change Detection and Description
The purpose of this notebook is to provide an easy to use method for comparing and visualising differences between 2 tiles which were classified using the L3 FAO land cover classification system.
```
%matplotlib inline
import xarray as xr
import numpy as np
import lccs_ch... | github_jupyter |
<a href="https://colab.research.google.com/github/Rishit-dagli/AI-Workshop-for-beginners/blob/master/Week%202/Week_2_Lab_1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
##### Copyright 2020 Rishit Dagli
```
#@title Licensed under the Apache Licen... | github_jupyter |
## Movielens
```
%reload_ext autoreload
%autoreload 2
%matplotlib inline
from fastai.learner import *
from fastai.column_data import *
```
Data available from http://files.grouplens.org/datasets/movielens/ml-latest-small.zip
```
path='data/ml-latest-small/'
```
We're working with the movielens data, which contains... | github_jupyter |
```
import torch
import torch.nn.init
from torch.autograd import Variable
import torchvision.utils as utils
import torchvision.datasets as dsets
import torchvision.transforms as transforms
torch.manual_seed(8)
import matplotlib.pyplot as plt
import numpy as np
import random
%matplotlib inline
```
## Loading MNIST data... | github_jupyter |
This notebook checks the correctness of the LDS net implementation
```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import nengo
from kalman import LDS, LDSNet
```
# 1D tests
```
def test_ldsnet_1d():
"""Test LDSNet correctness with
tau xdot = -x + u
"""
SIM_TIME = 1.
... | github_jupyter |
## Version 03 -> Pred RUL
```
!pip install texttable
from platform import python_version
print(python_version())
# importing required libraries
from scipy.io import loadmat
import matplotlib.pyplot as plt
import numpy as np
from pprint import pprint as pp
from sklearn.pipeline import make_pipeline
from sklearn.preproc... | github_jupyter |
```
import hail as hl
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
hl.init()
```
### MAF and Call Rates
```
mt = hl.balding_nichols_model(3, 6, 10)
mt = mt.annotate_entries(GT=hl.case().when(hl.rand_bool(.5, seed=1), mt.GT).or_missing())
mt = hl.variant_qc(mt)
mt.count()
mt.aggregate_entries... | github_jupyter |
# Use Core ML to predict Boston house prices with `ibm-watson-machine-learning`
This notebook demonstrates how to perform regression analysis using scikit-learn and the watson-machine-learning-client package.
Some familiarity with Python is helpful. This notebook is compatible with Python 3.8.
You will use the sampl... | github_jupyter |
# Using Python to Access NEXRAD Level 2 Data from Unidata THREDDS Server
This is a modified version of Ryan May's notebook here:
http://nbviewer.jupyter.org/gist/dopplershift/356f2e14832e9b676207
The TDS provides a mechanism to query for available data files, as well as provides access to the data as native volume fi... | github_jupyter |
# Project CodeNet Language Classification
## Introduction
This notebook takes you through the steps of a simple experiment that shows
how to create and exercise a Keras model to detect the language of a piece of
source code. We will be using TensorFlow as its backend.
For convenience, all the necessary steps will be ... | github_jupyter |
```
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0,1,2,3"
cd /media/datastorage/Phong/svhn_v2
ls
mkdir svhn_v2
!wget http://ufldl.stanford.edu/housenumbers/train_32x32.mat
!wget http://ufldl.stanford.edu/housenumbers/extra_32x32.mat... | github_jupyter |
# Tutorial Part 2: Learning MNIST Digit Classifiers
In the previous tutorial, we learned some basics of how to load data into DeepChem and how to use the basic DeepChem objects to load and manipulate this data. In this tutorial, you'll put the parts together and learn how to train a basic image classification model in... | github_jupyter |
```
import pandas as pd
df = pd.read_csv("../input/train/train.csv")
df_test = pd.read_csv("../input/test/test.csv")
print(len(df), len(df_test))
for a in range(5):
dfs = df[df["AdoptionSpeed"] == a]
for i, text in enumerate(dfs["Description"].values):
if i == 5:
break
print(text) ... | github_jupyter |
# XAI Tutorial 1: Label Tracking
## Overview
In this tutorial, we will discuss the following topics:
* [Label Tracking](#tx01labels)
We'll start by getting the imports out of the way:
```
import tempfile
import fastestimator as fe
from fastestimator.architecture.tensorflow import LeNet
from fastestimator.backend im... | github_jupyter |
## Text generation using tensor2tensor on Cloud ML Engine
This notebook illustrates using the <a href="https://github.com/tensorflow/tensor2tensor">tensor2tensor</a> library to do from-scratch, distributed training of a poetry model. Then, the trained model is used to complete new poems.
<p/>
### Install tensor2tensor... | github_jupyter |
```
%pylab inline
from mpl_toolkits import mplot3d
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import os
import pandas as pd
import numpy as np
from importlib import reload
import sys
sys.path.append('../../code/scripts')
from dataset_params import dataset_params
import utils
import plotti... | github_jupyter |
```
"""
This module consisted of two steps: 1) make planet ARD images and 2) call AFMapTSComposite for making composites
"""
import boto3
import pandas as pd
import gdal
import geopandas as gpd
import osr
from shapely.geometry import mapping
from math import ceil
from datetime import datetime
import os
import click
fr... | github_jupyter |
# Multilayer Perceptron - Old Refactor
## Import dependencies
```
import pickle, gzip, numpy as np
import matplotlib.pyplot as plt
import matplotlib.style as style
style.use('fivethirtyeight')
from IPython.core.display import display, HTML
invert_l = lambda x: 100 - x
discretize_l = lambda x: x / 100
f = gzip.open(... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
import fitsio as ft
import sys
sys.path.append('/home/mehdi/github/LSSutils')
import lssutils.utils as lu
import numpy as np
import healpy as hp
old = ft.read('/home/mehdi/data/dr9v0.57.0/sv3nn_v1/tables/sv3tab_LRG_NBMZLS_256.fits')
new = ft.read('/home/mehdi/data/... | github_jupyter |
# CatBoost and CoreML tutorial — Iris dataset
Get iris dataset:
```
from sklearn import datasets
iris = datasets.load_iris()
```
Train the model:
```
import catboost
cls = catboost.CatBoostClassifier(loss_function='MultiClass')
cls.fit(iris.data, iris.target)
```
Predict probabilities:
```
cls.predict(iris.data[0... | github_jupyter |
# GFL ENVIRONMENTAL 2020 STOCK TREND ANALYSIS
```
import warnings
warnings.filterwarnings('ignore') # Hide warnings
import datetime as dt
import pandas as pd
pd.core.common.is_list_like = pd.api.types.is_list_like
import pandas_datareader.data as web
import numpy as np
import matplotlib.pyplot as plt
import seaborn a... | github_jupyter |
# What can go wrong?
Consider a simple python computational model of chemical reaction networks:
```
class Element:
def __init__(self, symbol, number):
self.symbol = symbol
self.number = number
def __str__(self):
return str(self.symbol)
class Molecule:
def __init__(self, mass):
... | github_jupyter |
# 3D Geometries
## Introduction
In this tutorial we will describe how to create 3 dimensional structures that are based on one or more 2d profiles.
We assume that you already know how to create 2d profiles using the `weldx` package.
If this is not the case, please read the corresponding tutorial first.
You will lear... | github_jupyter |
# NumPy
In this lesson we will learn the basics of numerical analysis using the NumPy package.
<div align="left">
<a href="https://github.com/madewithml/basics/blob/master/notebooks/03_NumPy.ipynb" role="button"><img class="notebook-badge-image" src="https://img.shields.io/static/v1?label=&message=View%20On%20Git... | github_jupyter |
# Introduction to Neural Networks with TensorFlow
In this notebook, you'll get introduced to [TensorFlow](https://www.tensorflow.org/), an open source library to help you develop and train machine learning models.
TensorFlow in a lot of ways behaves like the arrays you love from NumPy. NumPy arrays, after all, are ju... | github_jupyter |
```
import pandas as pd
import fredpy as fp
import numpy as np
```
# US Seigniorage Data
Seigniorage is the real value of the change in the monetary base:
\begin{align}
\frac{M_{t}-M_{t-1}}{P_t} & = \Delta m_t + \frac{\pi_t}{1+\pi_t}m_{t-1}
\end{align}
where $\Delta m_t = m_t - m_{t-1}$ and $\pi_t = P_t/ P_{t-1} - ... | github_jupyter |
# Using packages
## Math
```
import java.lang.Math;
for (int i = 0; i < 5; i++) {
var num = Math.random();
var s = String.format("%.5f", num);
System.out.println(s);
}
```
## Strings
```
import java.lang.StringBuilder;
var entity = "One-Off Coder";
var address = "7526 Old Linton Hall Road";
var city =... | github_jupyter |
### TUTORIAL: Train LDA with OCTIS
Welcome! This is a tutorial that allows you to train a topic model using OCTIS (Optimizing and Comparing Topic Models Is Simple).
A topic model allows you to discover the latent topics in your documents in a comp... | github_jupyter |
<a href="https://colab.research.google.com/github/dlmacedo/starter-academic/blob/master/The_ultimate_guide_to_Encoder_Decoder_Models_1_4.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
%%capture
!pip install -qq git+https://github.com/huggingfac... | github_jupyter |
# Solving the heat equation in PyBaMM
In this notebook we create and solve a model for unsteady heat diffusion in 1D, with a spatially dependent heat source term. The notebook is adapted from example 4.1.2 on pg.16 of the online notes found [here](https://faculty.uca.edu/darrigo/Students/M4315/Fall%202005/sep-var.pdf)... | github_jupyter |
# Self Pix2Pix
<table class="tfo-notebook-buttons" align="left" >
<td>
<a target="_blank" href="https://colab.research.google.com/github/HighCWu/SelfGAN/blob/master/implementations/pix2pix/self_pix2pix.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a>
</td>
<td>... | github_jupyter |
```
from data_loader import load_data
from naive_bayes import BernoulliNaiveBayes
from nlp_processing import LemmaCountVectorizer
from sklearn.pipeline import Pipeline
from sklearn.model_selection import RandomizedSearchCV, GridSearchCV
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from... | github_jupyter |
# Attention - Qutorch
```
import numpy as np
import math, copy, time
import torch.nn as nn
import torch
import torch.nn.functional as F
from torch.autograd import Variable
import matplotlib.pyplot as plt
import seaborn
seaborn.set_context(context="talk")
%matplotlib inline
"""
def smiles2int(drug):
return [VOCAB... | github_jupyter |
Test shotnoise in the contaminated mocks --- small scale clustering changed due to Poisson
```
import fitsio as ft
import numpy as np
import healpy as hp
import os
import sys
%matplotlib inline
import matplotlib.pyplot as plt
class mock(object):
def __init__(self, featsfile, paramsfile, func='lin', sf=1207432.790... | github_jupyter |
```
import numpy as np
import pandas as pd
from scipy.stats import f
import matplotlib.pyplot as plt
df = pd.read_csv('insurance.csv')
df.head()
```
## Test of proportions
* 'sex' and 'smoker' are two categorical variables
* We want to see if the proportion of smokers in the female population is significantly less th... | github_jupyter |
# **New Variable Processing**
`BIOMASS, SFC_CHL, SFC_FED, SFC_IRR`
`BIOMASS` consists of `sfc_ndi`, `sfc_nlg_diatoms`, `sfc_nlg_nondiatoms`, and `sfc_nsm`
```
import os
import warnings
warnings.filterwarnings("ignore", message="invalid value encountered in true_divide")
warnings.filterwarnings("ignore", message="Unab... | github_jupyter |
```
# Copyright 2020 Erik Härkönen. All rights reserved.
# This file is licensed to you under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License. You may obtain a copy
# of the License at http://www.apache.org/licenses/LICENSE-2.0
# Unless required by app... | github_jupyter |
```
import tensorflow as tf
import tsp_env
import numpy as np
import itertools
import Q_function_graph_model
import matplotlib.pyplot as plt
%matplotlib inline
n_cities = 5
T = 4
n_mlp_layers = 0
p = 64
n_dagger_steps = 10; max_steps_per_rollout = 10;
n_rollouts = 50; n_gradient_steps = 20
learning_rate = 1e-2
obs_ph ... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import sys
import os
import argparse
import logging
import shutil
import re
import pickle
from PIL import Image
from skimage import io
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from auto_tqdm import tqdm
import torch
import torch.nn.functional as F
imp... | github_jupyter |
# Path sum: two ways
<div class="problem_content" role="problem">
<p>In the 5 by 5 matrix below, the minimal path sum from the top left to the bottom right, by <b>only moving to the right and down</b>, is indicated in bold red and is equal to 2427.</p>
<div style="text-align:center;">
$$
\begin{pmatrix}
\color{red}{13... | github_jupyter |
# First model: a single hidden layer
```
%matplotlib inline
import os
import time
import numpy as np
import matplotlib
from matplotlib import pyplot
from pandas import DataFrame
from pandas.io.parsers import read_csv
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
from nolearn.l... | github_jupyter |
```
"""
The MIT License (MIT)
Copyright (c) 2021 NVIDIA
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, pub... | github_jupyter |
# Técnicas de Validación
Las técnicas de validación consisten en la búsqueda de los metaparametros que mejor resultados nos retornan, las técnicas de validación comúnmente utilizadas son el **K-fold** y el **Grid search**.
## K-fold
 {
if (code_show){
$('div.input').hide()
} else {
$('div.input').show()
}
code_show = !code_show
}
$( document ).ready(code_toggle);
</script>
Promijeni vidljivost ... | github_jupyter |
# Implementing a Recommender System with SageMaker, MXNet, and Gluon
_**Making Video Recommendations Using Neural Networks and Embeddings**_
---
---
*This work is based on content from the [Cyrus Vahid's 2017 re:Invent Talk](https://github.com/cyrusmvahid/gluontutorials/blob/master/recommendations/MLPMF.ipynb)*
#... | github_jupyter |
```
test_index = 0
```
#### testing
```
from load_data import *
# load_data()
```
## Loading the data
```
from load_data import *
X_train,X_test,y_train,y_test = load_data()
len(X_train),len(y_train)
len(X_test),len(y_test)
```
## Test Modelling
```
import torch
import torch.nn as nn
import torch.optim as optim
i... | github_jupyter |
```
# pytorch
import torch
from torchvision.utils import make_grid
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
# fastmri
import fastmri
from fastmri.data import subsample
from fastmri.data import transforms, mri_data
from fastmri.evaluate import ssim, psnr, nmse
from fastmri.losses imp... | github_jupyter |
```
import sys
sys.path.append("..")
from nerpharma import PharmaEntitiesTagger, DrugEntitiesTagger
pharma_keywords = ["Pharma", "Pharmaceuticals", "Pharmaceutical", "Drugs", "Biotech", "Medical", "World", "Institute", "Health", "Medicare", "Oncology", "Medicine", "Medicines", "Labs", "Healthcare", "Laboratories", "Pha... | github_jupyter |
```
import librosa
import tensorflow as tf
from tensorflow.keras.utils import Sequence
import numpy as np
import os
import glob
from tqdm.auto import tqdm
import IPython.display as ipd
class Signal_Synthesis_Datagen_tf(Sequence):
def __init__(self, noise_dir, signal_dir, signal_nums_save=None, num_noise_samples=... | github_jupyter |
```
import pandas as pd
df = pd.read_csv('car data.csv')
df.head()
df.shape
```
301 rows 9 columns
```
print(df['Seller_Type'].unique()) # all unique values in seller
print(df['Transmission'].unique())
print(df['Owner'].unique())
print(df['Fuel_Type'].unique())
## check missing or null values
df.isnull().sum()
df.de... | github_jupyter |
```
%matplotlib inline
%load_ext autoreload
%autoreload 2
import os
import sys
import copy
import pickle
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from scipy import interpolate
from astropy.table import Table, Column, vstack, join
color_bins = ["#377eb8", "#e41a1c", "#1b9e77", "#... | github_jupyter |
Takes the results from both methods (IRG and U-NEt), introduce on a pandas dataset, saves it on csv and creates a boxplot with the comparison
```
import os
import nibabel as nib
import matplotlib.pyplot as plt
import numpy as np
from nilearn import image
import matplotlib.pyplot as plt
import sys
sys.path.insert(0,'C:... | github_jupyter |
<a href="https://colab.research.google.com/github/bipinKrishnan/fastai_course/blob/master/text_preprocessing.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
from torchvision.datasets.utils import download_and_extract_archive
from torch.utils.dat... | github_jupyter |
```
from IPython.display import Image
```
# Continuous Factors
1. Base Class for Continuous Factors
2. Joint Gaussian Distributions
3. Canonical Factors
4. Linear Gaussian CPD
In many situations, some variables are best modeled as taking values in some continuous space. Examples include variables such as position, v... | github_jupyter |
```
from NADINEmainloop import NADINEmain, NADINEmainId
from NADINEbasic import NADINE
from utilsNADINE import dataLoader, plotPerformance
import random
import torch
import numpy as np
# random seed control
np.random.seed(0)
torch.manual_seed(0)
random.seed(0)
# load data
dataStreams = dataLoader('../dataset/rfid2.mat'... | 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 |
```
import torch.nn as nn
import torch.nn.functional as F
import pandas as pd
import numpy as np
import torch
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import torch.optim as optim
from matplotlib import pyp... | github_jupyter |
# Bayesian Statistics for Physicists: 05 Sampling
## Contents of the BSFP series
<ul>
<li><a href="BSFP_01_Overview_and_setup.ipynb">01 Overview and Setup</a>
<li><a href="BSFP_02_Basics.ipynb">02 Bayesian basics</a>
<li><a href="BSFP_03_Choosing_priors.ipynb">03 Choosing priors</a>
<li><a href="BSFP_... | github_jupyter |
# Fraud_Detection_Using_SMOTE_OVERSAMPLING
I am able to achieve the following accuracies in the validation data. These results can be further improved by reducing the
parameter, number of frauds used to create features from category items. I have used a threshold of 100.
* Logistic Regression :
Validation A... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
plt.rcParams['axes.facecolor'] = 'white'
params = {"text.usetex" : True,
"text.latex.preamble": r"\usepackage{bm}",
"font.size" : 25,
"axes.labelsize": 30,
'axes.labelpad': 0
}
plt.rcParam... | github_jupyter |
```
# Make the following code support python2 and python3
from __future__ import division, print_function, unicode_literals
# Check if the version of python is 3.5 and above
import sys
assert sys.version_info >= (3, 5)
# Check to see if sklearn is version 0.20 and above
import sklearn
assert sklearn.__version__ >= "0... | github_jupyter |
# Tree cover classification using deep learning on AWS SageMaker
This notebook show how to run the [tree cover example](tree-cover-keras.ipynb) on AWS SageMaker.
Please read [these](sagemaker.md) instructions on how to setup AWS SageMaker.
```
import os
import datetime
from os import path as op
import itertools
fro... | github_jupyter |
# ECE 3 : Homework 3
## Instructions
To get started, you should go through the following steps.
- Rename this jupyter notebook by adding your name: e.g. `ECE3_HW3_<your-name>.ipynb`.
- Complete all the exercises by directly editing your notebook.
- Make sure that the coding portions run without errors.
## Problem 1 ... | github_jupyter |
## Exercises
### Forward rates
#### Discrete with specific numbers
###### Question
If the $\,\mathrm{3Y}\,$ bond pays $\,4\%\,$, and the $\,\mathrm{5Y}\,$ bond pays $\,4.5\%\,$, then is the $\,\mathrm{3Y}\to\mathrm{5Y}\,$ forward rate $\,0.5\%\,$?<br/>
`Help:` use the definitions to calculate the forward rate.
####... | github_jupyter |
# Estimating Joint Tour Frequency
This notebook illustrates how to re-estimate a single model component for ActivitySim. This process
includes running ActivitySim in estimation mode to read household travel survey files and write out
the estimation data bundles used in this notebook. To review how to do so, please ... | github_jupyter |
# Task-3
To design a OpenQASM3 interpreter which does the following:
1. Convert the OpenQASM3 code to a Quantum Circuit
2. Calculates the inverse of the circuit
For this task, I first start with writing out a basic tokenizer and line-by-line interpreter. The code for the tokenizer and interpreter is in the `qasm_to_q... | github_jupyter |
## TensorFlow 1.X
### Installing TensorFlow 1.X
```
%tensorflow_version 1.x
import tensorflow as tf
tf.__version__
```
### Constants
#### Defining a constant
```
# Defining a TensorFlow constant
tensor = tf.constant([[23, 4], [32, 51]])
tensor
# If a session is not initialized, we cannot access the values of th... | github_jupyter |
# Estimation Walkthrough
```
from shapely.geometry import Polygon
import numpy as np
%matplotlib inline
import geopandas as gpd
from tobler import area_weighted
from tobler.area_weighted import _area_tables_binning as area_tables
from tobler.area_weighted import area_interpolate
```
## Example: Two GeoDataFrames
```... | github_jupyter |
# Summary
<p class='lead'>
Compute the optical crosstalk in two 48-pixel SPAD arrays from the 48-spot smFRET-PAX setup.
</p>
# Find the data
```
fname = 'data/2017-10-16_00_DCR.hdf5'
fname
from pathlib import Path
assert Path(fname).is_file(), 'File not found.'
mlabel = Path(fname).stem
mlabel
```
# Load software
... | github_jupyter |
# Marginal Likelihood Implementation
The `gp.Marginal` class implements the more common case of GP regression: the observed data are the sum of a GP and Gaussian noise. `gp.Marginal` has a `marginal_likelihood` method, a `conditional` method, and a `predict` method. Given a mean and covariance function, the functio... | github_jupyter |
# Some Variations of Banach's Matchbox Problem
Banach's matchbox problem is a good entry point into stochastic stopping problems. A man buys two matchbooks and puts one in each of his two pockets. He then selects a matchbox at random from either pocket, uses a single match, and then returns the matchbox to the same po... | github_jupyter |
# Агрегирование в pandas
Pandas поддерживает все возможности по агрегированию, которые есть в NumPy. Кроме простых агрегирующих функций в pandas также есть возможность группировки и трансформации данных, которая поволяет выполнять очень сложные вычисления над данными.
Создадим небольшой набор данных для простых пример... | github_jupyter |
## Importing Dependencies
```
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
import sklearn
from datetime import datetime
import pickle
df = pd.read_csv('ipl.csv')
df.head()
```
## Data Cleaning
```
columns_remove=['mid','venue','batsman','bowler','str... | github_jupyter |
# Reporting Conditions and Predicted Capacities
```
import pandas as pd
import matplotlib as plt
from captest import capdata as pvc
```
First we load the NREL data used for testing and set the translation dictionary.
```
meas = pvc.CapData('meas')
meas.load_data(path='../../tests/data/', fname='nrel_data.csv', sourc... | github_jupyter |
# Implementing the Gradient Descent Algorithm
In this lab, we'll implement the basic functions of the Gradient Descent algorithm to find the boundary in a small dataset. First, we'll start with some functions that will help us plot and visualize the data.
```
import matplotlib.pyplot as plt
import numpy as np
import ... | github_jupyter |
# Creation of Line Plot Results Figure
Here we will be creating the figure displaying the skill of the trained models by visualizing true positive, false positive, false negative, and true negative events as a function of number of kernels used to train the model or as a function of lead time. The figure will contain ... | github_jupyter |
# Introduction to Programming with Python
# Unit 3: Conditional Operator
In the last unit, you were asked to solve a quadratic equation of the form
`$$ax^2+bx+c=0$$`.
To solve this equation, we can write the following function (based on the well-known [quadratic formula](https://en.wikipedia.org/wiki/Quadratic_formu... | github_jupyter |
# GluonTS SageMaker SDK Tutorial
***This notebook is meant to be uploaded to a SageMaker notebook instance and executed there. As a kernel choose `conda_mxnet_p36`***
***In this how-to tutorial we will train a SimpleFeedForwardEstimator on the m4_hourly dataset on AWS SageMaker using the GluonTSFramework, and later r... | github_jupyter |
```
import numpy as np
arr = np.random.rand(5,5)
```
### element-wise addition, subtraction, multiplication and division
```
print(arr + 10)
print(arr - 10)
print(arr * 10)
print(arr / 10)
# the above operations can be performed using numpy built-in functions
# which can save memory as the output can be stored in the... | github_jupyter |
# Human numbers
```
from fastai import *
from fastai.text import *
bs=64
```
## Data
```
path = untar_data(URLs.HUMAN_NUMBERS)
path.ls()
def readnums(d): return [', '.join(o.strip() for o in open(path/d).readlines())]
train_txt = readnums('train.txt'); train_txt[0][:80]
valid_txt = readnums('valid.txt'); valid_txt[0... | github_jupyter |
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