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** ----- IMPORTANT ------ **
The code presented here assumes that you're running TensorFlow v1.3.0 or higher, this was not released yet so the easiet way to run this is update your TensorFlow version to TensorFlow's master.
To do that go [here](https://github.com/tensorflow/tensorflow#installation) and then exec... | github_jupyter |
```
from autoreduce import *
import numpy as np
from sympy import symbols
# Post conservation law and other approximations phenomenological model at the RNA level
n = 4 # Number of states
nouts = 2 # Number of outputs
# Inputs by user
x_init = np.zeros(n)
n = 4 # Number of states
timepoints_ode = np.linspace(0, 100... | github_jupyter |
# Understanding the FFT Algorithm
Copy from http://jakevdp.github.io/blog/2013/08/28/understanding-the-fft/
*This notebook first appeared as a post by Jake Vanderplas on [Pythonic Perambulations](http://jakevdp.github.io/blog/2013/08/28/understanding-the-fft/). The notebook content is BSD-licensed.*
<!-- PELICAN_BEG... | github_jupyter |
```
# default_exp label
```
# Label
> A collection of functions to do label-based quantification
```
#hide
from nbdev.showdoc import *
```
## Label search
The label search is implemented based on the compare_frags from the search.
We have a fixed number of reporter channels and check if we find a respective peak ... | github_jupyter |
```
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
import seaborn as sns
sns.set_palette('Set2')
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.metrics import confusion_matrix, mean_squared_error
from sklearn.preprocessing import LabelEncoder, MinMaxScaler, ... | github_jupyter |
# Numpy
### GitHub repository: https://github.com/jorgemauricio/curso_itesm
### Instructor: Jorge Mauricio
```
# librerías
import numpy as np
```
# Crear Numpy Arrays
## De una lista de python
Creamos el arreglo directamente de una lista o listas de python
```
my_list = [1,2,3]
my_list
np.array(my_list)
my_matrix ... | github_jupyter |
```
import os
import numpy as np
import pandas as pd
import glob
from prediction_utils.util import yaml_read, df_dict_concat
table_path = '../figures/hyperparameters/'
os.makedirs(table_path, exist_ok = True)
param_grid_base = {
"lr": [1e-3, 1e-4, 1e-5],
"batch_size": [128, 256, 512],
"drop_prob... | github_jupyter |
# 时间序列预测
时间序列是随着时间的推移定期收集的数据。时间序列预测是指根据历史数据预测未来数据点的任务。时间序列预测用途很广泛，包括天气预报、零售和销量预测、股市预测，以及行为预测（例如预测一天的车流量）。时间序列数据有很多，识别此类数据中的模式是很活跃的机器学习研究领域。
<img src='notebook_ims/time_series_examples.png' width=80% />
在此 notebook 中，我们将学习寻找时间规律的一种方法，即使用 SageMaker 的监督式学习模型 [DeepAR](https://docs.aws.amazon.com/sagemaker/latest/dg/deep... | github_jupyter |
### Netflix Scrapper
The purpose of the code is to get details of all the Categories on Netflix and then to gather information about Sub-Categories and movies under each Sub-Category.
```
from bs4 import BeautifulSoup
import requests
import pandas as pd
import numpy as np
def make_soup(url):
return BeautifulSoup(... | github_jupyter |
# Explain Attacking BERT models using CAptum
Captum is a PyTorch library to explain neural networks
Here we show a minimal example using Captum to explain BERT models from TextAttack
[](https://colab.research.google.com/github/QData/TextAttack/... | github_jupyter |
```
import pandas as pd
import datetime
import vk_api
import os
import requests
import json
import random
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import sys
token = '4e6e771d37dbcbcfcc3b53d291a274d3ae21560a2e81f058a7c177aff044b5141941e89aff1fead50be4f'
vk_session = vk_api.VkApi(token=t... | github_jupyter |
# Software Analytics Mini Tutorial Part I: Jupyter Notebook and Python basics
## Introduction
This series of notebooks are a simple mini tutorial to introduce you to the basic functionality of Jupyter, Python, pandas and matplotlib. The comprehensive explanations should guide you to be able to analyze software data on... | github_jupyter |
```
## plot the histogram showing the modeled and labeled result
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
# for loop version
def read_comp(file):
Pwave = {}
Pwave['correct'] = []
Pwave['wrongphase'] = []
Pwave['miss'] = 0
Pwave['multiphase'] = [] ... | github_jupyter |
```
%matplotlib inline
```
torchaudio Tutorial
===================
PyTorch is an open source deep learning platform that provides a
seamless path from research prototyping to production deployment with
GPU support.
Significant effort in solving machine learning problems goes into data
preparation. ``torchaudio`` le... | github_jupyter |
# EEP/IAS 118 - Section 6
## Fixed Effects Regression
### August 1, 2019
Today we will practice with fixed effects regressions in __R__. We have two different ways to estimate the model, and we will see how to do both and the situations in which we might favor one versus the other.
Let's give this a try using the d... | github_jupyter |
# SVM Classification Using Individual Replicas
This notebook analyzes the quality of the classifiers resulting from training on individual replicas of read counts rather than averaged values. Data are adjusted for library size and gene length.
Training data
1. Uses individual replicas (not averaged)
1. Uses all genes
... | github_jupyter |
# Inference in Google Earth Engine + Colab
> Scaling up machine learning with GEE and Google Colab.
- toc: true
- badges: true
- author: Drew Bollinger
- comments: false
- hide: false
- sticky_rank: 11
# Inference in Google Earth Engine + Colab
Here we demonstrate how to take a trained model and apply to to imagery... | 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>From Understanding to Preparation</font></h1>
## Introduction
In this lab, we will continue learning about the data science methodology, and focus ... | github_jupyter |
# Autoencoder (Semi-supervised)
```
%load_ext autoreload
%autoreload 2
# Seed value
# Apparently you may use different seed values at each stage
seed_value= 0
# 1. Set the `PYTHONHASHSEED` environment variable at a fixed value
import os
os.environ['PYTHONHASHSEED']=str(seed_value)
# 2. Set the `python` built-in pseu... | github_jupyter |
# Facial Keypoint Detection
This project will be all about defining and training a convolutional neural network to perform facial keypoint detection, and using computer vision techniques to transform images of faces. The first step in any challenge like this will be to load and visualize the data you'll be working ... | github_jupyter |
```
import os
import pickle
import re
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sn
import numpy as np
import re
import xgboost as xgb
import shap
from sklearn import ensemble
from sklearn import dummy
from sklearn import linear_model
from sklearn import svm
from sklearn... | github_jupyter |
# 2.4 Deep Taylor Decomposition Part 2.
## Tensorflow Walkthrough
### 1. Import Dependencies
I made a custom `Taylor` class for Deep Taylor Decomposition. If you are interested in the details, check out `models_3_2.py` in the models directory.
```
import os
import re
from tensorflow.examples.tutorials.mnist import... | github_jupyter |
# Initialization
Welcome to the first assignment of "Improving Deep Neural Networks".
Training your neural network requires specifying an initial value of the weights. A well chosen initialization method will help learning.
If you completed the previous course of this specialization, you probably followed our ins... | github_jupyter |
```
# The usual preamble
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
# Make the graphs a bit prettier, and bigger
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (15, 5)
plt.rcParams['font.family'] = 'sans-serif'
# This is necessary to show lots of columns in pa... | github_jupyter |
```
import boto3
import sagemaker
import time
import pandas as pd
import numpy as np
role = sagemaker.get_execution_role()
region = boto3.Session().region_name
sagemaker_session = sagemaker.Session()
bucket_name = sagemaker_session.default_bucket()
prefix = 'endtoendmlsm'
print(region)
print(role)
print(bucket_name)
... | github_jupyter |
---
title: "Create empty feature groups for Online Feature Store"
date: 2021-04-25
type: technical_note
draft: false
---
```
import json
from pyspark.sql.types import StructField, StructType, StringType, DoubleType, TimestampType, LongType, IntegerType
```
# Create empty feature groups
In this demo example we are ex... | github_jupyter |
# Freesurfer space to native space using `mri_vol2vol`
BMED360-2021: `freesurfer-to-native-space.ipynb`
```
%matplotlib inline
import os
import pathlib
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import nibabel as nib
from os.path import expanduser, join, basename, split
import sys
sys.path... | github_jupyter |
# Tokenizers
```
! pipenv install nltk
import nltk
from nltk import tokenize
s1 = """Why wase time say lot word when few word do trick?"""
s2 = """Hickory dickory dock, the mouse ran up the clock."""
from nltk.tokenize import word_tokenize
! df -h /home/christangrant/nltk_data
# nltk.download('punkt') # Download the m... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
from IPython.display import Markdown, display
def printmd(string):
display(Markdown(string))
def colorize(string,color="red"):
return f"<span style=\"color:{color}\">{string}</span>"
```
# Problem description
### Subtask2: Detecting antecedent and consequence
Indi... | github_jupyter |
```
import numpy as np
import scipy.io as sio
from sklearn import svm
from sklearn.model_selection import cross_val_score
from sklearn.metrics.pairwise import pairwise_distances
from sklearn import manifold
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.svm import SVC
from graph_kern... | github_jupyter |
# Phase 2 Review
```
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from statsmodels.formula.api import ols
pd.set_option('display.max_columns', 100)
```
### Check Your Data … Quickly
The first thing you want to do when you get a new dataset, is to quickly to verify the conte... | github_jupyter |
# Stirlingの公式（対数近似）
* $\log n! \sim n\log n - n$
* $n!$はおおよそ$\left(\frac{n}{e}\right)^n$になる
* 参考: [スターリングの公式（対数近似）の導出](https://starpentagon.net/analytics/stirling_log_formula/)
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
```
## $\log n!$の上からの評価
```
MIN_X = 0.5
MAX_X = 10
x = np.linsp... | github_jupyter |
# Part 1: Extracting a Journal's Publications+Researchers Datasets
In this notebook we are going to
* extract all publications data for a given journal
* have a quick look at the publications' authors and affiliations
* review how many authors have been disambiguated with a Dimensions Researcher ID
* produce a data... | github_jupyter |
Your name here.
Your section number here.
# Workshop 1: Python basics, and a little plotting
**Submit this notebook to bCourses to receive a grade for this Workshop.**
Please complete workshop activities in code cells in this iPython notebook. The activities titled **Practice** are purely for you to explore Python... | github_jupyter |
## Recursive Functions
A recursive function is a function that makes calls to itself. It works like the loops we described before, but sometimes it the situation is better to use recursion than loops.
Every recursive function has two components: a base case and a recursive step. The base case is usually the smallest ... | github_jupyter |
```
from astropy.constants import G
import astropy.coordinates as coord
import astropy.table as at
import astropy.units as u
from astropy.time import Time
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
from gala.units import galactic, UnitSystem
from twobody import TwoBo... | github_jupyter |
Peakcalling Bam Stats and Filtering Report - Insert Sizes
================================================================
This notebook is for the analysis of outputs from the peakcalling pipeline
There are severals stats that you want collected and graphed (topics covered in this notebook in bold).
These are:
... | github_jupyter |
```
import keras
from keras.models import Sequential, Model, load_model
from keras.layers import Dense, Dropout, Flatten, Input, Lambda, Concatenate
from keras.layers import Conv1D, MaxPooling1D
from keras.callbacks import ModelCheckpoint, EarlyStopping
from keras import backend as K
import keras.losses
import tensorf... | github_jupyter |
# Working with MODFLOW-NWT v 1.1 option blocks
In MODFLOW-NWT an option block is present for the WEL file, UZF file, and SFR file. This block takes keyword arguments that are supplied in an option line in other versions of MODFLOW.
The `OptionBlock` class was created to provide combatibility with the MODFLOW-NWT opt... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Matplotlib" data-toc-modified-id="Matplotlib-1"><span class="toc-item-num">1 </span>Matplotlib</a></span><ul class="toc-item"><li><span><a href="#Customization" data-toc-modified-id="Customizatio... | github_jupyter |
```
import seaborn as sns
import pandas as pd
import numpy as np
import altair as alt
from markdown import markdown
from IPython.display import Markdown
from ipywidgets.widgets import HTML, Tab
from ipywidgets import widgets
from datetime import timedelta
from matplotlib import pyplot as plt
import os.path as op
from ... | github_jupyter |
# Introduction to Logistic Regression
## Learning Objectives
1. Create Seaborn plots for Exploratory Data Analysis
2. Train a Logistic Regression Model using Scikit-Learn
## Introduction
This lab is an introduction to logistic regression using Python and Scikit-Learn. This lab serves as a foundation for more... | github_jupyter |
# Generative Adversarial Network
In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits!
GANs were [first reported on](https://arxiv.org/abs/1406.2661) in 2014 from Ian Goodfellow and others in Yoshua Bengio'... | github_jupyter |
```
"""
We use following lines because we are running on Google Colab
If you are running notebook on a local computer, you don't need this cell
"""
from google.colab import drive
drive.mount('/content/gdrive')
import os
os.chdir('/content/gdrive/My Drive/finch/tensorflow1/free_chat/chinese/main')
%tensorflow_version 1.... | github_jupyter |
# PyIndMach012: an example of user-model using DSS Python
This example runs a modified example from the OpenDSS distribution for the induction machine model with a sample PyIndMach012 implementation, written in Python, and the original, built-in IndMach012.
Check the `PyIndMach012.py` file for more comments. Comparin... | github_jupyter |
# A Two-Level, Six-Factor Full Factorial Design
<br />
<br />
<br />
### Table of Contents
* [Introduction](#intro)
* Factorial Experimental Design:
* [Two-Level Six-Factor Full Factorial Design](#fullfactorial)
* [Variables and Variable Labels](#varlabels)
* [Computing Main and Interaction Effects](#computing_ef... | github_jupyter |
<a href="https://colab.research.google.com/github/NikolaZubic/AppliedGameTheoryHomeworkSolutions/blob/main/domaci3.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# TREĆI DOMAĆI ZADATAK iz predmeta "Primenjena teorija igara" (Applied Game Theory)
R... | github_jupyter |
```
# Copyright 2021 NVIDIA Corporation. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | github_jupyter |
```
import sys
sys.path.append('..') # for import src
import os
import cloudpickle
import lzma
import pandas as pd
import numpy as np
from sklearn.linear_model import Ridge
from sklearn.model_selection import cross_val_predict
from scipy.stats import pearsonr
import matplotlib.pyplot as plt
import lightgbm as lgb
impo... | github_jupyter |
# Packages
```
#!/usr/bin/env python
# coding: utf-8
import requests
import numpy as np
import json
import os
import time as tm
import pandas as pd
import http.client
import io
import boto3
import zipfile
from threading import Thread
import logging
from datetime import datetime
import time
from operator import itemget... | github_jupyter |
# DJL BERT Inference Demo
## Introduction
In this tutorial, you walk through running inference using DJL on a [BERT](https://towardsdatascience.com/bert-explained-state-of-the-art-language-model-for-nlp-f8b21a9b6270) QA model trained with MXNet.
You can provide a question and a paragraph containing the answer to the... | github_jupyter |
# Run Train of Bubble-Agent (DQN)
- Team: TToBoT
- Member: { Sejun, Steve, Victor } @kaist
## Objective
- run training simultaneously w/ notebook
- to compare the performance of traing
## For Competition
1. prepare the final trained IQN Model (checkpoint w/ 100 iteration)
2. need to customize of env.step()
- ... | github_jupyter |
```
import sys, os; sys.path.append('..')
import pyzx as zx
import random
import math
from fractions import Fraction
%config InlineBackend.figure_format = 'svg'
c = zx.qasm("""
qreg q[3];
cx q[0], q[1];
""")
zx.d3.draw(c)
c = zx.qasm("""
qreg q[2];
rx(0.5*pi) q[1];
t q[0];
cx q[0], q[1];
cx q[1], q[0];
cx q[0], q[1];
t... | github_jupyter |
# PDOS data analysis and plotting
---
### Import Modules
```
import os
print(os.getcwd())
import sys
import plotly.graph_objs as go
import matplotlib.pyplot as plt
from scipy import stats
# #########################################################
from methods import get_df_features_targets
from proj_data import ... | github_jupyter |
### Basic Functions for Interactively Exploring the CORTX Metrics Stored in Pickles
```
%cd /home/johnbent/cortx/metrics
import cortx_community
import cortx_graphing
import os
from github import Github
gh = Github(os.environ.get('GH_OATH'))
stx = gh.get_organization('Seagate')
repos = cortx_community.get_repos()
ps = ... | github_jupyter |
# Furniture Rearrangement - How to setup a new interaction task in Habitat-Lab
This tutorial demonstrates how to setup a new task in Habitat that utilizes interaction capabilities in Habitat Simulator.
## Task Definition:
The working example... | github_jupyter |
# Language Translation
In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French.
## Get the Data
Since translating the whole lan... | github_jupyter |
<a href="https://colab.research.google.com/github/yohanesnuwara/machine-learning/blob/master/06_simple_linear_regression/simple_linear_reg_algorithm.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# **Simple Linear Regression**
```
import numpy as ... | github_jupyter |
```
import matplotlib.pyplot as plt
%matplotlib inline
import pickle
import numpy as np
from scipy.spatial.distance import pdist, squareform
with open('exp_features.p', 'rb') as f:
data = pickle.load(f)
```
## visualize
```
def get_continuous_quantile(x, y, n_interval=100, q=1):
"""
Take continuous x and... | github_jupyter |
```
print("Hello world!")
a=10
a
b=5
b
#addition demo
sum=a+b
print("the sum of a and b is:",sum)
x=2**3
x
y=5/2
y
y=5//2
y
input("Enter some variable")
a=int(input("enter the first number"))
b=int(input("enter the second number"))
int("The sum of first number and second number is:",a+b)
int("The difference of the fi... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from IPython.core.display import display, HTML
plt.style.use('fivethirtyeight')
plt.rc('figure', figsize=(5.0, 2.0))
pokemon=pd.read_csv("../dataset/pokemon.csv")
# Which pokémon is the most difficult to catch?
pokemon['capture_rate']=pd.to_num... | github_jupyter |
```
import random
import gym
#import math
import numpy as np
from collections import deque
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten
from tensorflow.keras.optimizers import Adam
EPOCHS = 1000
THRESHOLD = 10
MONITOR = T... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import glob
import sys
import argparse as argp
change_50_dat = pd.read_csv('/Users/leg2015/workspace/Aagos/Data/Mut_Treat_Change_50_CleanedDataStatFit.csv', index_col="update", float_precision="high")
change_0_dat = pd.read... | github_jupyter |
# Deploy model
**Important**: Change the kernel to *PROJECT_NAME local*. You can do this from the *Kernel* menu under *Change kernel*. You cannot deploy the model using the *PROJECT_NAME docker* kernel.
```
from azureml.api.schema.dataTypes import DataTypes
from azureml.api.schema.sampleDefinition import SampleDefinit... | github_jupyter |
# DiFuMo (Dictionaries of Functional Modes)
<div class="alert alert-block alert-danger">
<b>NEW:</b> New in release 0.7.1
</div>
## Outline
- <a href="#descr">Description</a>
- <a href="#howto">Description</a>
- <a href="#closer">Coser look on the object</a>
- <a href="#visualize">Visualize</a>
<span id="descr"></s... | github_jupyter |
# Tutorial 06: Networks from OpenStreetMap
In this tutorial, we discuss how networks that have been imported from OpenStreetMap can be integrated and run in Flow. This will all be presented via the Bay Bridge network, seen in the figure below. Networks from OpenStreetMap are commonly used in many traffic simulators fo... | github_jupyter |
<table width="100%"> <tr>
<td style="background-color:#ffffff;">
<a href="http://qworld.lu.lv" target="_blank"><img src="../images/qworld.jpg" width="35%" align="left"> </a></td>
<td style="background-color:#ffffff;vertical-align:bottom;text-align:right;">
prepared by Abuzer Yak... | github_jupyter |
```
# Execute this code block to install dependencies when running on colab
try:
import torch
except:
from os.path import exists
from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag
platform = '{}{}-{}'.format(get_abbr_impl(), get_impl_ver(), get_abi_tag())
cuda_output = !ldconfig -... | github_jupyter |
```
from crystal_toolkit.helpers.layouts import Columns, Column
from crystal_toolkit.settings import SETTINGS
from jupyter_dash import JupyterDash
from pydefect.analyzer.calc_results import CalcResults
from pydefect.analyzer.dash_components.cpd_energy_dash import CpdEnergy2D3DComponent, CpdEnergyOtherComponent
from pyd... | github_jupyter |
# V2: SCF optimization with VAMPyR
## V2.1: Hydrogen atom
In order to solve the one-electron Schr\"{o}dinger equation in MWs we reformulate them in an integral form [1].
\begin{equation}
\phi = -2\hat{G}_{\mu}\hat{V}\phi
\end{equation}
Where $\hat{V}$ is the potential acting on the system, $\phi$ is the wavefuncti... | 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 |
# Módulo 2 - Modelos preditivos e séries temporais
# Desafio do Módulo 2
```
import pandas as pd
import numpy as np
base = pd.read_csv('https://pycourse.s3.amazonaws.com/banknote_authentication.txt', header=None)
base.head()
#labels:
#variance, skewness, curtosis e entropy)
base.columns=['variance', 'skewness', 'curt... | github_jupyter |
<img src="../../images/banners/python-advanced.png" width="600"/>
# <img src="../../images/logos/python.png" width="23"/> Python's property(): Add Managed Attributes to Your Classes
## <img src="../../images/logos/toc.png" width="20"/> Table of Contents
* [Managing Attributes in Your Classes](#managing_attributes_in... | github_jupyter |
# iMCSpec (iSpec+emcee)
iMCSpec is a tool which combines iSpec(https://www.blancocuaresma.com/s/iSpec) and emcee(https://emcee.readthedocs.io/en/stable/) into a single unit to perform Bayesian analysis of spectroscopic data to estimate stellar parameters. For more details on the individual code please refer to the lin... | github_jupyter |
<a href="https://colab.research.google.com/github/ryanleeallred/DS-Unit-1-Sprint-1-Dealing-With-Data/blob/master/module2-loadingdata/LS_DS_112_Loading_Data_Assignment.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Practice Loading Datasets
This ... | github_jupyter |
```
###################################
# Test cell, pyechonest - IO HAVOC
###################################
import os
import sys
sys.path.append(os.environ["HOME"] + "/github/pyechonest")
import pyechonest.track as track
import pyechonest.artist as artist
import pyechonest.util as util
import pyechonest.song as son... | github_jupyter |
[@LorenaABarba](https://twitter.com/LorenaABarba)
12 steps to Navier–Stokes
=====
***
Did you experiment in Steps [1](./01_Step_1.ipynb) and [2](./02_Step_2.ipynb) using different parameter choices? If you did, you probably ran into some unexpected behavior. Did your solution ever blow up? (In my experience, CFD stud... | github_jupyter |
# Likelihood for Retro
To calculate the likelihood of a hypothesis $H$ given observed data $\boldsymbol{k}$, we construct the extended likelihood given as:
$$\large L(H|\boldsymbol{k}) = \prod_{i\in\text{DOMs}} \frac{\lambda_i^{k_i}} {k_i!} e^{-\lambda_i} \prod_{j\in\text{hits}}p^j(t_j|H)^{k_j}$$
where:
* $\lambda_i... | github_jupyter |
```
import numpy as np
import pandas as pd
from grn_learn.viz import set_plotting_style
import seaborn as sns
import matplotlib.pyplot as plt
from grn_learn import download_and_preprocess_data
from grn_learn import annot_data_trn
from grn_learn import train_keras_multilabel_nn
from sklearn.model_selection import St... | github_jupyter |
# Batch Normalization – Lesson
1. [What is it?](#theory)
2. [What are it's benefits?](#benefits)
3. [How do we add it to a network?](#implementation_1)
4. [Let's see it work!](#demos)
5. [What are you hiding?](#implementation_2)
# What is Batch Normalization?<a id='theory'></a>
Batch normalization was introduced in ... | github_jupyter |
```
import logging
import os
import math
from dataclasses import dataclass, field
import copy # for deep copy
import torch
from torch import nn
from transformers import RobertaForMaskedLM, RobertaTokenizerFast, TextDataset, DataCollatorForLanguageModeling, Trainer
from transformers import TrainingArguments, HfArgumen... | github_jupyter |
# Numerical norm bounds for quadrotor
For a quadrotor system with state $x = \begin{bmatrix}p_x & p_z & \phi & v_x & v_z & \dot{\phi} \end{bmatrix}^T$ we have
\begin{equation}
\dot{x} = \begin{bmatrix}
v_x \cos\phi - v_z\sin\phi \\
v_x \sin\phi + v_z\cos\phi \\
\dot{\phi} \\
v_z\dot{\phi} - g\sin{\phi} \\
-v_x\dot{... | github_jupyter |
# Expectiminimax
Der Vollständigkeits halber der ganze Expectiminimax Algorithmus. <br>
Während 1-ply, 2-ply und 3-ply nur den ersten, die ersten beiden, bzw. ersten drei Schritte von Expectiminimax ausgeführt haben, kann man alle mit dem Expectiminmax Algorithmus zusammenfassen. Das erlaubt einem eine saubere Notatio... | github_jupyter |
```
import os, json
from pathlib import Path
from pandas import DataFrame
from mpcontribs.client import Client
from unflatten import unflatten
client = Client()
```
**Load raw data**
```
name = "screening_inorganic_pv"
indir = Path("/Users/patrick/gitrepos/mp/mpcontribs-data/ThinFilmPV")
files = {
"summary": "SUM... | github_jupyter |
```
import torch
from torch import nn
from torch import optim
from torchvision.datasets import MNIST
from torch.utils.data import TensorDataset, Dataset, DataLoader
from tqdm.notebook import tqdm
import numpy as np
from aijack.defense import VIB, KL_between_normals, mib_loss
dim_z = 256
beta = 1e-3
batch_size = 100
sa... | github_jupyter |
## 1-2. 量子ビットに対する基本演算
量子ビットについて理解が深まったところで、次に量子ビットに対する演算がどのように表されるかについて見ていこう。
これには、量子力学の性質が深く関わっている。
1. 線型性：
詳しくは第4章で学ぶのだが、量子力学では状態（量子ビット）の時間変化はつねに（状態の重ね合わせに対して）線型になっている。つまり、**量子コンピュータ上で許された操作は状態ベクトルに対する線型変換**ということになる
。１つの量子ビットの量子状態は規格化された２次元複素ベクトルとして表現されるのだったから、
１つの量子ビットに対する操作＝線型演算は$2 \times 2$の**複素行列**によって表現される。... | github_jupyter |
```
%matplotlib inline
from __future__ import absolute_import
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(1337) # for reproducibility
from theano import function
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core imp... | github_jupyter |
```
import os
import numpy as np
import pandas as pd
import jinja2 as jj
def mklbl(prefix, n):
return ["%s%s" % (prefix, i) for i in range(n)]
miindex = pd.MultiIndex.from_product([mklbl('A', 4),
mklbl('B', 2),
mklbl('C', 4),
... | github_jupyter |
# Figure 3: Cluster-level consumptions
This notebook generates individual panels of Figure 3 in "Combining satellite imagery and machine learning to predict poverty".
```
from fig_utils import *
import matplotlib.pyplot as plt
import time
%matplotlib inline
```
## Predicting consumption expeditures
The parameters ... | github_jupyter |
<div align="center">
<h1><strong>Herencia</strong></h1>
<strong>Hecho por:</strong> Juan David Argüello Plata
</div>
## __Introducción__
<div align="justify">
La relación de herencia facilita la reutilización de código brindando una base de programación para el desarrollo de nuevas clases.
</div>
## __1. Sup... | github_jupyter |
# 0.0 Notebook Template
--*Set the notebook number, describe the background of the project, the nature of the data, and what analyses will be performed.*--
## Jupyter Extensions
Load [watermark](https://github.com/rasbt/watermark) to see the state of the machine and environment that's running the notebook. To make s... | github_jupyter |
```
# import lib
# ===========================================================
import csv
import pandas as pd
from datascience import *
import numpy as np
import random
import time
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('fivethirtyeight')
import collections
import math
import sys
from tqdm imp... | github_jupyter |
<center><h1>Improved Graph Laplacian via Geometric Self-Consistency</h1></center>
<center>Yu-Chia Chen, Dominique Perrault-Joncas, Marina Meilă, James McQueen. University of Washington</center> <br>
<center>Original paper: <a href=https://nips.cc/Conferences/2017/Schedule?showEvent=9223>Improved Graph Laplacian via Ge... | github_jupyter |
##### Copyright 2019 Google LLC
```
#@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 agreed to in ... | github_jupyter |
# Road Following - Live demo
In this notebook, we will use model we trained to move jetBot smoothly on track.
### Load Trained Model
We will assume that you have already downloaded ``best_steering_model_xy.pth`` to work station as instructed in "train_model.ipynb" notebook. Now, you should upload model file to JetBo... | 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 |
# 광학 인식
흔히 볼 수 있는 Computer Vision 과제는 이미지에서 텍스트를 감지하고 해석하는 것입니다. 이러한 종류의 처리를 종종 *OCR(광학 인식)*이라고 합니다.
## Computer Vision 서비스를 사용하여 이미지에서 텍스트 읽기
**Computer Vision** Cognitive Service는 다음을 비롯한 OCR 작업을 지원합니다.
- 여러 언어로 된 텍스트를 읽는 데 사용할 수 있는 **OCR** API. 이 API는 동기식으로 사용할 수 있으며,... | github_jupyter |
# The Atoms of Computation
Programming a quantum computer is now something that anyone can do in the comfort of their own home.
But what to create? What is a quantum program anyway? In fact, what is a quantum computer?
These questions can be answered by making comparisons to standard digital computers. Unfortuna... | github_jupyter |
!wget https://www.dropbox.com/s/ic9ym6ckxq2lo6v/Dataset_Signature_Final.zip
#!wget https://www.dropbox.com/s/0n2gxitm2tzxr1n/lightCNN_51_checkpoint.pth
#!wget https://www.dropbox.com/s/9yd1yik7u7u3mse/light_cnn.py
import zipfile
sigtrain = zipfile.ZipFile('Dataset_Signature_Final.zip', mode='r')
sigtrain.extractall()
... | github_jupyter |
# Miscellaneous
This section describes the organization of classes, methods, and functions in the ``finite_algebra`` module, by way of describing the algebraic entities they represent. So, if we let $A \rightarrow B$ denote "A is a superclass of B", then the class hierarchy of algebraic structures in ``finite_algebra... | github_jupyter |
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