code stringlengths 2.5k 150k | kind stringclasses 1
value |
|---|---|
## Die Navier-Stokes-Gleichungen
Die Navier-Stokes-Gleichungen sind die Grundgleichungen zur Berechnung reibungsbehafteter Strömungen. Obwohl mit dem Begriff im strengen Sinne nur die Impulsgleichung gemeint ist, wird damit in der numerischen Strömungsmechanik gleich der ganze Gleichungssatz aus Kontinuitäts-, Impuls-... | github_jupyter |
# Ensemble Learning
## Initial Imports
```
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
from pathlib import Path
from collections import Counter
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import... | github_jupyter |
# Using AI planning to explore data science pipelines
```
from __future__ import print_function
import sys
import os
import types
sys.path.append(os.path.abspath(os.path.join(os.getcwd(), "../grammar2lale")))
# Clean output directory where we store planning and result files
os.system('rm -rf ../output')
os.system('m... | github_jupyter |
This Jupyter notebook details theoretically the architecture and the mechanism of the Convolutional Neural Network (ConvNet) step by step. Then, we implement the CNN code for multi-class classification task using pytorch. <br>
The notebook was implemented by <i>Nada Chaari</i>, PhD student at Istanbul Technical Univer... | github_jupyter |
```
# import requests
# with open("mynote.ipynb", "w") as f:
# f.write(requests.get("https://raw.githubusercontent.com/polyrand/teach/master/05_testing/testing.ipynb").text)
# !pip install ipytest
import warnings
warnings.filterwarnings("ignore")
i... | github_jupyter |
# Read in catalog information from a text file and plot some parameters
## Authors
Adrian Price-Whelan, Kelle Cruz, Stephanie T. Douglas
## Learning Goals
* Read an ASCII file using `astropy.io`
* Convert between representations of coordinate components using `astropy.coordinates` (hours to degrees)
* Make a spherica... | github_jupyter |
#Feature Engineering Notebook
Data given was in raw format and it needed to be converted into format which model could make sense. We considered data of 150K users while modelling. <br>
A data frame **final_df** was created with all the features.<br>
A dataframe **uninstall_unique** was created which had data of the us... | github_jupyter |
```
import os
import cv2
import numpy as np
#import layers
import matplotlib.pyplot as plt
# credits to https://towardsdatascience.com/lines-detection-with-hough-transform-84020b3b1549
import matplotlib.lines as mlines
# ist a,b == m, c
def line_detection_non_vectorized(image, edge_image, num_rhos=100, num_thetas=10... | github_jupyter |
# Activity 02
```
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from keras.models import Sequential
from keras.layers import Dense
from tensorflow import random
import matplotlib.pyplot as plt
import matplotlib
%matplotlib ... | github_jupyter |
# Autoregressions
This notebook introduces autoregression modeling using the `AutoReg` model. It also covers aspects of `ar_select_order` assists in selecting models that minimize an information criteria such as the AIC.
An autoregressive model has dynamics given by
$$ y_t = \delta + \phi_1 y_{t-1} + \ldots + \phi_... | github_jupyter |
```
import os
import pandas as pd
import math
import nltk
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
import re
from nltk.tokenize import WordPunctTokenizer
import pickle
def load_csv_as_df(file_name, sub_directories, col_name=None):
'''
Load any csv as a pandas dat... | github_jupyter |
# Deploy the Model
The pipeline that was executed created a Model Package version within the specified Model Package Group. Of particular note, the registration of the model/creation of the Model Package was done so with approval status as `PendingManualApproval`.
As part of SageMaker Pipelines, data scientists can r... | github_jupyter |
# Example: CanvasXpress bubble Chart No. 4
This example page demonstrates how to, using the Python package, create a chart that matches the CanvasXpress online example located at:
https://www.canvasxpress.org/examples/bubble-4.html
This example is generated using the reproducible JSON obtained from the above page an... | github_jupyter |
## Imports
```
import os
import sys
%env CUDA_VISIBLE_DEVICES=0
%matplotlib inline
import pickle
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from matplotlib.ticker import FormatStrFormatter
import tensorflow as tf
root_path = os.path.dirname(os.path.dirn... | github_jupyter |
# Data description & Problem statement:
This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constr... | github_jupyter |
# Simple Hashing and Collisions
This is a very simple example of hashing based on the modulo function and neglecting the issue of collisions mentioned in the lecture.
## Introduction
Good hashing approaches are available in Python for the *Dictionary* data type. However here is a demonstration of a simple hashing fu... | github_jupyter |
```
# Select TensorFlow 2.0 environment (works only on Colab)
%tensorflow_version 2.x
# Install wandb (ignore if already done)
!pip install wandb
# Authorize wandb
!wandb login
# Imports
from tensorflow.keras.models import *
from tensorflow.keras.layers import *
from wandb.keras import WandbCallback
import tensorflow a... | github_jupyter |
# Integrate 3rd party transforms into MONAI program
This tutorial shows how to integrate 3rd party transforms into MONAI program.
Mainly shows transforms from `BatchGenerator`, `TorchIO`, `Rising` and `ITK`.
```
! pip install batchgenerators==0.20.1
! pip install torchio==0.16.21
! pip install rising==0.2.0
! pip i... | github_jupyter |
```
import matplotlib,aplpy
from astropy.io import fits
from general_functions import *
import matplotlib.pyplot as plt
font = {'size' : 14, 'family' : 'serif', 'serif' : 'cm'}
plt.rc('font', **font)
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['lines.linewidth'] = 1
plt.rcParams['axes.linewidth'] = 1... | github_jupyter |
```
import matplotlib.pyplot as plt
import numpy as np
import numpy.linalg as nplin
import itertools
#from coniii import *
from sklearn.linear_model import LinearRegression
np.random.seed(0)
def operators(s):
#generate terms in the energy function
n_seq,n_var = s.shape
ops = np.zeros((n_seq,n_var+int(n_var*... | github_jupyter |
# Global Segment Overflow
- recall function pointers are pointers that store addresses of functions/code
- see [Function-Pointers notebook](./Function-Pointers.ipynb) for a review
- function pointers can be overwritten using overflow techniques to point to different code/function
## Lucky 7 game
- various luck-ba... | github_jupyter |
```
#import modules
import re
import nltk
import numpy as np
import pandas as pd
from nltk.stem.porter import PorterStemmer
from nltk.corpus import stopwords
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from s... | github_jupyter |
This notebook contains the code needed to process the data which tracks the POIS and diversity, as generated by the SOS framework
Because of the large size of these tables, not all in-between artifacts are provided
This code is part of the paper "The Importance of Being Restrained"
```
import numpy as np
import pick... | github_jupyter |
# Welcome to Covid19 Data Analysis Notebook
------------------------------------------
### Let's Import the modules
```
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
```
## Task 2
### Task 2.1: importing covid19 dataset
importing "Covid19_Confirmed_dataset.csv" from ... | github_jupyter |
# Stability verification of a fixed uncertain system (without dynamic programming)
```
from __future__ import division, print_function
import tensorflow as tf
import gpflow
import numpy as np
import matplotlib.pyplot as plt
from future.builtins import *
from functools import partial
%matplotlib inline
import plottin... | 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 |
# Deep Learning with PyTorch Step-by-Step: A Beginner's Guide
# Chapter 1
```
try:
import google.colab
import requests
url = 'https://raw.githubusercontent.com/dvgodoy/PyTorchStepByStep/master/config.py'
r = requests.get(url, allow_redirects=True)
open('config.py', 'wb').write(r.content)
excep... | github_jupyter |
# Transfer Learning
## Imports and Version Selection
```
# TensorFlow ≥2.0 is required for this notebook
import tensorflow as tf
from tensorflow import keras
assert tf.__version__ >= "2.0"
# check if GPU is available as this notebook will be very slow without GPU
if not tf.test.is_gpu_available():
print("No GPU w... | github_jupyter |
# KAIST AI605 Assignment 1: Text Classification
TA in charge: Miyoung Ko (miyoungko@kaist.ac.kr)
**Due Date:** September 29 (Wed) 11:00pm, 2021
## Your Submission
If you are a KAIST student, you will submit your assignment via [KLMS](https://klms.kaist.ac.kr). If you are a NAVER student, you will submit via [Google F... | github_jupyter |
# Regression Metrics
Metrics covered:
#### 1) MSE, RMSE, R-squared
#### 2) MAE
#### 3) (R)MSPE, MAPE
#### 4) (R)MSLE
#### Notation
# MSE: Mean Squared Error
### How to evaluate MSE ?
First, you make baseline and check if your mode... | github_jupyter |
# Example of simple use of active learning API
Compare 3 query strategies: random sampling, uncertainty sampling, and active search.
Observe how we trade off between finding targets and accuracy.
# Imports
```
import warnings
warnings.filterwarnings(action='ignore', category=RuntimeWarning)
from matplotlib import py... | github_jupyter |
<a href="https://colab.research.google.com/github/gagansingh23/DS-Unit-2-Applied-Modeling/blob/master/Gagan_Singh_DS11_Sprint_Challenge_7.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
_Lambda School Data Science, Unit 2_
# Applied Modeling Sprint... | github_jupyter |
<a href="https://colab.research.google.com/github/aruanalucena/Car-Price-Prediction-Machine-Learning/blob/main/Car_Price_Prediction_.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# **Car Price Prediction with python**.
# **Previsão de carrro com P... | github_jupyter |
```
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="ticks")
%matplotlib inline
import numpy as np
np.random.seed(sum(map(ord, "axis_grids")))
```
```
tips = sns.load_dataset("tips")
g = sns.FacetGrid(tips, col="time")
```
```
g = sns.FacetGrid(tips, col="time")
g.map(plt.hist, "tip");
```
```
g ... | github_jupyter |
**handson用資料としての注意点**
普通、同じセル上で何度も試行錯誤するので、最終的に上手くいったセルしか残らず、失敗したセルは残りませんし、わざわざ残しません。
今回はhandson用に 試行・思考過程を残したいと思い、エラーやミスが出ても下のセルに進んで処理を実行するようにしています。
notebookのセル単位の実行ができるからこそのやり方かもしれません。良い。
（下のセルから文は常体で書きます。）
kunai (@jdgthjdg)
---
# ここまでの処理を整理して、2008〜2019のデータを繋いでみる
## xls,xlsxファイルを漁る
```
from pathlib import Pa... | github_jupyter |
**Outline of Steps**
+ Initialization
+ Download COCO detection data from http://cocodataset.org/#download
+ http://images.cocodataset.org/zips/train2014.zip <= train images
+ http://images.cocodataset.org/zips/val2014.zip <= validation images
+ http://images.cocodataset.... | github_jupyter |
# Explore secretion system genes
[KEGG enrichment analysis](5_KEGG_enrichment_of_stable_genes.ipynb) found that genes associated with ribosome, Lipopolysaccharide (outer membrane) biosynthesis, citrate cycle are significantly conserved across strains.
Indeed functions that are essential seem to be significantly conser... | github_jupyter |
## Launching Spark
Spark's Python console can be launched directly from the command line by `pyspark`. SparkSession can be found by calling `spark` object. The Spark SQL console can be launced by `spark-sql`. We will experiment with these in the upcoming sessions.
If we have `pyspark` and other required packages inst... | github_jupyter |
```
import torch
import math
import torch.nn as nn
import torch.optim as optim
import torch.utils
import PIL
from matplotlib import pyplot as plt
from PIL import Image
from torchvision import transforms
from torchvision import datasets
#Downloading CIFAR-10
data_path = '../data-unversioned/p1ch7/'
cifar10 = datasets.C... | github_jupyter |
2018-10-26
실용주의 파이썬 101 - Part 2
[김영호](https://www.linkedin.com/in/danielyounghokim/)
난이도 ● ● ◐ ○ ○
# Data Structures
- Immutable vs. Mutable
- Immutable: `tuple`
- Mutable: `list`, `set`, `dict`
- Mutable Container 설명 순서
- 초기화
- 추가/삭제
- 특정 값 접근(access)
- 정렬
## `tuple`
### 초기화
```
seq = ()
t... | github_jupyter |
# Logistic Regression
Notebook version: 2.0 (Nov 21, 2017)
2.1 (Oct 19, 2018)
Author: Jesús Cid Sueiro (jcid@tsc.uc3m.es)
Jerónimo Arenas García (jarenas@tsc.uc3m.es)
Changes: v.1.0 - First version
v.1.1 - Typo correction. Prepared for slide presentation
... | github_jupyter |
<div class="alert alert-block alert-info">
<b><h1>ENGR 1330 Computational Thinking with Data Science </h1></b>
</div>
Copyright © 2021 Theodore G. Cleveland and Farhang Forghanparast
Last GitHub Commit Date:
# 15: The `matplotlib` package
- explore different types of plots
- user defined functions for spe... | github_jupyter |
<a href="https://colab.research.google.com/github/mmoghadam11/ReDet/blob/master/train_UCAS_AOD.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
from google.colab import drive
drive.mount('/content/drive')
#باشد tesla t4 باید
#اگر نبود در بخش ران ... | github_jupyter |
## Importing the library
```
import numpy as np
```
## Data Types
### Scalars
```
# creating a scalar, we use the 'array' in order to create any type of data type e.g. scalar, vector, matrix
s = np.array(5)
# visualizing the shape of a scalar, in the example below it returns an empty tuple which is normal
# a scala... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import datetime
import os
print(datetime.datetime.now())
from pygentoolbox import SplitFastqFileBySeqLength
# from pygentoolbox.Tools import read_interleaved_fasta_as_noninterleaved
# from pygentoolbox.Tools import make_circos_karyotype_file
#dir(pygentoolbox.Tools)
%matplotlib i... | github_jupyter |
```
#Load dependencies
import numpy as np
import pandas as pd
pd.options.display.float_format = '{:,.1e}'.format
import sys
sys.path.insert(0, '../../statistics_helper')
from CI_helper import *
from excel_utils import *
```
# Estimating the total biomass of marine deep subsurface archaea and bacteria
We use our best ... | github_jupyter |
```
#%%appyter init
from appyter import magic
magic.init(lambda _=globals: _())
%%appyter hide_code
{% do SectionField(
name='PRIMARY',
title='1. Upload your data',
subtitle='Upload up and down gene-sets to perform two-sided rank enrichment. '+
'Upload up- or down-only gene-sets to perform rank... | github_jupyter |
# Probabilistic Matrix Factorization for Making Personalized Recommendations
```
%matplotlib inline
import numpy as np
import pandas as pd
import pymc3 as pm
from matplotlib import pyplot as plt
plt.style.use("seaborn-darkgrid")
print(f"Running on PyMC3 v{pm.__version__}")
```
## Motivation
So you are browsing for... | github_jupyter |
# Making your own modules
If a function will be used in multiple programs, it should be written
as a module instead.
All one has to do is put the functions in a program_name.py
file and import it (the whole thing) or the functions, then
use them in the main program.
Exactly the same way how you import and use oth... | github_jupyter |
<a href="https://colab.research.google.com/github/josearangos/PDI/blob/Colab/Colab_Class/binarySegmentation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import cv2
import numpy as np
import matplotlib.pyplot as plt
from google.colab.patches i... | github_jupyter |
```
import torch
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn import svm
from xgboost import XGBClassifier
from sklearn.metrics import recall_score
from joblib import dump, load
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
from s... | github_jupyter |
# Housing economy, home prices and affordibility
Alan Greenspan in 2014 pointed out that there was never a recovery from recession
without improvements in housing construction. Here we examine some relevant data,
including the Case-Shiller series, and derive an insightful
measure of the housing economy, **hscore**,... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Tutorials/Keiko/glad_alert.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_blank" h... | github_jupyter |
```
# Initial imports and notebook setup, click arrow to show
%matplotlib inline
# The first step is to be able to bring things in from different directories
import sys
import os
sys.path.insert(0, os.path.abspath('../lib'))
import matplotlib.pyplot as plt
import numpy as np
from copy import deepcopy
from util import... | github_jupyter |
<h1> Simple Single Layer RNN with Monthly dataset</h1>
```
import os
import numpy as np
import math
import pandas as pd
import seaborn as sns
import tensorflow as tf
import matplotlib.pyplot as plt
from keras.optimizers import SGD
from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout, GRU... | github_jupyter |
# Welcome to DFL-Colab!
This is an adapted version of the DFL for Google Colab.
# Overview
* Extractor works in full functionality.
* Training can work without preview.
* Merger works in full functionality.
* You can import/export workspace with your Google Drive.
* Import/export and another manipulations ... | github_jupyter |
# Convert verse ranges of genres to TF verse node features
```
import collections
import pandas as pd
from tf.fabric import Fabric
from tf.compose import modify
from tf.app import use
A = use('bhsa', hoist=globals())
genre_ranges = pd.read_csv('genre_ranges.csv')
genre_ranges
```
# Compile data & sanity checks
```
#... | github_jupyter |
```
#---------
# Recommend System in NetEase
#
# Modify History : 2019 - Jan - 22
# Platform: Win7 + Python2
#---------
```
### 1 从原始文件中抽取期望的歌单数据
```
#coding:urf-8
import json
import sys
# 从Json文件中提取特定格式的文本数据 #
# 返回的文本格式：歌曲名字##歌曲标签##歌单ID##歌曲收藏数目 歌曲信息
def parse_song_inline(in_line):
loaded_data = json.loa... | github_jupyter |
## 1. Importing the required libraries for EDA
```
import pandas as pd
import numpy as np # For mathematical calculations
import seaborn as sns # For data visualization
import matplotlib.pyplot as plt # For plotting graphs
%matplotlib inline
sns.set(color_codes=True)
im... | github_jupyter |
# Regularization
Welcome to the second assignment of this week. Deep Learning models have so much flexibility and capacity that **overfitting can be a serious problem**, if the training dataset is not big enough. Sure it does well on the training set, but the learned network **doesn't generalize to new examples** that... | github_jupyter |
<a href="https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_08_4_bayesian_hyperparameter_opt.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# T81-558: Applications of Deep Neural Networks
**Module 8:... | github_jupyter |
```
name = '2015-12-11-meeting-summary'
title = 'Introducing Git'
tags = 'git, github, version control'
author = 'Denis Sergeev'
from nb_tools import connect_notebook_to_post
from IPython.core.display import HTML
html = connect_notebook_to_post(name, title, tags, author)
```
Today we talked about git and its function... | github_jupyter |
# Observations
1. It appears the salaries are very high on the high end and very low at the low end.
2. Mimics the business model of a franchise restaurant where most of the lower end employees make minimum wage.
```
from sqlalchemy import create_engine, Table, Column, MetaData, Integer, Computed
from random import ra... | github_jupyter |
<a href="https://colab.research.google.com/github/MarceloClaro/python-business/blob/gh-pages/Avaliar_o_desempenho_de_um_aluno_usando_t%C3%A9cnicas_de_Machine_Learning_e_python.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# **AVALIAÇÃO DO DESEMPEN... | github_jupyter |
# Basic Bayesian Linear Regression Implementation
```
# Pandas and numpy for data manipulation
import pandas as pd
import numpy as np
# Matplotlib and seaborn for visualization
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
# Linear Regression to verify implementation
from sklearn.linear_... | github_jupyter |
<a href="https://colab.research.google.com/github/hoops92/DS-Unit-2-Kaggle-Challenge/blob/master/module3-cross-validation/Scott_LS_DS10_223_assignment.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Lambda School Data Science
*Unit 2, Sprint 2, Mod... | github_jupyter |
# Text recognition
We have a set of water meter images. We need to get each water meter’s readings. We ask performers to look at the images and write down the digits on each water meter.
To get acquainted with Toloka tools for free, you can use the promo code **TOLOKAKIT1** on $20 on your [profile page](https://toloka... | github_jupyter |
# Multi-Layer Perceptron, MNIST
---
In this notebook, we will train an MLP to classify images from the [MNIST database](http://yann.lecun.com/exdb/mnist/) hand-written digit database.
The process will be broken down into the following steps:
>1. Load and visualize the data
2. Define a neural network
3. Train the model... | github_jupyter |
```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
import statsmodels as stm
from os import walk
sns.set(rc={'figure.figsize':(14.7,8.27)})
OxA00=np.load("NeuroNER-master/src/SalmanTest/MyTrain385SeparateRepFlag00/Mr1mainSalmanUnary_scorestest.npy")
O... | github_jupyter |
# A Cartographers Expedition
You and your friends have decided to tackle NYC old school! No cell phones or GPS devices allowed. Although everyone is a bit nervous, you realize that using an actual map might be pretty cool.
Your goal is to generate a map that plots your between five and six locations in the city. Pl... | github_jupyter |
```
import emcee
import pandas as pd
import numpy as np
from scipy import stats
import chainconsumer
import matplotlib.pyplot as plt
from scipy.integrate import quad
from chainconsumer import ChainConsumer
plt.rc('font', size=15) # controls default text sizes
plt.rc('axes', titlesize=15) # fontsize of the... | github_jupyter |
# 붓꽃(Iris) 품종 데이터 예측하기
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#DataFrame" data-toc-modified-id="DataFrame-1"><span class="toc-item-num">1 </span>DataFrame</a></span></li><li><span><a href="#Train/Test-데이터-나누어-학습하기" data-toc-modified-i... | github_jupyter |
<a id="title_ID"></a>
# JWST Pipeline Validation Notebook: AMI3, AMI3 Pipeline
<span style="color:red"> **Instruments Affected**</span>: NIRISS
### Table of Contents
<div style="text-align: left">
<br> [Introduction](#intro)
<br> [JWST CalWG Algorithm](#algorithm)
<br> [Defining Terms](#terms)
<br> [Test Desc... | github_jupyter |
# Density estimation demo
Here we demonstrate how to use the ``inference.pdf`` module for estimating univariate probability density functions from sample data.
```
from numpy import linspace, zeros, exp, log, sqrt, pi
from numpy.random import normal, exponential
from scipy.special import erfc
import matplotlib.pyplot... | github_jupyter |
# Assignment 3: RTRL
Implement an RNN with RTRL. The ds/dw partial derivative is 2D hidden x (self.n_hidden * self.n_input) instead of 3d.
```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
class RNN(object):
def __init__(self, n_input, n_hidden, n_output):
# init weights and biases... | github_jupyter |
===============================
### IMPORTS & GET DATABASE INFO
```
from jsons import read_json_to_dict
from mysql_driver import MySQL
import pandas as pd
from sqlalchemy import create_engine
json_readed = read_json_to_dict("sql_server_settings.json")
IP_DNS = json_readed["IP_DNS"]
USER = json_readed["USER"]
PASSWOR... | github_jupyter |
# 各主流摄像头的rtsp地址格式
## 海康威视
### IPC 摄像头
>rtsp://[username]:[password]@[ip]:[port]/[codec]/[channel]/[subtype]/av_stream
说明：
- username: 用户名。例如admin。
- password: 密码。例如12345。
- ip: 为设备IP。例如 192.0.0.64。
- port: 端口号默认为554，若为默认可不填写。
- codec：有h264、MPEG-4、mpeg4这几种。
- channel: 通道号，起始为1。例如通道1，则为ch1。
- subtype: 码流类型，主码流为main，... | github_jupyter |
# Heating Mesh Tally on CAD geometry made from Shapes
This constructs a reactor geometry from 3 Shape objects each made from points.
The Shapes made include a breeder blanket, PF coil and a central column shield.
2D and 3D Meshes tally are then simulated to show nuclear heating, flux and tritium_production across th... | github_jupyter |
```
from gtts import gTTS
LANG_PATH = '../lang/{0}/speech/{1}.mp3'
tts = gTTS(text='Se ha detectado más de una persona, inténtelo de nuevo con una persona sólo por favor', lang='es', slow=False)
tts.save(LANG_PATH.format('es', 'more_than_one_face'))
tts = gTTS(text='There appears to be more than one person, try again ... | github_jupyter |
<a href="https://colab.research.google.com/github/MoRebaie/Sequences-Time-Series-Prediction-in-Tensorflow/blob/master/Course_4_Week_1_Exercise_Question.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!pip install tensorflow==2.0.0b1
import tenso... | github_jupyter |
<img src="../../images/qiskit-heading.gif" alt="Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook" width="500 px" align="left">
# Getting Started with Qiskit
Here, we provide an overview of working with Qiskit. Qiskit provides the basic building blocks necessary... | github_jupyter |
____
__Universidad Tecnológica Nacional, Buenos Aires__\
__Ingeniería Industrial__\
__Cátedra de Investigación Operativa__\
__Autor: Martín Palazzo__ (Mpalazzo@frba.utn.edu.ar) y __Rodrigo Maranzana__ (Rmaranzana@frba.utn.edu.ar)
____
# Simulación con distribución Exponencial
<h1>Índice<span class="tocSkip"></span></... | github_jupyter |
<p><font size="6"><b>Visualization - Matplotlib</b></font></p>
> *DS Data manipulation, analysis and visualization in Python*
> *May/June, 2021*
>
> *© 2021, Joris Van den Bossche and Stijn Van Hoey (<mailto:jorisvandenbossche@gmail.com>, <mailto:stijnvanhoey@gmail.com>). Licensed under [CC BY 4.0 Creative Commons]... | github_jupyter |
# Reproduce CheXNet: Explore Predictions
## Import other modules and pandas
```
import visualize_prediction as V
import pandas as pd
#suppress pytorch warnings about source code changes
import warnings
warnings.filterwarnings('ignore')
```
## Settings for review
We can examine individual results in more detail, se... | github_jupyter |
# Assignment 1
#### Student ID: *Double click here to fill the Student ID*
#### Name: *Double click here to fill the name*
## Q1: Exploring the TensorFlow playground
http://playground.tensorflow.org/
(a) Execute the following steps first:
1. Change the dataset to exclusive OR dataset (top-right dataset under "DATA... | github_jupyter |
#### The purpose of this notebook is to compare D-REPR with other methods such as KR2RML and R2RML in term of performance
```
import re, numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm_notebook as tqdm
%matplotlib inline
plt.rcParams["figure.figsize"] = (10.0, 8.0) # set default size of plots
plt.rc... | github_jupyter |
```
#Importing necessary libraries
import keras
import numpy as np
import pandas as pd
from keras.applications import VGG16, inception_v3, resnet50, mobilenet
from keras import models
from keras import layers
from keras import optimizers
from sklearn.metrics import classification_report, confusion_matrix
import matplo... | github_jupyter |
# Chapter 1: Pandas Foundations
```
import pandas as pd
import numpy as np
```
## Introduction
## Dissecting the anatomy of a DataFrame
```
pd.set_option('max_columns', 4, 'max_rows', 10)
movies = pd.read_csv('../data/movie.csv')
movies.head()
```
### How it works...
## DataFrame Attributes
### How to do it... {... | github_jupyter |
```
%matplotlib inline
```
# Tuning a scikit-learn estimator with `skopt`
Gilles Louppe, July 2016
Katie Malone, August 2016
Reformatted by Holger Nahrstaedt 2020
.. currentmodule:: skopt
If you are looking for a :obj:`sklearn.model_selection.GridSearchCV` replacement checkout
`sphx_glr_auto_examples_sklearn-grids... | github_jupyter |
# Using Convolutional Neural Networks
Welcome to the first week of the first deep learning certificate! We're going to use convolutional neural networks (CNNs) to allow our computer to see - something that is only possible thanks to deep learning.
## Introduction to this week's task: 'Dogs vs Cats'
We're going to tr... | github_jupyter |
```
import pandas as pd
import numpy as np
import xgboost as xgb
from sklearn import preprocessing
from sklearn.cross_validation import KFold
from sklearn.metrics import mean_absolute_error
%matplotlib inline
train = pd.read_csv('train.csv')
cat_feats = train.select_dtypes(include=["object"]).columns
for feat in ca... | github_jupyter |
# <center>MobileNet - Pytorch
# Step 1: Prepare data
```
# MobileNet-Pytorch
import argparse
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim.lr_scheduler import StepLR
from torchvision import datasets, transforms
from torch.autograd i... | github_jupyter |
# Lab 1: Tensor Manipulation
First Author: Seungjae Ryan Lee (seungjaeryanlee at gmail dot com)
Second Author: Ki Hyun Kim (nlp.with.deep.learning at gmail dot com)
<div class="alert alert-warning">
NOTE: This corresponds to <a href="https://www.youtube.com/watch?v=ZYX0FaqUeN4&t=23s&list=PLlMkM4tgfjnLSOjrEJN31gZA... | github_jupyter |
## Tutorial on how to simulate an Argo float in Parcels
This tutorial shows how simple it is to construct a Kernel in Parcels that mimics the [vertical movement of Argo floats](http://www.argo.ucsd.edu/operation_park_profile.jpg).
```
# Define the new Kernel that mimics Argo vertical movement
def ArgoVerticalMovement... | github_jupyter |
# 고객의 행동 분석/파악
* use_log.csv : 선테 이용 이력(2018년 4월 ~ 2019년 3월)
* customer_master.csv : 2019년 3월 말 회원 데이터(이전 탈퇴 회원 포함)
* class_master.csv : 회원 구분(종일, 주간, 야간)
* campaign_master.csv : 행사 구분(입회비 유무)
## 1. 데이터 확인
```
import pandas as pd
uselog = pd.read_csv('use_log.csv')
print(len(uselog))
uselog.head()
customer = pd.read_... | github_jupyter |
```
from database.market import Market
from database.strategy import Strategy
from extractor.tiingo_extractor import TiingoExtractor
from preprocessor.model_preprocessor import ModelPreprocessor
from preprocessor.predictor_preprocessor import PredictorPreprocessor
from modeler.modeler import Modeler
from datetime impor... | github_jupyter |
# Use Keras to recognize hand-written digits with `ibm-watson-machine-learning`
This notebook uses the Keras machine learning framework with the Watson Machine Learning service. It contains steps and code to work with [ibm-watson-machine-learning](https://pypi.python.org/pypi/ibm-watson-machine-learning) library avai... | github_jupyter |
<a href="https://colab.research.google.com/github/katie-chiang/ARMultiDoodle/blob/master/Copy_of_Welcome_To_Colaboratory.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
<p><img alt="Colaboratory logo" height="45px" src="/img/colab_favicon.ico" align... | github_jupyter |
```
import random
import copy
import logging
import sys
from run_tests_201204 import *
import os
import sys
import importlib
from collections import defaultdict
sys.path.insert(0, '/n/groups/htem/Segmentation/shared-nondev/cb2_segmentation/analysis_mf_grc')
from tools_pattern import get_eucledean_dist
import compress... | github_jupyter |
<a href="https://colab.research.google.com/github/RoisulIslamRumi/MNIST-PyTorch/blob/main/MNist_with_Pytorch.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
#Import required libraries
import torch #imports all essential modules to build NN
impor... | github_jupyter |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.