code stringlengths 2.5k 150k | kind stringclasses 1
value |
|---|---|
# Uncertainty Sampling on the Radio Galaxy Zoo
```
import sys
import h5py, numpy, sklearn.neighbors
from astropy.coordinates import SkyCoord
import matplotlib.pyplot as plt
sys.path.insert(1, '..')
import crowdastro.train, crowdastro.test
TRAINING_H5_PATH = '../training.h5'
CROWDASTRO_H5_PATH = '../crowdastro.h5'
N... | github_jupyter |
<a href="https://colab.research.google.com/github/lasyaistla/Ai.fellowship/blob/main/Copy_of_Style_Transfer_PyTorch.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Please complete the missing parts in the code below. Moreover, please correct the m... | github_jupyter |
# Assignment 1: Numpy RNN
Implement a RNN and run BPTT
```
from typing import Dict, Tuple
import numpy as np
class RNN(object):
"""Numpy implementation of sequence-to-one recurrent neural network for regression tasks."""
def __init__(self, input_size: int, hidden_size: int, output_size: int):
"""I... | github_jupyter |
<a href="https://colab.research.google.com/github/marixko/Supervised_Learning_Tutorial/blob/master/The_Basics_of_Supervised_Learning_For_Astronomers.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
###**About Google's Colaboratory: **
This is a fre... | github_jupyter |
```
import numpy as np
import pandas as pd
import xarray as xr
import xesmf as xe
import json
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
def make_regridder(ds, ds_base, variable, algorithm='bilinear'):
if 'latitude' in ds[variable].dims:
ds = ds.rename({'latitude': 'lat', 'longitude': 'lon... | github_jupyter |
# Peform statistical analyses of GNSS station locations and tropospheric zenith delays
**Author**: Simran Sangha, David Bekaert - Jet Propulsion Laboratory
This notebook provides an overview of the functionality included in the **`raiderStats.py`** program. Specifically, we outline examples on how to perform basic st... | github_jupyter |
<a href="https://colab.research.google.com/github/probml/probml-notebooks/blob/main/notebooks/smc_logreg_tempering.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#SMC for logistic regression
We compare data tempering (IBIS) with temperature temper... | github_jupyter |
* basic roberta ft: 0.6589791487657798 (thr 0.3)
* basic roberta ft (head first): 0.6768011808573329 (thr 0.42)
* fine tune roberta on weird clf, then only head on spans, then whole: 0.6853127403287083 (thr 0.32)
*
```
from transformers import RobertaTokenizer, RobertaForTokenClassification
from transformers import Be... | github_jupyter |
# Predicting Credit Card Default with Neural Networks
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
%matplotlib inline
```
### Back with the credit card default dataset
```
# Loading the dataset
DATA_DIR = '../data'
FILE_NAME = 'credit_card_default.csv'
da... | github_jupyter |
```
import pandas as pd
# Let us just create a dictioinary to understand about the DataFrame.
people = {
"First": ["Me", "Myself", "I"],
"Last" : ["He", 'She', "It"],
"Email" : ["mehe@email.com", "myselfshe@email.com", "iit@email.com"]
}
# In this dict We can visualise the keys as the column's descripton... | github_jupyter |
```
## Basic stuff
%load_ext autoreload
%autoreload
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
display(HTML("""<style>div.output_area{max-height:10000px;overflow:scroll;}</style>"""))
#IPython.Cell.options_default.cm_config.lineNumbers = true;
... | github_jupyter |
# WELL NOTEBOOK
## Well logs visualization & petrophysics
Install the the repository reservoirpy from github and import the required packages
```
import os
path = os.path.join('/home/santiago/Documents/dev/reservoirpy')
import sys
sys.path.insert(0,path)
import pandas as pd
import geopandas as gpd
import numpy as... | github_jupyter |
```
import paddle
from paddle.nn import Linear
import paddle.nn.functional as F
import numpy as np
import os
import random
def load_data():
# 从文件导入数据
datafile = 'external-libraries/housing.data'
data = np.fromfile(datafile, sep=' ', dtype=np.float32)
# 每条数据包括14项，其中前面13项是影响因素，第14项是相应的房屋价格中位数
... | github_jupyter |
# **<div align="center"> Dolby.io Developer Days Media APIs 101 - Getting Started </div>**
### **<div align="center"> Notebook #1: Getting Started</div>**
### Starting with a Raw Audio File
We can run code blocks like this in Binder by pressing "Control+Enter". Try it now after clicking the below code block!
```
im... | github_jupyter |
# Import
```
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import matplotlib
matplotlib.__version__
np.__version__, pd.__version__
```
# Dataset:
```
from sklearn.datasets import california_housing
data = california_housing.fetch_california_housing()
X = data['data']
y = data['target']
col... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import sys
sys.path.append('../docs')
from gen_doc.nbdoc import show_doc as sd
#export
from nb_001b import *
import sys, PIL, matplotlib.pyplot as plt, itertools, math, random, collections, torch
import scipy.stats, scipy.special
from enum import Enum, IntEnum
from torch import ... | github_jupyter |
<a href="https://colab.research.google.com/github/JimKing100/nfl-test/blob/master/predictions/Prediction_Offense_Final.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
# Installs
!pip install pmdarima
# Imports
import numpy as np
import pandas as... | github_jupyter |
### Instructions
1. Make sure you are using a version of notebook greater than v.3. If you installed Anaconda with python 3 - this is likely to be fine. The next piece of code will check if you have the right version.
2. The notebook has both some open test cases that you can use to test the f... | github_jupyter |
```
# Itertools
# product --> Returns the Cartesian product of iterables such as lists
from itertools import product
lst_a=[2,4]
lst_b=[3,6]
print('List a -->',lst_a)
print('List b -->',lst_b)
ab=product(lst_a,lst_b)
print('Returns a product object -->',type(ab))
lst_ab=list(ab)
print('Returns a list -->',type(lst_ab... | github_jupyter |
### As before, let's find the set of compounds for which both simulations and experimental measurements exist
Matt Robinson posted a `moonshot_initial_activity_data.csv` file of the initial activity data:
```
import numpy as np
import pandas as pd
df_activity = pd.read_csv('../data-release-2020-05-10/moonshot_initia... | github_jupyter |
## Model one policy variables
This notebook extracts the selected policy variables in the `indicator_list` from IMF and World Bank (wb) data sources, and writes them to a csv file.
```
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
warn... | github_jupyter |
```
from opentrons import simulate
ctx = simulate.get_protocol_api('2.1')
NUM_SAMPLES = 48
VOLUME_MMIX = 20
ELUTION_LABWARE = '2ml tubes'
PREPARE_MASTERMIX = True
MM_TYPE = 'MM1'
EL_LW_DICT = {
'large strips': 'opentrons_96_aluminumblock_generic_pcr_strip_200ul',
'short strips': 'opentrons_96_aluminumblock_ge... | github_jupyter |
<a href="https://colab.research.google.com/github/wearlianbaguio/OOP-1-1/blob/main/OOP_Concepts_2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
##Application 1
1. Create a Python program that displays the name of the students (Student 1, Student 2... | github_jupyter |
# ETL Pipeline Preparation
Follow the instructions below to help you create your ETL pipeline.
### 1. Import libraries and load datasets.
- Import Python libraries
- Load `messages.csv` into a dataframe and inspect the first few lines.
- Load `categories.csv` into a dataframe and inspect the first few lines.
```
# imp... | github_jupyter |
# Assignment 1
This assignment is to test your understanding of Python basics.
Answer the questions and complete the tasks outlined below; use the specific method described, if applicable. In order to get complete points on your homework assigment you have to a) complete this notebook, b) based on your results answer... | github_jupyter |
# Sklearn
## sklearn.model_selection
документация: http://scikit-learn.org/stable/modules/cross_validation.html
```
from sklearn import model_selection, datasets
import numpy as np
```
### Разовое разбиение данных на обучение и тест с помощью train_test_split
```
iris = datasets.load_iris()
train_data, test_data,... | github_jupyter |
# Black Litterman with Investor Views Optimization: Oldest Country ETFs
# Charts
## 1. Data Fetching
### 1.1 Model configuration
```
import os
import sys
import datetime as dt
import logging
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from hmmlearn import hmm
import c... | github_jupyter |
<a href="https://colab.research.google.com/github/Conscious-Mind/TensorFlow-Course-DeepLearning.AI/blob/main/C1/W2/ungraded_labs/C1_W2_Lab_1_beyond_hello_world_completed.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Ungraded Lab: Beyond Hello Wo... | github_jupyter |
# <strong>Road networks and robustness to flooding on US Atlantic and Gulf barrier islands</strong>
## <strong>- Download elevations for the US Atlantic and Gulf barrier islands -</strong>
### The purpose of this notebook is to download CUDEM tiles from https://coast.noaa.gov/htdata/raster2/elevation/NCEI_ninth_Topo... | github_jupyter |
```
"""
Training script to train a model on MultiNLI and, optionally, on SNLI data as well.
The "alpha" hyperparamaters set in paramaters.py determines if SNLI data is used in training. If alpha = 0, no SNLI data is used in training. If alpha > 0, then down-sampled SNLI data is used in training.
"""
%tb
import tens... | github_jupyter |
```
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
from torch.utils.tensorboard import SummaryWriter
import numpy as np
import pandas as p... | github_jupyter |
# Testing `TFNoiseAwareModel`
We'll start by testing the `textRNN` model on a categorical problem from `tutorials/crowdsourcing`. In particular we'll test for (a) basic performance and (b) proper construction / re-construction of the TF computation graph both after (i) repeated notebook calls, and (ii) with `GridSear... | github_jupyter |
# Analyze population data from https://covidtracking.com
**Note:** This is a Jupyter notebook which is also available as its executable export as a Python 3 script (therefore with automatically generated comments).
### Sept 29,2021: Obsolete data
Our source https://covidtracking.com/data/api says:
- `As of March 7, ... | github_jupyter |
```
from scripts.setup_libs import *
```
# [CatBoost](https://github.com/catboost/catboost)
Бустинг от Яндекса для категориальных фичей и много чего еще.
Для начала настоятельно рекомендуется посмотреть видео. Там идет основная теория по CatBoost
```
from IPython.display import YouTubeVideo
YouTubeVideo('UYDwhuyWYS... | github_jupyter |
### Evaluación 1. Parte Computacional (60 puntos)
#### (Elementos de Probabilidad y Estadística: 3008450)
Se tiene información acerca de 694 propiedades ubicadas en el valle de aburra. La base de datos fue recolectada en el año 2015, e incluye las siguientes variables:
1. valor comercial de la propiedad en millones ... | github_jupyter |
# Sorting Madonna Songs
This project will randomly order the entire backlog of Madonna's songs. This was motivated following a colleague's offhand remark about one's favourite song being *Material Girl* by Madge, which triggered another colleague to provide the challenge of ranking all of Madonna's songs.
In particu... | github_jupyter |
# Homework (16 pts) - Hypothesis Testing
```
import numpy as np
import scipy.stats as st
import matplotlib.pyplot as plt
```
1. You measure the duration of high frequency bursts of action potentials under two different experimental conditions (call them conditions A and B). Based on your measured data below, determin... | github_jupyter |
### TDD for data with pytest
TDD is great for software engineering, but did you know TDD can add a lot of speed and quality to Data Science projects too?
We'll learn how we can use TDD to save you time - and quickly improve functions which extract and process data.
# About me
**Chris Brousseau**
*Surface Owl - F... | github_jupyter |
# Information Flow
In this chapter, we detail how to track information flows in python by tainting input strings, and tracking the taint across string operations.
Some material on `eval` exploitation is adapted from the excellent [blog post](https://nedbatchelder.com/blog/201206/eval_really_is_dangerous.html) by Ned ... | github_jupyter |
## Face and Facial Keypoint detection
After you've trained a neural network to detect facial keypoints, you can then apply this network to *any* image that includes faces. The neural network expects a Tensor of a certain size as input and, so, to detect any face, you'll first have to do some pre-processing.
1. Detect... | github_jupyter |
## Reinforcement Learning Tutorial -1: Q Learning
#### MD Muhaimin Rahman
sezan92[at]gmail[dot]com
Q learning , can be said one of the most famous -and kind of intuitive- of all Reinforcement learning algorithms. In fact ,the recent all algorithms using Deep learning , are based on the Q learning algorithms. So, to w... | github_jupyter |
```
import sys; sys.path.insert(0, '..')
import spot
spot.setup()
import buddy
from spot.jupyter import display_inline
from decimal import Decimal
import decimal
from fimdp.core import ConsMDP
from fimdp.energy_solvers import BasicES
from fimdp.labeled import LabeledConsMDP
from fimdp.objectives import BUCHI
```
# P... | github_jupyter |
```
"""
The concept of the creation of the test data in order to evaluate the similarities, a synthethic data creation is introduced.
The idea is as follows:
1. Outlier Instances.
These are the users who deviate from the other in the data.
Identifying them would of interest to understand how they behave against most o... | github_jupyter |
<a href="https://colab.research.google.com/github/michelmunoz99/daa_2021_1/blob/master/20enero.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
class NodoArbol:
def __init__(self, value, left=None, right=None):
self.data=value
self.left=left... | github_jupyter |
# Marginalized Gaussian Mixture Model
Author: [Austin Rochford](http://austinrochford.com)
```
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
import pymc3 as pm
import seaborn as sns
SEED = 383561
np.random.seed(SEED) # from random.org, for reproducibility
```
Gaussian mixtures are a fle... | github_jupyter |
<img align="left" src="https://lever-client-logos.s3.amazonaws.com/864372b1-534c-480e-acd5-9711f850815c-1524247202159.png" width=200>
<br></br>
<br></br>
# Major Neural Network Architectures Challenge
## *Data Science Unit 4 Sprint 3 Challenge*
In this sprint challenge, you'll explore some of the cutting edge of Data... | github_jupyter |
# The Data
to see where we got the data go here: https://www.ndbc.noaa.gov/station_history.php?station=42040
```
import pandas as pd
import numpy as np
import datetime
```
This is the first set of data from 1995
```
from utils import read_file, build_median_df
df1995 = read_file('data/42040/buoy_data_1995.txt') #all... | github_jupyter |
Lambda School Data Science
*Unit 1, Sprint 2, Module 1*
---
_Lambda School Data Science_
# Join and Reshape datasets
Objectives
- concatenate data with pandas
- merge data with pandas
- understand tidy data formatting
- melt and pivot data with pandas
Links
- [Pandas Cheat Sheet](https://github.com/pandas-dev/p... | github_jupyter |
## Model training
In this notebook we'll first define a baseline model and then train a couple of ML models to try to better that performance.
The dataset is quite small and the features are few, so I'm going to keep it simple in terms of algotrithms. We'll see how a logistic regression model and a random forest model... | github_jupyter |
```
library(magrittr)
treatment_df = readr::read_tsv('../summary/indications.tsv') %>%
dplyr::filter(rel_type == 'TREATS_CtD') %>%
dplyr::select(compound_id, disease_id) %>%
dplyr::mutate(status = 1)
degree_prior_df = readr::read_tsv('data/degree-prior.tsv') %>%
dplyr::mutate(Empiric = n_treatments / n_possibl... | github_jupyter |
```
import pandas as pd
train_data_file = 'data/zhengqi_train.txt'
test_data_file = 'data/zhengqi_test.txt'
train_data = pd.read_csv(train_data_file, sep = '\t', encoding = 'utf-8')
test_data = pd.read_csv(test_data_file, sep = '\t', encoding = 'utf_8')
```
## 定义特征构造方法
```
eps = 1e-5
# 交叉特征方式
func_dict = {
'a... | github_jupyter |
# 基于注意力的神经机器翻译
此笔记本训练一个将波斯语翻译为英语的序列到序列（sequence to sequence，简写为 seq2seq）模型。此例子难度较高，需要对序列到序列模型的知识有一定了解。
训练完此笔记本中的模型后，你将能够输入一个波斯语句子，例如 *"من می دانم."*，并返回其英语翻译 *"I know."*
对于一个简单的例子来说，翻译质量令人满意。但是更有趣的可能是生成的注意力图：它显示在翻译过程中，输入句子的哪些部分受到了模型的注意。
<img src="https://tensorflow.google.cn/images/spanish-english.png" alt="spanish... | github_jupyter |
This is from a "Getting Started" competition from Kaggle [Titanic competition](https://www.kaggle.com/c/titanic) to showcase how we can use Auto-ML along with datmo and docker, in order to track our work and make machine learning workflow reprocible and usable. Some part of data analysis is inspired from this [kernel]... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import os
from scipy.optimize import curve_fit
os.getcwd()
data = pd.read_csv('data/CA_Fc_GC_MeCN_0V-1.2V_P-06-14/data.csv', sep=',')
data
data.plot('t', 'iw', xlim=(0,2))
index_max = data['iw'].idxmax()
time_max = data.loc[in... | github_jupyter |
# Extract from JSON and XML
You'll now get practice extracting data from JSON and XML. You'll extract the same population data from the previous exercise, except the data will be in a different format.
Both JSON and XML are common formats for storing data. XML was established before JSON, and JSON has become more pop... | github_jupyter |
# Main Code
```
import os
import time
import numpy as np
import redis
from IPython.display import clear_output
from PIL import Image
from io import BytesIO
import base64
import json
import matplotlib.pyplot as plt
from face_detection import get_face
from utils import img_to_txt, decode_img, log_error
###############... | github_jupyter |
```
from mxnet import nd
from mxnet.contrib import text
glove_vec = text.embedding.get_pretrained_file_names("glove")
print(glove_vec)
glove_6b50d = text.embedding.create('glove', pretrained_file_name="glove.6B.50d.txt")
word_size = len(glove_6b50d)
print(word_size)
#词的索引
index = glove_6b50d.token_to_idx['happy']
print... | github_jupyter |
# Dictionaries
### # Dictionary in Python is an unordered collection of data values.
### # Dictionary holds key:value pair.
### # Each key-value pair in a Dictionary is separated by a colon whereas each key is separated by a ‘comma’.
### # Keys of a Dictionary must be unique.
```
Dictionary = {1:'Nokia',2:'EDP'... | github_jupyter |
# Tutorial 01: Running Sumo Simulations
This tutorial walks through the process of running non-RL traffic simulations in Flow. Simulations of this form act as non-autonomous baselines and depict the behavior of human dynamics on a network. Similar simulations may also be used to evaluate the performance of hand-design... | github_jupyter |
# Quick start guide
## Installation
### Stable
Fri can be installed via the Python Package Index (PyPI).
If you have `pip` installed just execute the command
pip install fri
to get the newest stable version.
The dependencies should be installed and checked automatically.
If you have problems installing plea... | github_jupyter |
# Amortized Neural Variational Inference for a toy probabilistic model
Consider a certain number of sensors placed at known locations, $\mathbf{s}_1,\mathbf{s}_2,\ldots,\mathbf{s}_L$. There is a target at an unknown position $\mathbf{z}\in\mathbb{R}^2$ that is emitting a certain signal that is received at the $i$-th... | github_jupyter |
```
%matplotlib inline
from pyvista import set_plot_theme
set_plot_theme('document')
```
Colormap Choices {#colormap_example}
================
Use a Matplotlib, Colorcet, cmocean, or custom colormap when plotting
scalar values.
```
from pyvista import examples
import pyvista as pv
import matplotlib.pyplot as plt
fro... | github_jupyter |
```
# Originally made by Katherine Crowson (https://github.com/crowsonkb, https://twitter.com/RiversHaveWings)
# The original BigGAN+CLIP method was by https://twitter.com/advadnoun
import math
import random
# from email.policy import default
from urllib.request import urlopen
from tqdm import tqdm
import sys
import o... | github_jupyter |
## Homework: Multilingual Embedding-based Machine Translation (7 points)
**In this homework** **<font color='red'>YOU</font>** will make machine translation system without using parallel corpora, alignment, attention, 100500 depth super-cool recurrent neural network and all that kind superstuff.
But even without para... | github_jupyter |
# Matplotlib and NumPy crash course
You may install numpy, matplotlib, sklearn and many other usefull package e.g. via Anaconda distribution.
```
import numpy as np
```
## NumPy basics
### Array creation
```
np.array(range(10))
np.ndarray(shape=(5, 4))
np.linspace(0, 1, num=20)
np.arange(0, 20)
np.zeros(shape=(5, ... | github_jupyter |
<a href="https://colab.research.google.com/github/piyushsharma1812/recurrent-neural-networks/blob/master/NER_LSTM.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
# Required Libraries
import numpy as np
import pandas as pd
from keras.preprocessi... | github_jupyter |
# 虚谷号WebGPIO应用（客户端Python版）
虚谷号和手机（App inventor）如何互动控制？
虚谷号和掌控板如何互动控制？
为了让虚谷号和其他开源硬件、编程语言快速互动，虚谷号的WebGPIO应运而生。简单的说，只要在虚谷号上运行一个python文件，就可以用WebAPI的形式来与虚谷号互动，可以获取虚谷号板载Arduino的所有引脚的电平，也可以控制所有引脚。
## 1.接口介绍
要在虚谷号上运行“webgpio.py”。也可以将“webgpio.py”文件更名为“main.py”，复制到vvBoard的Python目录，只要一开机，虚谷号就会执行。
下载地址：https://github.com/vv... | github_jupyter |
# Toy Environment
In this exercise we will learn how to implement a simple Toy environment using Python. The environment is illustrated in figure. It is composed of 3 states and 2 actions. The initial state is state 1.
The goal of this exercise is to implement a class Environment w... | github_jupyter |
The second example shows how to set up a power-law spectral source, as well as how to add two sets of photons together.
This example will also briefly show how to set up a mock dataset "in memory" using yt. For more details on how to do this, check out [the yt docs on in-memory datasets](http://yt-project.org/doc/exam... | github_jupyter |
```
# extract features of region of an image from mask-rcnn by Detectron2
import detectron2
from detectron2.utils.logger import setup_logger
setup_logger()
# import some common libraries
import numpy as np
import cv2
import random
import io
import torch
# import some common detectron2 utilities
from detectron2 impo... | github_jupyter |
```
from helpers.utilities import *
%run helpers/notebook_setup.ipynb
```
While attempting to compare limma's results for log-transformed an non-transformed data, it was noticed (and brought up by Dr Tim) That the values of logFC produced by limma for non-transformed data are of wrong order of magnitude.
I have inves... | github_jupyter |
### AVGN Tutorial
This tutorial walks you through getting started with AVGN on a sample dataset, so you can figure out how to use it on your own data.
If you're not too familiar with Python, make sure you've first familiarized yourself with Jupyter notebooks and installing pyhton packages. Then come back and try the ... | github_jupyter |
# First BigQuery ML models for Taxifare Prediction
In this notebook, we will use BigQuery ML to build our first models for taxifare prediction.BigQuery ML provides a fast way to build ML models on large structured and semi-structured datasets.
## Learning Objectives
1. Choose the correct BigQuery ML model type and sp... | github_jupyter |
# Plotting
Here you can explore the different possibilities that the hep_spt package offers for plotting.
```
%matplotlib inline
import hep_spt
hep_spt.set_style()
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.axes_grid1 import make_axes_locatable
from scipy.stats import norm
```
## Plotting a ... | github_jupyter |
```
Copyright 2021 IBM Corporation
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 applicable law or agreed to in writing, softwa... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
from collections import defaultdict
from scipy.optimize import minimize
import networkx as nx
from networkx.generators.random_graphs import erdos_renyi_graph
from IPython.display import Image
from qiskit import QuantumCircuit, execute, Aer
from qiskit.tools.visu... | github_jupyter |
##### Detection and Location Chain
**Abstract**: This hackathon project represents our effort to combine our existing machine learning and photogrametry efforts and further combine those efforts with both Cloud and Edge based solutions based upon Xilinx FPGA acceleration.
The Trimble team decided that the Xilinx hac... | github_jupyter |
# 6 - Pivot Table
In this sixth step I'll show you how to reshape your data using a pivot table.
This will provide a nice condensed version.
We'll reshape the data so that we can see how much each customer spent in each category.
```
import pandas as pd
import numpy as np
df = pd.read_json("customer_data.json", c... | github_jupyter |
```
#export
from fastai.basics import *
from fastai.callback.progress import *
from fastai.text.data import TensorText
from fastai.tabular.all import TabularDataLoaders, Tabular
from fastai.callback.hook import total_params
#hide
from nbdev.showdoc import *
#default_exp callback.wandb
```
# Wandb
> Integration with [... | github_jupyter |
# Detecting Payment Card Fraud
In this section, we'll look at a credit card fraud detection dataset, and build a binary classification model that can identify transactions as either fraudulent or valid, based on provided, *historical* data. In a [2016 study](https://nilsonreport.com/upload/content_promo/The_Nilson_Rep... | github_jupyter |
***
***
# 11. 튜플과 집합
***
***
***
## 1 튜플 활용법
***
- 튜플(Tuples): 순서있는 임의의 객체 모음 (시퀀스형)
- 튜플은 변경 불가능(Immutable)
- 시퀀스형이 가지는 다음 연산 모두 지원
- 인덱싱, 슬라이싱, 연결, 반복, 멤버쉽 테스트
### 1-1 튜플 연산
```
t1 = () # 비어있는 튜플
t2 = (1,2,3) # 괄호 사용
t3 = 1,2,3 # 괄호가 없어도 튜플이 됨
print(type(t1), type(t2), type(t3))
# <type 'tuple'> <type ... | github_jupyter |
```
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from rmgpy.kinetics import *
# Set global plot styles
plt.style.use('seaborn-paper')
plt.rcParams['axes.labelsize'] = 16
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12
# Set temperature range a... | github_jupyter |
## Test Riksdagen SFS dokument
* Denna [Jupyter Notebook](https://github.com/salgo60/open-data-examples/blob/master/Riksdagens%20dokument%20SFS.ipynb)
* [KU anmälningar](https://github.com/salgo60/open-data-examples/blob/master/Riksdagens%20dokument%20KU-anm%C3%A4lningar.ipynb)
* [Motioner](https://github.com/sa... | github_jupyter |
```
import os
import numpy as np
from mnucosmomap import util as UT
from mnucosmomap import catalogs as mNuCats
%load_ext line_profiler
fullbox = mNuCats.mNuICs(1, sim='paco')
x, y, z = fullbox['Position'].T
vx, vy, vz = fullbox['Velocity'].T
nside = 8
L_subbox = 1000./float(nside) # L_subbox
L_res = 1000./512.
L_hal... | github_jupyter |
<h1>Lists in Python</h1>
<p><strong>Welcome!</strong> This notebook will teach you about the lists in the Python Programming Language. By the end of this lab, you'll know the basics list operations in Python, including indexing, list operations and copy/clone list.</p>
<h2>Table of Contents</h2>
<div class="alert ale... | github_jupyter |
```
! wget http://corpora.linguistik.uni-erlangen.de/someweta/german_web_social_media_2018-12-21.model -P /mnt/data2/ptf
from someweta import ASPTagger
model = "/mnt/data2/ptf/german_web_social_media_2018-12-21.model"
# future versions will have sensible default values
asptagger = ASPTagger(beam_size=5, iterations=1... | github_jupyter |
```
import sys
import tensorflow as tf
from sklearn.datasets import load_boston
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('dark_background')
boston = load_boston()
''' THIS is how you print the name of a function from within the function
print(sys._getframe().f_code.co_name)
'''#print([x fo... | github_jupyter |
# Import the data
```
import pandas as pd
import numpy as np
import networkx as nx
import statsmodels
import statsmodels.api as sm
import scipy.stats as stats
import matplotlib.pyplot as plt
# import the csv file with JUST the politicians post
comDB = pd.read_csv(r"/Users/tassan-mazzoccoarthur/Desktop/NETWORK SCI... | github_jupyter |
```
import glob
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.discriminant_analysis... | github_jupyter |
## Make your own heatmap based on Strava activities
This notebook shows you how to create your own heatmap based on your Strava activities.
You need to create a Strava API application in order to use their API. Follow the instructions on this page to create your app: <https://medium.com/@annthurium/getting-started-wit... | github_jupyter |
# <p style="text-align: center;"> Part Two: Scaling & Normalization </p>
```
from IPython.display import HTML
from IPython.display import Image
Image(url= "https://miro.medium.com/max/3316/1*yR54MSI1jjnf2QeGtt57PA.png")
HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide();... | github_jupyter |
# Implementing a Route Planner
In this project you will use A\* search to implement a "Google-maps" style route planning algorithm.
## The Map
```
# Run this cell first!
from helpers import Map, load_map_10, load_map_40, show_map
import math
%load_ext autoreload
%autoreload 2
```
### Map Basics
```
map_10 = load_... | github_jupyter |
# Structured and time series data
This notebook contains an implementation of the third place result in the Rossman Kaggle competition as detailed in Guo/Berkhahn's [Entity Embeddings of Categorical Variables](https://arxiv.org/abs/1604.06737).
The motivation behind exploring this architecture is it's relevance to re... | github_jupyter |
# Classifying Images with a NN and DNN Model
## Introduction
In this notebook, you learn how to build a neural network to classify the tf-flowers dataset using a Deep Neural Network Model.
## Learning Objectives
* Define Helper Functions.
* Train and evaluate a Neural Network (NN) model.
* Train and evaluate a Deep... | github_jupyter |
```
import sys
sys.path.append('../Scripts')
from capstone_functions import *
```
## Gradient Descent exploration
### This notebook has many example of running gradient descent with different hyper parameters
### Epoch Choice
This calls our main pipeline function that loads raw data performs all adjustments and crea... | github_jupyter |
# Naive Bayes from scratch
```
import pandas as pd
import numpy as np
def get_accuracy(x: pd.DataFrame, y: pd.Series, y_hat: pd.Series):
correct = y_hat == y
acc = np.sum(correct) / len(y)
cond = y == 1
y1 = len(y[cond])
y0 = len(y[~cond])
print(f'Class 0: tested {y0}, correctly classified {co... | github_jupyter |
# Create simple CNN network
Import all important libraries
```
import tensorflow as tf
from tensorflow import keras
import matplotlib as plt
import pandas as pd
# Import stuff fof preprocessing
from tensorflow.keras.preprocessing.image import ImageDataGenerator
```
Now load the IDmap
```
IDmap = {} # id: hand gest... | github_jupyter |
## Import dependencies
```
import numpy as np
import sys
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import seaborn as sn
import scipy as sp
from tqdm import tqdm
import glob
from fair import *
from fair.scripts.data_retrieval impo... | github_jupyter |
```
%reload_ext autoreload
%autoreload 2
%matplotlib inline
```
### Image Generation from Audio
```
from pathlib import Path
from IPython.display import Audio
import librosa
import librosa.display
import matplotlib.pyplot as plt
import numpy as np
from utils import read_file, transform_path
DATA = Path('data')
# the... | github_jupyter |
```
import os
import pandas as pd
from pandas_profiling import ProfileReport
from pandas_profiling.utils.cache import cache_file
from collections import Counter
import seaborn as sn
import random
import statistics
import statsmodels.api as sm
import numpy as np
box_file_dir = os.path.join(os.getcwd(), "..", "..", "... | github_jupyter |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.