text stringlengths 2.5k 6.39M | kind stringclasses 3
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|---|---|
```
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)... | github_jupyter |
# exma quick start
In this tutorial we will take the typical molecular dynamics case of a Lennard-Jones (LJ) fluid, in its solid phase and in its liquid phase, and we will see how to obtain different properties of them using this library.
This first part of the code will be common to all three sections. We are going ... | github_jupyter |
<h1> Scaling up ML using Cloud ML Engine </h1>
In this notebook, we take a previously developed TensorFlow model to predict taxifare rides and package it up so that it can be run in Cloud MLE. For now, we'll run this on a small dataset. The model that was developed is rather simplistic, and therefore, the accuracy of ... | github_jupyter |
# Train-validation tagging
This notebook shows how to split a training dataset into train and validation folds using tags
**Input**:
- Source project
- Train-validation split ratio
**Output**:
- New project with images randomly tagged by `train` or `val`, based on split ration
## Configuration
Edit the following s... | github_jupyter |
## Train GPT on addition
Train a GPT model on a dedicated addition dataset to see if a Transformer can learn to add.
```
# set up logging
import logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
# ma... | github_jupyter |
# Demo 1: a demo based on visual-92-categories-task MEG data
Here is a demo based on the publicly available visual-92-categories-task MEG datasets. (Reference: Cichy, R. M., Pantazis, D., & Oliva, A. “Resolving human object recognition in space and time.” Nature neuroscience (2014): 17(3), 455-462.) MNE-Python has bee... | github_jupyter |
# Detailed Steps Example
#### This notebook demonstrates how the data cleaning, peak fitting and descriptors generation works step by step, serving as a detailed example of the `ProcessData_PlotDescriptors_Examples.ipynb`.
## Packages and Needed Python Files Preparation
### First we import packages we need:
```
impo... | github_jupyter |
# Indexing
Okay guys today's lecture is indexing.
> What is indexing?
At heart, indexing is the ability to inspect a value inside a object. So basically if we have a list, X, of 100 items and our index is 'i' then 'i of X' returns the *ith value* inside the list (p.s. we can index strings too).
Okay, so what is t... | github_jupyter |
```
from fastai.text.all import *
chunked??
```
Let's look at how long it takes to tokenize a sample of 1000 IMDB review.
```
path = untar_data(URLs.IMDB_SAMPLE)
df = pd.read_csv(path/'texts.csv')
df.head(2)
ss = L(list(df.text))
ss[0]
```
We'll start with the simplest approach:
```
def delim_tok(s, delim=' '): ret... | github_jupyter |
# Lab 05 : Train with mini-batches -- solution
```
# For Google Colaboratory
import sys, os
if 'google.colab' in sys.modules:
# mount google drive
from google.colab import drive
drive.mount('/content/gdrive')
# find automatically the path of the folder containing "file_name" :
file_name = 'minibatc... | github_jupyter |
# Lab 3 - Distance Metrics and Clustering
### Non-Euclidean Distance Metrics
We are most familiar with the typical Euclidian distance metric, ie: given two vectors $\overline{v_1} = [x_1, y_1]$ and $\overline{v_2} = [x_2, y_2]$, the distance $D$ between them is $\sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2}$. This is generali... | github_jupyter |
# Deep $Q$-learning
In this notebook, we'll build a neural network that can learn to play games through reinforcement learning. More specifically, we'll use $Q$-learning to train an agent to play a game called [Cart-Pole](https://gym.openai.com/envs/CartPole-v0). In this game, a freely swinging pole is attached to a c... | github_jupyter |
## Week 2 Optional Thoery Discussion
The following problems are for those of you looking to challenge yourself beyond the required problem sets and programming questions. Most of these have been given in Stanford's CS161 course, Design and Analysis of Algorithms, at some point. They are completely optional and will no... | github_jupyter |
# Multi-Label Classification Tutorial
This tutorial shows how to use Tribuo's MultiLabel package to perform [multi-label classification](https://en.wikipedia.org/wiki/Multi-label_classification) tasks. Multi-label classification is the task of assigning a *set* of labels to a given example from a specific label domain... | github_jupyter |
# Copying an ArcGIS StoryMap item to another organization
## Introduction
Esri provides two models for telling stories with maps: The [Classic Story Map](https://storymaps-classic.arcgis.com/en/) and the newer [ArcGIS StoryMap](https://www.esri.com/en-us/arcgis/products/arcgis-storymaps/overview). Each offers the inf... | github_jupyter |
# 实战 Kaggle 比赛：图像分类 (CIFAR-10)
:label:`sec_kaggle_cifar10`
之前几节中，我们一直在使用深度学习框架的高级API直接获取张量格式的图像数据集。
但是在实践中，图像数据集通常以图像文件的形式出现。
在本节中，我们将从原始图像文件开始，然后逐步组织、读取并将它们转换为张量格式。
我们在 :numref:`sec_image_augmentation`中对CIFAR-10数据集做了一个实验。CIFAR-10是计算机视觉领域中的一个重要的数据集。
在本节中，我们将运用我们在前几节中学到的知识来参加CIFAR-10图像分类问题的Kaggle竞赛，(**比赛的网址是https://ww... | github_jupyter |
# Assignment 1
Welcome to the first programming assigment for the course. This assignments will help to familiarise you with qiskit while revisiting the topics discussed in this week's lectures.
### Submission Guidelines
For final submission, and to ensure that you have no errors in your solution, please use the 'Res... | github_jupyter |
```
import pandas as pd
import numpy as np
import csv
f = open('find inter thres_300.csv', 'rt')
reader = csv.reader(f)
data_list = []
for line in reader:
data_list.append(line)
f.close()
find_inter_thres_300 = pd.DataFrame(data_list)
new_col = ['rep', 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55... | github_jupyter |
### Regression results
```
import numpy as np
import tensorflow as tf
tf.logging.set_verbosity(tf.logging.ERROR)
import tensorflow.contrib.slim as slim
import matplotlib.pyplot as plt
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
from cn_reg_class import cn_reg_class
from mlp_reg_class import mlp... | github_jupyter |
```
from mayavi import mlab
mlab.init_notebook()
from pyscf import gto, scf, lo
import numpy as np
from functools import reduce
from pyscf.lo.orth import pre_orth_ao_atm_scf
import ase, scipy
from pyscf import lo
import itertools as itl
import ase.visualize as av
T,F=True,False
np.set_printoptions(precision=2,suppress... | github_jupyter |
## Text Classification using torchflare.
***
* Dataset: https://www.kaggle.com/columbine/imdb-dataset-sentiment-analysis-in-csv-format
```
import pandas as pd
import numpy as np
import torch
import torch.nn as nn
from sklearn.model_selection import train_test_split
import transformers
import torchflare.metrics as m... | github_jupyter |
<img src="https://raw.githubusercontent.com/google/jax/main/images/jax_logo_250px.png" width="300" height="300" align="center"/><br>
Welcome to another JAX tutorial. I hope you all have been enjoying the JAX Tutorials so far. We have already completed three tutorials on JAX each of which introduced an important concep... | github_jupyter |
# pyGemPick Tutorial 3: Outputing Detected Gold Particle Centers
## How to Output Gold Particle Centers To Use In Future Spatial-Statistical Analysis of Gold Particle Cross Correlation
In this tutorial we'll be using the **bin2df( )** function found in the pygempick.spatialstats package to record the x,y centers of e... | github_jupyter |
# <font color='blue'>Monte Carlo Simulation</font>
# <font color='blue'>Monte Carlo Simulation and Time Series for Financial Modeling</font>
### Loading the Packages
```
# Python Version
from platform import python_version
print('Python Version:', python_version())
# Imports for data manipulation
import numpy as np
... | github_jupyter |
## 2020년 2월 6일 금요일
### 백준 6588번: 골드바흐의 추측문제
### 문제 : https://www.acmicpc.net/problem/6588
### 블로그 : https://somjang.tistory.com/entry/BaeKJoon-6588%EB%B2%88-%EA%B3%A8%EB%93%9C%EB%B0%94%ED%9D%90%EC%9D%98-%EC%B6%94%EC%B8%A1-%EB%AC%B8%EC%A0%9C-%ED%92%80%EC%9D%B4
### 첫번째 시도
먼저 입력 받은 수보다 작은 소수를 모두 구하고 가장 큰 소수와 가장 작은 소수와 더한... | github_jupyter |
# Transforms and Multi-Table Relational Databases
* This notebook shows how to run transforms directly on a mutli-table relational database while keeping the referential integrity of primary and foreign keys intact.
* This notebook also contains instructions on how to transform data residing in CSV files.
* Primary and... | github_jupyter |
<a href="https://colab.research.google.com/github/yohanesnuwara/reservoir-engineering/blob/master/Unit%203%20Reservoir%20Statics/notebook/3_examples.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# **Unit 3 Reservoir Statics (Examples)**
```
impor... | github_jupyter |
# Fitting BERT Classifier to Twitter MBTI
```
import tensorflow as tf
import torch
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from sklearn.model_selection import train_test_split
from transformers import BertTokenizer, BertConfig
from transformers import AdamW, BertForSequ... | github_jupyter |
```
import pandas as pd
import numpy as np
import seaborn as sns
from google.colab import drive
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import classification_report, plot_confu... | github_jupyter |
```
# from google.colab import drive
# drive.mount('/content/drive')
import torch.nn as nn
import torch.nn.functional as F
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import torch
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import Dataset, DataLoader... | github_jupyter |
# Josephson Junction QComponent Demo Notebook
This demo notebook describes two types of Josephson Junction (JJ) qcomponents available in Qiskit Metal, including a "Manhattan"-style JJ and a "Dolan"-style JJ. In addition, we demonstrate how to insert these realistic JJ structures in between the capacative pads of the t... | github_jupyter |
# データサイエンス100本ノック（構造化データ加工編） - SQL
## はじめに
- データベースはPostgreSQL13です
- 初めに以下のセルを実行してください
- セルに %%sql と記載することでSQLを発行することができます
- jupyterからはdescribeコマンドによるテーブル構造の確認ができないため、テーブル構造を確認する場合はlimitを指定したSELECTなどで代用してください
- 使い慣れたSQLクライアントを使っても問題ありません（接続情報は以下の通り）
- IPアドレス：Docker Desktopの場合はlocalhost、Docker toolboxの場合は192.168.99.1... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
# %matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
import src.solver_helper as helper
from src.traffic_world import TrafficWorld
from src.car_plotting_multiple import plot_multiple_cars, plot_cars, animate, plot_single_frame
from src.multiagent_mpc import Mu... | github_jupyter |
```
import os
import sys
import gym
from gym import wrappers, logger
import gridworld
import random
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use("TkAgg")
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from collections import nam... | github_jupyter |
# Getting started with ReColorAdv
This file contains instructions for experimenting with the ReColorAdv attack, by itself and combined with other attacks. This tutorial is based on the [first tutorial](https://github.com/revbucket/mister_ed/blob/master/notebooks/tutorial_1.ipynb) of `mister_ed`. See the README to make ... | 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 |
# Classical Music Recommendation Playground
This notebook will show how to implement simple recommender system follwing two different approaches: **Collaborative Filtering** (user based) and **Content Based** recommendation.
> DISCLAIMER:
> The used dataset is NOT a real dataset, but it has been artificially generate... | github_jupyter |
```
import numpy as np
class LinearRegression:
def __init__(self, fit_intercept=True):
self.coef_ = None
self.intercept_ = None
self._fit_intercept = fit_intercept
def fit(self, X, y):
"""Fit model coefficients.
Arguments:
X -- 1D or 2D numpy array
... | github_jupyter |
```
import numpy
import imp
from re import sub
import logging
from datetime import datetime, timedelta
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import os
try:
changed
except NameError:
os.chdir('..')
os.system('find -name *.pyc | xargs rm')
changed = True
import sys
std... | github_jupyter |
```
import pandas as pd
import numpy as np
import os
import re
```
# Encoding of categorical variables
In this notebook, we will present typical ways of dealing with
**categorical variables** by encoding them, namely **ordinal encoding** and
**one-hot encoding**.
Let us first load the entire adult dataset containin... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df=pd.read_csv('only_road_accidents_data3.csv')
df.head()
print(df['STATE/UT'].unique())
df2=df[df.columns[2:10]].sum(axis=0)
#print(df2)
df2.plot.pie(title='Time slot distribution of all accidents in India(2001-14)',autopc... | github_jupyter |
# Description
A Colab notebook for generating images using OpenAI's CLIP model.
Heavily influenced by Alexander Mordvintsev's Deep Dream, this work uses CLIP to match an image learned by a SIREN network with a given textual description.
As a good launching point for future directions and to find more related w... | github_jupyter |
## Load Data
```
!wget http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
!tar xfz ./aclImdb_v1.tar.gz
import os
import numpy as np
def get_files(dir):
return [dir + d for d in os.listdir(dir) if os.path.isfile(dir + d)]
train_dir = '/content/aclImdb/train/'
test_dir = '/content/aclImdb/test/'
train_p... | github_jupyter |
#Part 4 BERT for arithmetic sentiment analysis
Acknowledgement: We used most of the code from https://mccormickml.com/2019/07/22/BERT-fine-tuning/
Most Credit to:
Chris McCormick and Nick Ryan
# Bert Background
**B**idirectional **E**ncoder **R**epresentations from
**T**ransformers (BERT) [Devlin et al., 2019]... | github_jupyter |
## 第4章 Matplotlibでグラフを 描画しよう
### 4-7: 箱ひげ図
```
import matplotlib.pyplot as plt
# リスト4.7.1：箱ひげ図の描画
plt.style.use("ggplot")
x = [1, 2, 3, 3, 11, 20]
fig = plt.figure()
ax = fig.add_subplot(111)
ax.boxplot(x)
plt.show()
# リスト4.7.2：複数の箱ひげ図の描画
# 複数のリストをリストにセット
x = [[1, 2, 3, 3, 11, 20], [1, 2, 9, 10, 15, 16]]
labels = ["A... | github_jupyter |
# Stochastic Gradient Descent Regression
This Code template is for regression analysis using the simple SGDRegressor based on the Stochastic Gradient Descent approach.
### Required Packages
```
import warnings
import numpy as np
import pandas as pd
import seaborn as se
import matplotlib.pyplot as plt
from s... | github_jupyter |
<a href="https://colab.research.google.com/github/keithvtls/Numerical-Method-Activities/blob/main/Lecture/Week%2015%20-%20Numerical%20Integration/NuMeth_6_Numerical_Integration.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Numerical Integration
... | github_jupyter |
# Geopandas
```
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
import cartopy
# Cargar el mapa
mapa = gpd.read_file('data/provincias.geojson')
mapa.head(10)
mapa.plot()
natalidad = pd.read_csv('data/natalidad.csv')
natalidad.head()
mapa_data = pd.merge(mapa, natalidad, left_on='NAME_2', ri... | github_jupyter |
# Intro to Python Data Structures
Lists, Tuples, Sets, Dicts
(c) 2019 Joe James
## Sequences: String, List, Tuple
****
**indexing** - access any item in the sequence using its index.
Indexing starts with 0 for the first element.
```
# string
x = 'frog'
print (x[3])
# list
x = ['pig', 'cow', 'horse']
print (x[1]... | github_jupyter |
```
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models.vgg as vgg
import torchvision.models.resnet as resnet
import ocd
device = 'cuda' if torch.cuda.is_avail... | github_jupyter |
```
# Import pyNBS modules
from pyNBS import data_import_tools as dit
from pyNBS import network_propagation as prop
from pyNBS import pyNBS_core as core
from pyNBS import pyNBS_single
from pyNBS import consensus_clustering as cc
from pyNBS import pyNBS_plotting as plot
# Import other needed packages
import os
import t... | github_jupyter |
# 6 - Transformers for Sentiment Analysis
In this notebook we will be using the transformer model, first introduced in [this](https://arxiv.org/abs/1706.03762) paper. Specifically, we will be using the BERT (Bidirectional Encoder Representations from Transformers) model from [this](https://arxiv.org/abs/1810.04805) pa... | github_jupyter |
# FKLearn Tutorial:
* <font size="4"> FKlearn is the nubank functional library for Machine Learning </font>
* <font size="4"> It was created with the idea of scaling machine learning through the company by standardizing model development and implementing an easy interface to allow all users to develop the best prac... | github_jupyter |
```
%matplotlib inline
import os
import csv
import codecs
import numpy as np
import pandas as pd
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
from sklearn.utils import shuffle
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import log_loss
import datetime
seed = 111
np.rand... | github_jupyter |
```
# Install the latest Tensorflow version.
!pip3 install --quiet "tensorflow>=1.7"
# Install TF-Hub.
!pip3 install --quiet "tensorflow-hub>=0.7.0"
!pip3 install --quiet seaborn
from absl import logging
import tensorflow.compat.v1 as tf
import tensorflow_hub as hub
import matplotlib.pyplot as plt
import numpy as np
i... | github_jupyter |
# Machine Learning Engineer Nanodegree
## Supervised Learning
## Project 2: Building a Student Intervention System
Welcome to the second project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and it will be your job to implement the additional funct... | github_jupyter |
# Machine Learning Part I
## 1. Introduction
### 1.1 What is Machine Learning?
[Machine learning](https://en.wikipedia.org/wiki/Machine_learning) was described by Arthur Samuel as the "field of study that gives computers the ability to learn without being explicitly programmed".
The following three principles are c... | github_jupyter |
# CStreet: a computed <ins>C</ins>ell <ins>S</ins>tate <ins>tr</ins>ajectory inf<ins>e</ins>r<ins>e</ins>nce method for <ins>t</ins>ime-series single-cell RNA-seq data
## This is a tutorial written using Jupyter Notebook.
### Step 1. CStreet installation following the [tutorial](https://github.com/yw-Hua/CStreet).
#... | github_jupyter |
# RadarCOVID-Report
## Data Extraction
```
import datetime
import logging
import os
import shutil
import tempfile
import textwrap
import uuid
import dataframe_image as dfi
import matplotlib.ticker
import numpy as np
import pandas as pd
import seaborn as sns
%matplotlib inline
sns.set()
matplotlib.rcParams['figure.f... | github_jupyter |
# D&D Name generator
```
%matplotlib inline
import os
import sys
PROJECT_ROOT = os.path.dirname(os.getcwd())
sys.path.append(PROJECT_ROOT)
from collections import defaultdict
import numpy as np
import matplotlib.pyplot as plt
from data import DnDCharacterNameDataset
from train import RNNLayerTrainer
from generator ... | github_jupyter |
```
import json
import time
import sys
import pandas as pd
import numpy as np
import scipy.sparse as sp
import pickle as pkl
import collections
import gc
with open('../data/index_item_map.pkl', 'rb') as f:
data_map = pkl.load(f)
paper_id_title = data_map['paper_id_title']
author_id_name = data_map['author_id_name... | github_jupyter |
```
"""
Convert netCDF files to geotiff.
Update 2019 06 26: rerun using updated inundation layers
Author: Rutger Hofste
Date: 20180816
Kernel: python35
Docker: rutgerhofste/gisdocker:ubuntu16.04
Args:
TESTING (boolean) : Toggle testing mode
SCRIPT_NAME (string) : Script name
OUTPUT_VERSION (integer) : o... | github_jupyter |
# Temporal Difference: On-policy n-Tuple Sarsa, Stochastic
```
import numpy as np
```
## Create environment
```
def create_environment_states():
"""Creates environment states.
Returns:
num_states: int, number of states.
num_terminal_states: int, number of terminal states.
num_non_ter... | github_jupyter |
# Classification on Iris dataset with sklearn and DJL
In this notebook, you will try to use a pre-trained sklearn model to run on DJL for a general classification task. The model was trained with [Iris flower dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set).
## Background
### Iris Dataset
The dataset c... | github_jupyter |
<a href="https://colab.research.google.com/github/martin-fabbri/colab-notebooks/blob/master/deeplearning.ai/tf/trax_ner_reformer.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# NER Reformer
## Named Entity Recognition
Named-entity recognition ... | github_jupyter |
# Transfer Learning
In this notebook, you'll learn how to use pre-trained networks to solved challenging problems in computer vision. Specifically, you'll use networks trained on [ImageNet](http://www.image-net.org/) [available from torchvision](http://pytorch.org/docs/0.3.0/torchvision/models.html).
ImageNet is a m... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
a = np.zeros(3, dtype=int)
a
z = np.zeros(10)
print(z)
print(z.shape)
z.shape = (10,1)
print(z)
z = np.zeros(4)
z.shape = (2,2)
print(z)
z = np.zeros((2,2))
print(z)
z = np.ones((2,3))
print(z)
z = np.empty((3,3))
print(z)
z = np.identity(3)
print(z)
blalist = [222... | github_jupyter |
```
#IMPORT SEMUA LIBARARY
#IMPORT LIBRARY PANDAS
import pandas as pd
#IMPORT LIBRARY UNTUK POSTGRE
from sqlalchemy import create_engine
import psycopg2
#IMPORT LIBRARY CHART
from matplotlib import pyplot as plt
from matplotlib import style
#IMPORT LIBRARY BASE PATH
import os
import io
#IMPORT LIBARARY PDF
from fpdf im... | github_jupyter |
# ECCB2020 Tutorial T05: Computational modelling of cellular processes: regulatory vs metabolic systems
## Part 3: Introductions to constrainat-based modeling using cobrapy
### Instructors:
* Miguel Ponce de León from (Barcelona Supercomputing Center)
* Marta Cascante (Universidad de Barcelona)
1 Septembar, 2020
``... | github_jupyter |
# Particle Filter on Episode
千葉工業大学 上田 隆一
(c) 2017 Ryuichi Ueda
This software is released under the MIT License, see LICENSE.
## はじめに
このコードは、上田が https://link.springer.com/chapter/10.1007/978-3-319-48036-7_54 で公表した「particle filter on episode」というアルゴリズムです。簡単なタスクを学習できますが、まだ弱いです。
```
%matplotlib inline
import numpy as... | github_jupyter |
# Backtesting Sentiment Pairs
<b>Summary: </b>
<br>
For rolling average and rolling standard deviation of the lenght 7 days, NLP was calcualted for all possible combinations. A trading fee was assumed 0.0075 (taken from Bitmex). Calculations show that the best pairs are Bots/Whitepaper, Announcement/Bearish, Shilling/... | github_jupyter |
# Hybrid quantum-classical Neural Networks with PyTorch and Qiskit
Machine learning (ML) has established itself as a successful interdisciplinary field which seeks to mathematically extract generalizable information from data. Throwing in quantum computing gives rise to interesting areas of research which seek to leve... | github_jupyter |
```
!curl -o datasets 'https://ftp.ncbi.nlm.nih.gov/pub/datasets/command-line/LATEST/linux-amd64/datasets'
!curl -o dataformat 'https://ftp.ncbi.nlm.nih.gov/pub/datasets/command-line/LATEST/linux-amd64/dataformat'
!chmod +x datasets
!./datasets --help
!./datasets version
!./datasets download virus genome taxon SARS-CoV... | github_jupyter |
# 2A.algo - L'énigme d'Einstein et sa résolution
Résolution de l'énigme [L'énigme d'Einstein](http://fr.wikipedia.org/wiki/%C3%89nigme_d'Einstein). Implémentatin d'une solution à base de règles.
```
from io import StringIO
from pandas import read_csv
```
[L'énigme d'Einstein](http://fr.wikipedia.org/wiki/%C3%89nigme... | github_jupyter |
```
import os
import math
import pandas as pd
import numpy as np
import seaborn as sns
from pandas import datetime
from matplotlib import pyplot as plt
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import r2_score
## convert one to multiple series
de... | github_jupyter |
##### Copyright 2020 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
```
from pymongo import MongoClient
import pymatgen
import bson
import itertools
import numpy as np
import collections
from bson import ObjectId
from pymatgen.core.structure import Structure
from pymatgen.vis.structure_vtk import EL_COLORS
M = MongoClient()
distortions = M.ferroelectric_dataset.distortions
workflow_dat... | github_jupyter |
## Chapter 11.3
```
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional, Dropout
from tensorflow.k... | github_jupyter |
<img src="https://avatars.githubusercontent.com/u/74911464?s=200&v=4"
alt="OpenEO Platform logo"
style="float: left; margin-right: 10px;" />
## openEO Platform UC6 - Near Real Time Forest Dynamics
### Author michele.claus@eurac.edu
### Date: 2021/09/10
```
from eo_utils import *
```
## Connect to openEO
``... | github_jupyter |
# Introduction to Deep Learning with PyTorch
In this notebook, you'll get introduced to [PyTorch](http://pytorch.org/), a framework for building and training neural networks. PyTorch in a lot of ways behaves like the arrays you love from Numpy. These Numpy arrays, after all, are just tensors. PyTorch takes these tenso... | github_jupyter |
# Automated Air-Liquid Interface Cell Culture Analysis Using Deep Optical Flow
## Autogenerated Report: RAINBOW image series analysis results
#### Author: Alphons G
#### Website: https://github.com/AlphonsGwatimba/Automated-Air-Liquid-Interface-Cell-Culture-Analysis-Using-Deep-Optical-Flow
```
# import required packa... | github_jupyter |
```
from skimage import io
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from skimage.transform import pyramid_gaussian
from torch.autograd import Variable
from math import exp
device = torch.device("cuda:0" if torch.cu... | github_jupyter |
# Conservation Analysis and Epitope Prediction
#### Author: C. Mazzaferro, K. Fisch
#### Email: cmazzafe@ucsd.edu
#### Date: October 2016
## Outline of Notebook
<a id = "toc"></a>
1. <a href = "#background">Background</a>
2. <a href = "#Cons">High Affinity Binding Prediction </a>
* <a href = "#Agg">Data Aggrega... | github_jupyter |
# Setup
```
%%capture
%pip install poetry
%pip install git+https://github.com/oughtinc/ergo.git@f5646b672eb0d60c58e7de850eea5f43a4feaacc
%pip install xlrd
%load_ext google.colab.data_table
%%capture
import ergo
import numpy as np
import pandas as pd
import ssl
import warnings
import requests
from datetime import timed... | github_jupyter |
```
%matplotlib inline
import pymc3 as pm
import numpy as np
import matplotlib.pyplot as plt
import pystan
import pystan.chains
from collections import OrderedDict
import pandas as pd
plt.style.use('seaborn-darkgrid')
print('Runing on PyMC3 v{}'.format(pm.__version__))
```
# Effective sample size in PyStan
Referenc... | github_jupyter |
# Highly Performant TensorFlow Batch Inference on TFRecord Data Using the SageMaker CLI
In this notebook, we'll show how to use SageMaker batch transform to get inferences on a large datasets. To do this, we'll use a TensorFlow Serving model to do batch inference on a large dataset of images encoded in TFRecord forma... | github_jupyter |
# Read in redrock output for SV1 Blanc Deep Exposures
```
!pip install git+https://github.com/desi-bgs/bgs-cmxsv.git --upgrade --user
import numpy as np
import matplotlib.pyplot as plt
from bgs_sv import sv1
# get TileIDs of Blanc deep exposures
deep_exp = sv1.blanc_deep_exposures()
deep_exp
# get redrock zbest file ... | github_jupyter |
```
# !wget https://github.com/gouthamcm/recruit/raw/master/Entity%20Recognition%20in%20Resumes.tsv
# import pandas as pd
# df = pd.read_csv('/content/Entity Recognition in Resumes.tsv', sep='\t')
# df.head()
# from tqdm.notebook import tqdm
# ids = []
# for i, text in tqdm(enumerate(df.Abhishek)):
# if not str(... | github_jupyter |
# Auxiliary layers - DEV
Here we create
* a raster that is empty - is this useful ?
* a raster with the distance to the raster border - used for selecting pixels in a multi-tile project
* a raster with the distance to the polygon border - useful for selecting clean training samples
**TODO**: Create a Snakemake task... | github_jupyter |
**[Data Visualization: From Non-Coder to Coder Micro-Course Home Page](https://www.kaggle.com/learn/data-visualization-from-non-coder-to-coder)**
---
Now it's time for you to demonstrate your new skills with a project of your own!
In this exercise, you will work with a dataset of your choosing. Once you've selected... | github_jupyter |
# Weighted Least Squares
```
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import statsmodels.api as sm
from scipy import stats
from statsmodels.iolib.table import SimpleTable, default_txt_fmt
np.random.seed(1024)
```
## WLS Estimation
### Artificial data: Heteroscedasticity 2 groups
Model... | github_jupyter |
# **Introduction to Competitive Programming**
---
Date and Time: 8th July 2019 Monday 5-7pm
Venue: Matthews Bldg RM232
Handlers: Payton Yao (Canva), Kathrina Ondap (Google)
Coordinator: Luke Sy
Repository: https://github.com/ieeeunswsb/cpworkshop
```
print("Welcome to IEEE UNSW student branch's introduction to c... | github_jupyter |
# Elaborate statistics features for Silvereye
## Dependencies imports
```
import xarray as xr
import os
import sys
import pandas as pd
from functools import wraps
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns # noqa, pandas aware plotting library
from datetime import date
from dateutil.re... | github_jupyter |
# Convolutional Layer
In this notebook, we visualize four filtered outputs (a.k.a. activation maps) of a convolutional layer.
In this example, *we* are defining four filters that are applied to an input image by initializing the **weights** of a convolutional layer, but a trained CNN will learn the values of these w... | github_jupyter |
```
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
from sklearn.datasets import fetch_openml
import matplotlib.pyplot as plt
import time
import warnings
warnings.filterwarnings('ignore')
`... | github_jupyter |
## NLP model creation and training
```
from fastai.gen_doc.nbdoc import *
from fastai.text import *
```
The main thing here is [`RNNLearner`](/text.learner.html#RNNLearner). There are also some utility functions to help create and update text models.
## Quickly get a learner
```
show_doc(language_model_learner)
```... | github_jupyter |
# Exchanging assignment files manually
After an assignment has been created using `nbgrader generate_assignment`, the instructor must actually release that assignment to students. This page describes how to do that using your institution's existing learning management system, assuming that the students will fetch the ... | github_jupyter |
# Unit 5 - Financial Planning
```
# Initial imports
import os
import requests
import pandas as pd
from dotenv import load_dotenv
import alpaca_trade_api as tradeapi
from MCForecastTools import MCSimulation
import json
%matplotlib inline
# Load .env enviroment variables
load_dotenv()
```
## Part 1 - Personal Finance ... | github_jupyter |
# Example of optimizing a convex function
# Goal is to test the objective values found by Mango
- Search space size: Uniform
- Number of iterations to try: 40
- domain size: 5000
- Initial Random: 5
# Benchmarking test with different iterations for serial executions
```
from mango.tuner import Tuner
from scipy.stat... | 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">
# _*Qiskit Chemistry, Programmatic Approach*_
The latest version of this notebook is available on https://github.com/Qiskit/qiskit... | github_jupyter |
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