text stringlengths 2.5k 6.39M | kind stringclasses 3
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|---|---|
```
import os
import tifffile
import numpy as np
import phathom.phenotype.mesh as mesh
import phathom.phenotype.niche as niche
import matplotlib.pyplot as plt
working_dir = '/media/jswaney/SSD EVO 860/organoid_phenotyping/20181210_eF9_A34_2'
```
# Load centers and cell-type labels
```
centers_path = 'centers.npy'
sox... | github_jupyter |
# Feature Transformation with Scikit-Learn In This Notebook
## Saving Features into the SageMaker Feature Store
In this notebook, we convert raw text into BERT embeddings. This will allow us to perform natural language processing tasks such as text classification. We save the features into the SageMaker Feature Store... | github_jupyter |
<a href="https://colab.research.google.com/github/adasegroup/ML2021_seminars/blob/master/seminar7/seminar_GB.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Seminar: Gradient Boosting
Course: Machine Learning by professor Evgeny Burnaev
<br>
Autho... | github_jupyter |
# GEE score tests
This notebook uses simulation to demonstrate robust GEE score tests. These tests can be used in a GEE analysis to compare nested hypotheses about the mean structure. The tests are robust to miss-specification of the working correlation model, and to certain forms of misspecification of the variance... | github_jupyter |
```
import json
import altair as alt
from altair import expr, datum
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import requests
colors = ["#FFC759", "#FF7B9C", "#607196", "#BABFD1"]
alt.themes.enable("dark")
n = 24
w = 15
x = [i for i in range(8, n + 1)]
y = [w / (n / 24) * (n - i) for i in x... | github_jupyter |
```
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats
import datetime
import calendar
```
### variable
- PatientId - 환자를 식별할수 식별자
- AppointmentID - 예약의 식별자
- Gender = 성별 (여성의 비율이 크다, woman takes way more care of they health in comparison to man.)
- S... | github_jupyter |
# Pentode Modeling
* Model Parameter Extraction
* Model Parameter Verification
This experiment uses data extracted from a vacuum tube datasheet and scipy.optimize to calculate the [Child-Langmuir](http://www.john-a-harper.com/tubes201/) parameters used for circuit simulation.
$$I_a = K (V_{g1k} + D_{g2}V_{g2k} + D_aV... | github_jupyter |
# Read data and create timeseries using PICES LME
Look at SST, ocean currents, chl-a
```
import xarray as xr
import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import sys
import pandas as pd
sys.path.append('./../subroutines/')
import piceslocal
adir_data = './../data/'
```
## Read in da... | github_jupyter |
##### Copyright 2018 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
plt.rcParams["figure.figsize"] = (10, 6)
df = pd.read_csv('crawler/data/data.csv')
df = df[df['published_date'] < '2020-01-23'] # an article age more than 1 month (to stable ratings)
df['published_date'] = pd.to_d... | github_jupyter |
*Accompanying code examples of the book "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python" by [Sebastian Raschka](https://sebastianraschka.com). All code examples are released under the [MIT license](https://github.com/rasbt/deep-learning-book/blob/master/LICEN... | github_jupyter |
```
import gdal, osr
import numpy as np
from skimage.graph import route_through_array
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats
import os
import math
from osgeo import ogr
import fiona
def raster2array(rasterfn):
#print('converting raster to array...')
raster = gdal.Open(raster... | github_jupyter |
<hr>
# [cknowledge.org](http://cknowledge.org): Community-driven benchmarking and optimization of computing systems - from classical to quantum
<hr>
[Quantum Computing](https://github.com/ctuning/ck-quantum/wiki)
* [CK-QISKit](https://github.com/ctuning/ck-qiskit) (IBM)
* [CK-Rigetti](https://github.com/ctuning/ck-rig... | github_jupyter |
<a href="http://landlab.github.io"><img style="float: left" src="../../../landlab_header.png"></a>
# Component Overview: `DepthDependentTaylorDiffuser`
<hr>
<small>For more Landlab tutorials, click here: <a href="https://landlab.readthedocs.io/en/latest/user_guide/tutorials.html">https://landlab.readthedocs.io/en/lat... | github_jupyter |
```
# !wget https://raw.githubusercontent.com/huseinzol05/Malaya-Dataset/master/dictionary/dialect/kelantan.csv
import pandas as pd
kelantan = pd.read_csv('kelantan.csv')
import malaya
malays = malaya.texts._malay_words._malay_words
import re
from unidecode import unidecode
def cleaning(string):
string = unideco... | github_jupyter |
```
# Goal: Predict if an individual is currently diagnosed with Mental Health disorder based on participant answer.
# The machine learning algorithm use clean_machine_learning.csv as data entries.
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
import numpy as np
# Dependencies for interaction w... | github_jupyter |
## Area level model: empirical best linear unbiased predictor (EBLUP)
Small area estimation (SAE) are useful techniques when the sample sizes are not sufficient to provide reliable direct domain estimates given the sampling design. In this tutorial, the direct estimates refer to estimates obtained from the design-base... | github_jupyter |
```
# @title Copyright & License (click to expand)
# Copyright 2021 Google LLC
#
# 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 r... | github_jupyter |
# This notebook produces Figure 2, 3 and 4:
**Figure 2: CDF of pairwise cosine similarity of traffic profiles across <span style="color:blue; font-size:large">device types</span> (vertical lines denote medians.)
Figure 3: CDF of pairwise cosine similarity of traffic profiles in <span style="color:blue; font-size:larg... | github_jupyter |
# Panel Data Models
---
```
import pandas as pd
import numpy as np
import statsmodels.api as sm
from linearmodels import PooledOLS
from linearmodels import RandomEffects
from linearmodels import PanelOLS
from linearmodels import FirstDifferenceOLS
from stargazer.stargazer import Stargazer
# Import crime data from N... | github_jupyter |
<a href="https://colab.research.google.com/github/hila-chefer/Transformer-Explainability/blob/main/Transformer_explainability.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# **Transformer Interpretability Beyond Attention Visualization**
```
!git... | github_jupyter |
# Performance Overview
Here, we will example the performance of FNGS as a function of time on several datasets. These investigations were performed on a 4 core machine (4 threads) with a 4.0 GhZ processor. These investigations were performed on the version of FNGS in ndmg/eric-dev-gkiar-fmri on 03/27.
```
%%script fa... | github_jupyter |
# 12. 사이킷런으로 구현해 보는 머신러닝
**머신러닝의 다양한 알고리즘에 대해 알아보고 사이킷런 라이브러리 사용법을 익힙니다. 사이킷런에서 제공하는 모듈을 이해하고, 머신러닝에 적용해 봅니다.**
## 12-1. 들어가며
## 12-2. 머신러닝 알고리즘
## 12-3. 사이킷런에서 가이드하는 머신러닝 알고리즘
## 12-4. Hello Scikit-learn
```bash
$ pip install scikit-learn
```
```
import sklearn
print(sklearn.__version__)
```
## 12-5. 사이킷런의 주요 ... | github_jupyter |
<h1 align="center">Segmentation Evaluation</h1>
**Summary:**
1. SimpleITK supports two ways of combining expert segmentations to obtain a reference segmentation.
2. A variety of criteria used for evaluating a segmentation result are readily available or implemented in SimpleITK.
<u>Reference Segmentation</u>
Evalua... | github_jupyter |
# Spatial and temporal characteristics of a movement pattern
> Marcos Duarte
> Laboratory of Biomechanics and Motor Control ([http://demotu.org/](http://demotu.org/))
> Federal University of ABC, Brazil
The measurement of spatial and temporal characteristics of a movement pattern is an important resource for the ... | github_jupyter |
```
import numpy as np
from lapy import TetMesh, TetIO, FuncIO
from lapy.Plot import plot_tet_mesh
import plotly
plotly.offline.init_notebook_mode(connected=True)
T = TetIO.import_vtk('../data/cubeTetra.vtk')
#T.is_oriented()
T.orient_()
from lapy import Solver
fem = Solver(T,lump=True)
evals, evec = fem.eigs(10)
#... | github_jupyter |
```
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
%matplotlib inline
import seaborn as sns
import re
data = pd.read_csv("./dataset/final_training.csv") ## path
data.head()
data.drop(["Unnamed: 0"],axis=1,inplace=True)
data.describe()
```
#### 1 for toxic 0 for normal
```
toxic = data[da... | github_jupyter |
```
#mounting drive
from google.colab import drive
drive.mount('/content/drive')
#importing required lib
import nltk
from nltk.tokenize import sent_tokenize, word_tokenize
nltk.download('punkt')
nltk.download('wordnet')
import numpy as np
import random
import string
import warnings
warnings.simplefilter("ignore")
text... | github_jupyter |
# Linear SVM Model for model analysis
```
import pandas as pd
import numpy as np
import re
from tqdm import tqdm
import warnings
warnings.filterwarnings("ignore")
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text impor... | github_jupyter |
<a href="https://colab.research.google.com/github/GoogleCloudPlatform/training-data-analyst/blob/master/courses/fast-and-lean-data-science/02_Dataset_playground.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## Imports
```
import os, math
import n... | github_jupyter |
<!--BOOK_INFORMATION-->
<img align="left" style="padding-right:10px;" src="figures/PDSH-cover-small.png">
*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/Pytho... | github_jupyter |
#Imports/Permissions to load data
```
# Python ≥3.5 is required
import sys
assert sys.version_info >= (3, 5)
# Scikit-Learn ≥0.20 is required
import sklearn
assert sklearn.__version__ >= "0.20"
# Common imports
import numpy as np
import os
# to make this notebook's output stable across runs
np.random.seed(42)
# To... | github_jupyter |
# Getting started with DeBaCl
## 1. Create some data
Our first step is to create some data using the scikit-learn `make_blobs` and `make_circles` utility. To make this a hard (but not impossible) clustering problem, we set the random state of the blob so that it's always outside the two concentric circles.
```
impor... | github_jupyter |
<!--BOOK_INFORMATION-->
<img align="left" style="padding-right:10px;" src="figures/PDSH-cover-small.png">
*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/Pytho... | github_jupyter |
```
import pandas as pd
pd.set_option('display.max_columns', None)
df = pd.read_csv('https://raw.githubusercontent.com/niravjdn/Software-Measurement-Project/master/data/pit/lang/mutations.csv', error_bad_lines=False, names = ["Class", "Package", "gc1", "gc2","gc3","Coverage","gc4"])
df.head()
df.drop('gc1', axis=1, inp... | github_jupyter |
<a href="https://colab.research.google.com/github/Kanghee-Lee/Mask-RCNN_TF/blob/master/Mask_RCNN(RPN).ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
#Imports
import numpy as np
import os
import glob
import cv2
!pip install xmltodict
import xmlt... | github_jupyter |
**3장 – 분류**
_이 노트북은 3장의 모든 샘플 코드와 연습 문제 정답을 담고 있습니다._
<table align="left">
<td>
<a target="_blank" href="https://colab.research.google.com/github/rickiepark/handson-ml2/blob/master/03_classification.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />구글 코랩에서 실행하기</a>
</td>
</table>
# 설정... | github_jupyter |
```
from qiskit.aqua.algorithms import VQE, NumPyEigensolver
import matplotlib.pyplot as plt
import numpy as np
from qiskit.chemistry.components.variational_forms import UCCSD
from qiskit.chemistry.components.initial_states import HartreeFock
from qiskit.circuit.library import EfficientSU2
from qiskit.aqua.components.o... | github_jupyter |
```
!pip install dynet
!git clone https://github.com/neubig/nn4nlp-code.git
from __future__ import print_function
import time
from collections import defaultdict
import random
import math
import sys
import argparse
import dynet as dy
import numpy as np
# format of files: each line is "word1|tag1 word2|tag2 ..."
train... | github_jupyter |
```
import pandas as pd
import numpy as np
import seaborn as sb
import matplotlib.pyplot as plt
import xgboost as xgb
from xgboost.sklearn import XGBClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn import model_selection, metrics
from sklearn import tree
from sklearn.metrics import accuracy_s... | github_jupyter |
# Node representation learning with GraphSAGE and UnsupervisedSampler
<table><tr><td>Run the latest release of this notebook:</td><td><a href="https://mybinder.org/v2/gh/stellargraph/stellargraph/master?urlpath=lab/tree/demos/embeddings/graphsage-unsupervised-sampler-embeddings.ipynb" alt="Open In Binder" target="_par... | github_jupyter |
# 1. FeatureExtraction_data
Reference:
- https://www.kaggle.com/asraful70/talkingdata-new-features-in-lightgbm-lb-0-9784
- https://www.kaggle.com/danieleewww/talkingdata-added-new-features-in-lightg-50cf9b/code
- https://www.kaggle.com/anttip/talkingdata-wordbatch-fm-ftrl-lb-0-9769
- https://www.kaggle.com/pranav84/ta... | github_jupyter |
```
from reconciler import reconcile
import pandas as pd
original_data = "../../analysis/data/panglaodb/"
results_path = "../../analysis/results/true_matches/"
original_cells_organs = pd.read_csv(f"{original_data}cells_organs_germlayers.csv")
original_cells_organs.head()
cell_types = original_cells_organs["cell_type... | github_jupyter |
# Exploring routing tools for calculating paths for a set of origin-destination pairs.
In this notebook, I explore OSRM service, OSMnx python library, and googlemaps python library (requests made to Google Maps Directions API) for computing routes and corresponding travel times and distances for a set of origin and de... | github_jupyter |
<a href="https://colab.research.google.com/github/pb111/Python-tutorials-and-projects/blob/master/Python_List_Comprehension.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# **Python List Comprehension**
- In this article, we will learn about Pytho... | github_jupyter |
# Betting Framework
In this notebook, I am evaluating the score prediction models versus the betting odds of bookmakers. I identify the best betting opportunity (the one with the highest expected value) and then place a bet using risk management: using the Kelly criterion, I am riding the maximum growth curve.
```
im... | github_jupyter |
# End-to-End FINN Flow for a Simple Convolutional Net
-----------------------------------------------------------------
In this notebook, we will go through the FINN steps needed to take a binarized convolutional network all the way down to a heterogeneous streaming dataflow accelerator running on the FPGA.
It's rec... | github_jupyter |
# Gaussian XOR and Gaussian R-XOR Experiment with Task Unaware Settings
```
# import dependencies
import numpy as np
import random
from proglearn.sims import generate_gaussian_parity
import matplotlib.pyplot as plt
import seaborn as sns
# functions to perform the experiments in this notebook
import functions.xor_rxor... | github_jupyter |
```
# NLTK 토큰화 하기
import nltk
# NLTK 다운
nltk.download('punkt')
sent = "don't worry, be happy. please wake up everbody come on"
from nltk.tokenize import word_tokenize
print(word_tokenize(sent))
word_tokenize(sent)
from nltk.tokenize import wordpunct_tokenize
print(wordpunct_tokenize(sent))
!pip install konlpy
from ... | github_jupyter |
```
########################################################################################################################
# Filename: RNN_Models.ipynb
#
# Purpose: Multi-label Text-categorization via recurrent neural networks
# Author(s): Bobby (Robert) Lumpkin
#
# Library Dependencies: numpy, pandas, scikit-learn,... | 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 |
```
#------------------------------------------------------------------------------------
import numpy as np
import pycuda.gpuarray as gpuarray
from pycuda.tools import make_default_context
import matplotlib as matplotlib
import pylab as plt
from mpl_toolkits.mplot3d import Axes3D
#-----------------------------------... | github_jupyter |
# Simple MIDI Chorder
***
### A simple, yet very capable MIDI chords detector and annotator
***
### Based upon Yating Music repo/code:
https://github.com/YatingMusic/compound-word-transformer
### And chorder repo/code by Joshua Chang:
https://github.com/joshuachang2311/chorder
***
#### Project Los Angeles
##... | github_jupyter |
*Accompanying code examples of the book "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python" by [Sebastian Raschka](https://sebastianraschka.com). All code examples are released under the [MIT license](https://github.com/rasbt/deep-learning-book/blob/master/LICEN... | github_jupyter |
<a href="https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/lvm/dcgan_fashion_tf.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Deep convolutional generative adversarial networks (DCGAN)
This tutorial fits a DC-GAN to... | github_jupyter |
```
%matplotlib inline
```
Transfer Learning Tutorial
==========================
**Author**: `Sasank Chilamkurthy <https://chsasank.github.io>`_
In this tutorial, you will learn how to train your network using
transfer learning. You can read more about the transfer learning at `cs231n
notes <http://cs231n.github.io/... | github_jupyter |
# Random Signals and LTI-Systems
*This jupyter notebook is part of a [collection of notebooks](../index.ipynb) on various topics of Digital Signal Processing. Please direct questions and suggestions to [Sascha.Spors@uni-rostock.de](mailto:Sascha.Spors@uni-rostock.de).*
## Introduction
The response of a system $y[k] ... | github_jupyter |
# Worksheet A-5: Working With Factors & Tibble Joins
## Getting Started
Load the requirements for this worksheet:
```
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(tsibble))
suppressPackageStartupMessages(library(gapminder))
suppressPackageStartupMessages(library(testthat)... | github_jupyter |
This notebook was prepared by [Donne Martin](https://github.com/donnemartin). Source and license info is on [GitHub](https://github.com/donnemartin/interactive-coding-challenges).
# Challenge Notebook
## Problem: Find the lowest common ancestor in a binary tree.
* [Constraints](#Constraints)
* [Test Cases](#Test-Cas... | github_jupyter |
# Fit NED to Pixels Map
This is an example of scaling local North East coordinates to pixel to plot on a sattlelite Google Map Image to
```
%matplotlib inline
# Import important libraries
import matplotlib.pyplot as plt
from math import cos, sin, pi, sqrt, atan2, degrees, hypot, pi
# Plotting the original top view im... | github_jupyter |
```
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import pickle
import random
from tqdm.notebook import tqdm
from sklearn.metrics import roc_curve, auc
from sklearn.preprocessing import label_binarize
from skimage.transform import resize
from skimage.io import imread
from skimage import col... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import os
import os.path as Path
data_dir = Path.join('..','data')
url = Path.join(data_dir,'raw','hvc_annotations.csv')
url
# if Path.isfile(url):
# df = pd.read_csv(url)
# df.head(2)
try:
... | github_jupyter |
```
import (
"fmt"
"os"
"bufio"
"strings"
"strconv"
)
const numVertices int = 10
type Adjacency struct {
a string
b string
}
func loadData() (map[string]int, []Adjacency) {
f, err := os.Open("input_data")
input := bufio.NewReader(f)
scanner := bufio.NewScanner(input)
vert... | github_jupyter |
## En este ejercicio vamos a clusterizar con kmeans ##
**Aprendizaje no supervisado** es aquel en el que no sabemos nada sobre los datos.
**Clusterizar** es hallar grupos de clases de iguales, dentro de un dataset.
<div class="alert alert-block alert-info">
Para clusterizar, se suele usar el algoritmo del kmea... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
import pandas as pd
import sys
import exman
def last(s):
return s.values[-1]
from itertools import product
def plot_alot(x, ys, data, hue, col, row, ylims=None, ylabel=None, std=True):
row_grid = sorte... | github_jupyter |
```
import csv
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from google.colab import files
```
The data for this exercise is available at: https://www.kaggle.com/datamunge/sign-language-mnist/home
Sign up and download to find 2 CSV files: sign_mnist_te... | github_jupyter |
```
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tqdm
import wandb
import torch
from torch import nn, optim
import torch.nn.functional as F
from torchvision import datasets, transforms
mode = 'disabled'
# mode = 'online'
wandb.init(project='leafsnap', entity='dianna-ai', mod... | github_jupyter |
#Titanic: Machine Learning from Disaster
Olivier RISSER-MAROIX (VieVie31)
```
import graphlab as gl
data_train = gl.load_sframe("train.csv")
data_test = gl.load_sframe("test.csv")
data_train.head(3)
```
##Cleanning trainning data
```
data_train["male"] = data_train["Sex"] == "male"
data_train["female"] = data_train[... | github_jupyter |
I dati utilizzati in questo notebook sono stati presi dalla competizione di Kaggle [Twitter Sentiment Analysis](https://www.kaggle.com/c/twitter-sentiment-analysis2).
# Analisi del sentimento
## Indice
1. [Twitter Sentiment Analysis](#twitter)<br>
1.1 [Descrizione](#descrizione)<br>
2. [Analisi lessicale](#lessi... | github_jupyter |
## 1. Setting up the environment
```
import numpy as np
import gym
#pytorch
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.distributions import Normal
# setting manual seed
torch.manual_seed(0)
from unityagents import UnityEnvironment
#matplotlib
import mat... | github_jupyter |
<a href="https://colab.research.google.com/github/ahmedhisham73/deep_learningtuts/blob/master/deeplearningtutorialCNN.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import tensorflow as tf
mnist = tf.keras.datasets.fashion_mnist
(training_image... | github_jupyter |
# STATE Data Transformation
Task: use Pandas to transform csv files into DataFrames that match desired tables for database schema
Tables:
- STATE (done)
- STATE_DATES (done)
- STATE_CONTIGUITY (done)
- STATE_RESOURCE (done)
```
import pandas as pd
import numpy as np
!ls SourceData/CorrelatesOfWar/
```
## Create 'S... | github_jupyter |
### Deep Kung-Fu with advantage actor-critic
In this notebook you'll build a deep reinforcement learning agent for Atari [Kung-Fu Master](https://gym.openai.com/envs/KungFuMaster-v0/) that uses a recurrent neural net.
- Container unpacking into function ... | github_jupyter |
**Group Members**\
**Cherukuri Nikhilesh - S20180010040**\
**Kore Nithish Kumar - S20180010086**\
**Pulla Nagendra Babu - S20180010138**\
**Rishab Tripati - S20180010147**
```
import pandas as pd
import statistics
import numpy as np
import matplotlib.pyplot as plt
from pandas.plotting ... | github_jupyter |
```
import pandas as pd
from numpy import array
from numpy import argmax
from keras.utils import to_categorical
user_features=pd.read_csv("users.csv")
user_features["gender"][8]=="M"
for i in range(len(user_features["gender"])):
if(user_features["gender"][i]=="M"):
user_features["gender"][i]=0
else:
... | github_jupyter |
# User's Guide, Chapter 36: Clients and Weak References
This chapter explains some of the underlying aspects of `music21`'s functioning that may be helpful for someone doing advanced work in understanding how the system works.
## It pays to have good references
I've mentioned several times indirectly or directly the... | github_jupyter |
<!--
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pickle
import os
import random
from sklearn.metrics import balanced_accuracy_score, precision_score, recall_score, accuracy_score
!pip install -U mlxtend
from mlxtend.evaluate import confusion_matrix
from mlxtend.plotting import plot_conf... | github_jupyter |
```
# Erasmus+ ICCT project (2018-1-SI01-KA203-047081)
# Toggle cell visibility
from IPython.display import HTML
tag = HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide()
} else {
$('div.input').show()
}
code_show = !code_show
}
$( document... | github_jupyter |
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed u... | github_jupyter |
# Movie Recommendations using Content Based Filtering
In this kernel we'll be building a baseline Movie Recommendation System using TMDB 5000 Movie Dataset. For novices like me this kernel will pretty much serve as a foundation in recommendation systems and will provide you with something to start with.
In this ker... | github_jupyter |
# Introduction To GradCAM (Part 1) - Lecture Notebook
In this lecture notebook we'll be looking at an introduction to Grad-CAM, a powerful technique for interpreting Convolutional Neural Networks. Grad-CAM stands for Gradient-weighted Class Activation Mapping.
CNN's are very flexible models and their great predictive... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Visualization/nlcd_land_cover.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_blank"... | github_jupyter |
```
from IPython.display import HTML
HTML("""
<br><br>
<a href=http://wwwgong.pythonanywhere.com/cuspea/default/list_talks target=new>
<font size=+3 color=blue>CUSPEA Talks</font>
</a>
<br><br>
<img src=images/jupyter-notebook-wen-gong.jpg><br>
""")
```
# Fun with [Jupyter](http://jupyter.org/)
## Table of Contents
... | github_jupyter |
# Bangalore House Price Prediction - Outlier Detection
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
pd.set_option("display.max_columns", None)
pd.set_option("display.max_rows", None)
"""from google.colab import files
files=files.upload()
df = pd.read_csv('oh_encoded_... | github_jupyter |
```
# Data Type : Microarray data
# Dependent Variable : 0 or 1
# Human Acute Myeloid Leukemia (AML) or Acute Lymphoblast Leukemia (ALL))
library(spikeslab)
data(leukemia)
library(glmnet)
x <- as.matrix(leukemia[,-1])
y <- leukemia[,1]
# 행, 열
cat( nrow(leukemia), ncol(leukemia) )
lasso.leukemia <- glmnet(x, y, family ... | github_jupyter |
##### Copyright 2019 The TensorFlow Probability Authors.
Licensed under the Apache License, Version 2.0 (the "License");
```
#@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# you may not use this file except in compliance with the License.
# You may obtain a copy of th... | github_jupyter |
```
# import community
import numpy as np
import networkx as nx
import matplotlib as mpl
from matplotlib.pyplot import imshow
from matplotlib import pyplot as plt
import matplotlib.image as mpimg
import graphviz
from networkx.drawing.nx_agraph import write_dot, graphviz_layout
import random
import pydoc
import sys
sys.... | github_jupyter |
# Comparison to iPRG2012 consensus
```
import os
import sys
src_dir = os.path.abspath('../src')
if src_dir not in sys.path:
sys.path.append(src_dir)
%matplotlib inline
import math
import Levenshtein
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import squarify
from ... | github_jupyter |
# 1 - Sequence to Sequence Learning with Neural Networks
In this series we'll be building a machine learning model to go from once sequence to another, using PyTorch and TorchText. This will be done on German to English translations, but the models can be applied to any problem that involves going from one sequence to... | github_jupyter |
# Stat 222: Finance Project (Spring 2016)
##### Team: Fengshi Niu, Shamindra Shrotriya, Yueqi (Richie) Feng, Thibault Doutre
## Abstract
We created an open source Python Package 'lobpredictrst' to predict mid price movements for the AAPL LOB stock
- In data preprocessing part, we follow closely to the Kercheval and... | github_jupyter |
<!-- img src="http://cognitiveclass.ai/wp-content/uploads/2017/11/cc-logo-square.png" width="150"-->
<h1 align=center>R BASICS</h1>
### Welcome!
By the end of this notebook, you will have learned the basics of R!
## Table of Contents
<ul>
<li><a href="#About-the-Dataset">About the Dataset</a></li>
<li><a href="#S... | github_jupyter |
# Environment Setup Guide to work with Qiskit Textbook
This is a comprehensive guide for setting up your environment on your personal computer for working with Qiskit Textbook. This will help you reproduce the results as you see them on the textbook website. The Qiskit Textbook is written in [Jupyter notebooks](https:... | 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 Aqua: Pricing European Call Options*_
The latest version of this notebook is available on https://github.com/Qiskit/qis... | github_jupyter |
```
# note - you will need to be able to run the UNIX ffmpeg utility to run this notebook
#pip install google-cloud-storage
#!pip install google-cloud-language
#!pip install google-cloud-speech
import urllib.request
import os
import glob
from google.cloud import storage
from google.cloud import speech
from google.proto... | github_jupyter |
# **ArtLine**
**Create** **amazing** **lineart** **portraits**
```
import torch
import torch.nn as nn
import fastai
from fastai.vision import *
from fastai.callbacks import *
from fastai.vision.gan import *
from torchvision.models import vgg16_bn
from fastai.utils.mem import *
from PIL import Image
import matplotlib.p... | github_jupyter |
```
%matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
```
# Vocabulary
In the previous parts, you learned how matplotlib organizes plot-making by figures and axes. We broke down the components of a basic figure and learned how to create them. You also learned how to add one or more axes to a fig... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive')
path = '/content/drive/MyDrive/Research/AAAI/complexity/5D_elliptical_zeroth/200_50/'
import numpy as np
import pandas as pd
import torch
import torchvision
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import... | github_jupyter |
### Where Art Thou Min'ral?
In this notebook, we'll be using [H2o's AutoMl](https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html) algorithm to train our first **Binary Classification Model** on the data we prepared in our earlier notebook.
Before we begin, make sure you install h2o in your system. You can refer... | github_jupyter |
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