text stringlengths 2.5k 6.39M | kind stringclasses 3
values |
|---|---|
```
import urllib.request
import os
import geopandas as gpd
import rasterio
from rasterio.plot import show
import zipfile
import matplotlib.pyplot as plt
```
# GIS visualizations with geopandas
```
url = 'https://biogeo.ucdavis.edu/data/gadm3.6/shp/gadm36_COL_shp.zip'
dest = os.path.join('data', 'admin')
os.makedirs(... | github_jupyter |
```
import cobra
import copy
import mackinac
mackinac.modelseed.ms_client.url = 'http://p3.theseed.org/services/ProbModelSEED/'
mackinac.workspace.ws_client.url = 'http://p3.theseed.org/services/Workspace'
mackinac.genome.patric_url = 'https://www.patricbrc.org/api/'
# PATRIC user information
mackinac.get_token('mljen... | github_jupyter |
# SVM
```
import numpy as np
import sympy as sym
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
%matplotlib inline
np.random.seed(1)
```
## Simple Example Application
对于简单的数据样本例子(也就是说可以进行线性划分,且不包含噪声点)
**算法:**
输入:线性可分训... | github_jupyter |
This notebooks finetunes VGG16 by adding a couple of Dense layers and trains it to classify between cats and dogs.
This gives a better classification of around 95% accuracy on the validation dataset
```
%load_ext autoreload
%autoreload 2
import numpy as np
import tensorflow as tf
from tensorflow.contrib.keras impor... | github_jupyter |
## Introduction
**Offer Recommender example:**
___
In this example we will show how to:
- Setup the required environment for accessing the ecosystem prediction server.
- View and track business performance of the Offer Recommender.
## Setup
**Setting up import path:**
___
Add path of ecosystem notebook wrappers.... | github_jupyter |
> Code to accompany **Chapter 10: Defending Against Adversarial Inputs**
# Fashion-MNIST - Generating Adversarial Examples on a Drop-out Network
This notebook demonstrates how to generate adversarial examples using a network that incorporates randomised drop-out.
```
import tensorflow as tf
from tensorflow import ke... | github_jupyter |
```
# Install TensorFlow
# !pip install -q tensorflow-gpu==2.0.0-beta1
try:
%tensorflow_version 2.x # Colab only.
except Exception:
pass
import tensorflow as tf
print(tf.__version__)
# Load in the data
from sklearn.datasets import load_breast_cancer
# load the data
data = load_breast_cancer()
# check the type of... | github_jupyter |
# Writing OER sets to file for
---
### Import Modules
```
import os
print(os.getcwd())
import sys
import time; ti = time.time()
import json
import pandas as pd
import numpy as np
# #########################################################
from methods import (
get_df_features_targets,
get_df_jobs,
get_... | github_jupyter |
<a href="https://colab.research.google.com/github/jantic/DeOldify/blob/master/ImageColorizerColabStable.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
### **<font color='blue'> Stable Colorizer </font>**
#◢ DeOldify - Colorize your own photos!
##... | github_jupyter |
```
# testing scRFE
pip list
from scRFE import scRFE
from scRFE import scRFEimplot
from scRFE.scRFE import makeOneForest
import numpy as np
import pandas as pd
from anndata import read_h5ad
adata = read_h5ad('/Users/madelinepark/Downloads/Liver_droplet.h5ad')
madeForest = makeOneForest(dataMatrix=adata, classOfInteres... | github_jupyter |
```
import pickle
PIK = 'data/sirt6/final/20191217_m87e_counts.pkl'
with open(PIK, 'rb') as f:
m87e_clobs = pickle.load(f)
m87e_clobs
import pandas as pd
def extract_panda(clob_list):
dictlist = []
for i in range(len(clob_list)):
dictlist += [clob_list[i].to_dict()]
DF = pd.DataFrame(dictlist)
... | github_jupyter |
# Performing the Hyperparameter tuning
**Learning Objectives**
1. Learn how to use `cloudml-hypertune` to report the results for Cloud hyperparameter tuning trial runs
2. Learn how to configure the `.yaml` file for submitting a Cloud hyperparameter tuning job
3. Submit a hyperparameter tuning job to Cloud AI Platform
... | github_jupyter |
## _*Using Qiskit Aqua for exact cover problems*_
In mathematics, given a collection $S$ of subsets of a set $X$.
An exact cover is a subcollection $S_{ec} \subseteq S$ such that each element in $X$ is contained in exactly one subset $\in S_{ec}$.
We will go through three examples to show (1) how to run the optimiza... | github_jupyter |
# Overlays
Spatial overlays allow you to compare two GeoDataFrames containing polygon or multipolygon geometries
and create a new GeoDataFrame with the new geometries representing the spatial combination *and*
merged properties. This allows you to answer questions like
> What are the demographics of the census tract... | github_jupyter |
# Calling RES with Python in SPARK
## Pre-Requisite
* Python 3.5 for Spark
## Initializing Python environment with ODM Jars files and ODM Model archive
* Create a Spark Session
* Initialize the Python environment
```
from io import StringIO
import requests
import json
import pandas as pd
#from pyspark.sql... | github_jupyter |
Before you turn this problem in, make sure everything runs as expected. First, **restart the kernel** (in the menubar, select Kernel$\rightarrow$Restart) and then **run all cells** (in the menubar, select Cell$\rightarrow$Run All).
Make sure you fill in any place that says `YOUR CODE HERE` or "YOUR ANSWER HERE", as we... | github_jupyter |
##### Copyright 2020 The Cirq Developers
```
#@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 agre... | github_jupyter |
*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lecture/115) 03:42*
In numerical variable, you want to take the average mean and infer the average and the differences. In categorical variable, you take the proportion of frequency, you may ... | github_jupyter |
# Question repository
A list of open questions and possibly ambiguous stuff encountered throughout the material.
TODO: Tag exam-related ones appropriately, to differentiate them from (exclusively) curiosity-related ones.
**Note:** An alternative design would consist of adding a questions section to every notebook, t... | github_jupyter |
# Model Checking
After running an MCMC simulation, `sample` returns a `MultiTrace` object containing the samples for all the stochastic and deterministic random variables. The final step in Bayesian computation is model checking, in order to ensure that inferences derived from your sample are valid. There are two comp... | github_jupyter |
# 16장. 로지스틱 회귀 분석 과제
```
import matplotlib.pyplot as plt
import os
from typing import List, Tuple
import csv
from scratch.linear_algebra import Vector, get_column
```
## 1. 데이터셋
### 1.1 데이터셋 다운로드
```
import requests
data = requests.get("https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisco... | github_jupyter |
```
%pylab inline
import os
import glob
import pandas as pd
import re
from collections import OrderedDict
import seaborn as sns
sns.set_context('paper', font_scale=2)
sns.set_style('white')
def clean_tx(tx):
return re.sub(r'\.[0-9]+', '', tx)
root_dir = '/staging/as/skchoudh/re-ribo-analysis/hg38/SRP010679/ribocop... | github_jupyter |
# Nearest Centroid Classification with MInMaxScaler & PowerTransformer
This Code template is for the Classification task using a simple NearestCentroid with feature rescaling technique MinMaxScaler and feature tranformation technique used is PowerTransformer in a pipeline.
### Required Packages
```
!pip install imbl... | github_jupyter |
```
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms, models
from torch.autograd import Variable
data_dir = 'Cat... | github_jupyter |
# IEEE MEGA PROJECT
**Team Name: BetaTech**
**Team Leader: Mollika Garg**
**Email Id: mollika.garg@gmail.com**
**Team Member: Shreya Sharma**
**Email Id: shreyasharma.1510001@gmail.com**
**Team Member: Koushiki Chakrabarti**
*... | github_jupyter |
```
from os import listdir
from numpy import array
from keras.preprocessing.text import Tokenizer, one_hot
from keras.preprocessing.sequence import pad_sequences
from keras.models import Model
from keras.utils import to_categorical
from keras.layers import Embedding, TimeDistributed, RepeatVector, LSTM, concatenate , I... | github_jupyter |
# Modeling and Simulation in Python
Chapter 13
Copyright 2017 Allen Downey
License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)
```
# Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an a... | github_jupyter |
# Writing Your Own Graph Algorithms
The analytical engine in GraphScope derives from [GRAPE](https://dl.acm.org/doi/10.1145/3282488), a graph processing system proposed on SIGMOD-2017. GRAPE differs from prior systems in its ability to parallelize sequential graph algorithms as a whole. In GRAPE, sequential algorithms... | github_jupyter |
# SuStaIn tutorial using simulated data
Written by Alex Young in April 2020, updated in April 2021. Please email alexandra.young@kcl.ac.uk with any questions.
This tutorial demonstrates how to run Subtype and Stage Inference (SuStaIn) using simulated data. SuStaIn is an unsupervised learning algorithm that identifies... | github_jupyter |
# Math and Statistics Review for ML
Using the smallpox data set, review relevant mathematical and statistical methods commonly used in machine learning. An example will be shown using the Utah data. Choose another state and perform the same operations on the data for that state.
```
import pandas as pd
import numpy as... | github_jupyter |
# Gas Mixtures: Perfect and Semiperfect Models
This Notebook is an example about how to declare and use *Gas Mixtures* with **pyTurb**. Gas Mixtures in **pyTurb** are treated as a combination of different gases of **pyTurb**:
- *PerfectIdealGas*: Ideal Equation of State ($pv=R_gT$) and constant $c_p$, $c_v$, $\gamma_g... | github_jupyter |
```
%reload_ext autoreload
%autoreload 2
from fastai.gen_doc.gen_notebooks import *
from pathlib import Path
```
### To update this notebook
Run `tools/sgen_notebooks.py
Or run below:
You need to make sure to refresh right after
```
import glob
for f in Path().glob('*.ipynb'):
generate_missing_metadata(f)
```... | github_jupyter |
# maysics.calculus模块使用说明
calculus模块包含七个函数
|名称|作用|
|---|---|
|lim|极限|
|ha|哈密顿算符|
|grad|梯度|
|nebla_dot|nebla算子点乘|
|nebla_cross|nebla算子叉乘|
|laplace|拉普拉斯算子|
|inte|积分|
<br></br>
## 求极限：lim
lim(f, x0, acc=0.01, method='both')
<br>求函数```f```在```acc```的误差下，$x\rightarrow x_{0}$的函数值
<br>```method```可选'both'、'+'、'-'，分别表示双边极限、右... | github_jupyter |
# Safely refactoring ACLs and firewall rules
Changing ACLs or firewall rules (or *filters*) is one of the riskiest updates to a network. Even a small error can block connectivity for a large set of critical services or open up sensitive resources to the world at large. Earlier notebooks showed how to [analyze filters ... | github_jupyter |
# Logistic Regression with a Neural Network mindset
Welcome to your first (required) programming assignment! You will build a logistic regression classifier to recognize cats. This assignment will step you through how to do this with a Neural Network mindset, and so will also hone your intuitions about deep learning.... | github_jupyter |
# This notebook serves as an example of how to create AutoTST objects and how to create 3D geometries
```
#General imports
import os, sys
import logging
from copy import deepcopy
import numpy as np
import pandas as pd
from multiprocessing import Process
#RDKit imports
import rdkit
from rdkit import Chem
from rdkit.Ch... | github_jupyter |
## Tutorial 2: Mixture Models and Expectation Maximization
### Exercise 1: Categorical Mixture Model (CMM)
```
# Import libraries
import numpy as np
import pandas as pd
from ast import literal_eval
import matplotlib.pyplot as plt
import gensim
from wordcloud import WordCloud, STOPWORDS
from categorical_em import Ca... | github_jupyter |
# Dimensionality reduction using `scikit-learn`
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import preprocessing, model_selection as ms, \
manifold, decomposition as dec, cross_decomposition as cross_dec
from sklearn.pipeline import Pipeline
%matplotl... | github_jupyter |
# 1. Import libraries
```
#----------------------------Reproducible----------------------------------------------------------------------------------------
import numpy as np
import tensorflow as tf
import random as rn
import os
seed=0
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
rn.seed(seed)
#sess... | github_jupyter |
## Part I: On-policy learning and SARSA
(3 points)
_This notebook builds upon `qlearning.ipynb`, or to be exact, generating qlearning.py._
The policy we're gonna use is epsilon-greedy policy, where agent takes optimal action with probability $(1-\epsilon)$, otherwise samples action at random. Note that agent __can__... | github_jupyter |
# Facies classification using Machine Learning #
## LA Team Submission 5 ##
### _[Lukas Mosser](https://at.linkedin.com/in/lukas-mosser-9948b32b/en), [Alfredo De la Fuente](https://pe.linkedin.com/in/alfredodelafuenteb)_ ####
In this approach for solving the facies classfication problem ( https://github.com/seg/2016-... | github_jupyter |
TSG086 - Run `top` in all containers
====================================
Steps
-----
### Instantiate Kubernetes client
```
# Instantiate the Python Kubernetes client into 'api' variable
import os
from IPython.display import Markdown
try:
from kubernetes import client, config
from kubernetes.stream import ... | github_jupyter |
# TASK #1: DEFINE SINGLE AND MULTI-DIMENSIONAL NUMPY ARRAYS
```
# NumPy is a Linear Algebra Library used for multidimensional arrays
# NumPy brings the best of two worlds: (1) C/Fortran computational efficiency, (2) Python language easy syntax
# Let's define a one-dimensional array
import NumPy as np
list_1 = [6,8... | github_jupyter |
```
import pandas as pd
import scipy.io
import os
import matplotlib.pyplot as plt
path = os.getcwd()
matlab_exe_path = '''matlab'''
julia_path = '''C:\\Users\\mwaugh\\AppData\\Local\\Programs\\Julia\\Julia-1.4.0\\bin\\julia.exe'''
path = "src\\calibration"
#fig_path = "C:\\users\mwaugh\\github\\perla_tonetti_waugh\... | github_jupyter |
```
import numpy as np
import pandas as pd
from CSVUtils import *
import pickle
from os import path
import matplotlib.pyplot as plt
ROOT_DIR = "./from github/Stock-Trading-Environment/"
freq_list = [
{
"freq": 1,
"training": "10k",
"DIR": "./output/200",
"prefix": "BRZ+TW+NASDAQ-Trai... | github_jupyter |
# Federated learning: pretrained model
In this notebook, we provide a simple example of how to perform an experiment in a federated environment with the help of the Sherpa.ai Federated Learning framework. We are going to use a popular dataset and a pretrained model.
## The data
The framework provides some functions f... | github_jupyter |
## Using Isolation Forest to Detect Criminally-Linked Properties
The goal of this notebook is to apply the Isolation Forest anomaly detection algorithm to the property data. The algorithm is particularly good at detecting anomalous data points in cases of extreme class imbalance. After normalizing the data and splitti... | github_jupyter |
<img src="../images/aeropython_logo.png" alt="AeroPython" style="width: 300px;"/>
# Secciones de arrays
_Hasta ahora sabemos cómo crear arrays y realizar algunas operaciones con ellos, sin embargo, todavía no hemos aprendido cómo acceder a elementos concretos del array_
## Arrays de una dimensión
```
# Accediendo a... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W1D1_ModelTypes/W1D1_Tutorial2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Neuromatch Academy: Week 1, Day 1, Tutorial 2
# Model Types: "... | github_jupyter |
## A quick Gender Recognition model
Grabbed from [nlpforhackers](https://nlpforhackers.io/introduction-machine-learning/) webpage.
1. Firstly convert the dataset into a numpy array to keep only gender and names
2. Set the feature parameters which takes in different parameters
3. Vectorize the parametes
4. Get varied tr... | github_jupyter |
# Get started
<a href="https://mybinder.org/v2/gh/tinkoff-ai/etna/master?filepath=examples/get_started.ipynb">
<img src="https://mybinder.org/badge_logo.svg" align='left'>
</a>
This notebook contains the simple examples of time series forecasting pipeline
using ETNA library.
**Table of Contents**
* [Creating T... | github_jupyter |
# <div align="center">What is a Tensor</div>
---------------------------------------------------------------------
you can Find me on Github:
> ###### [ GitHub](https://github.com/lev1khachatryan)
***Tensors are not generalizations of vectors***. It’s very slightly more understandable to say that tensors are gene... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import numpy as np
np.set_printoptions(precision=2)
import matplotlib.pyplot as plt
import copy as cp
import sys, json, pickle
PROJECT_PATHS = ['/home/nbuckman/Dropbox (MIT)/DRL/2020_01_cooperative_mpc/mpc-multiple-vehicles/', '/Users/noambuckman/mpc-multiple-vehicles/']
for p in ... | github_jupyter |
# Classification example 2 using Health Data with PyCaret
```
#Code from https://github.com/pycaret/pycaret/
# check version
from pycaret.utils import version
version()
```
# 1. Data Repository
```
import pandas as pd
url = 'https://raw.githubusercontent.com/davidrkearney/colab-notebooks/main/datasets/strokes_traini... | github_jupyter |
# Logistic regression example
### Dr. Tirthajyoti Sarkar, Fremont, CA 94536
---
This notebook demonstrates solving a logistic regression problem of predicting Hypothyrodism with **Scikit-learn** and **Statsmodels** libraries.
The dataset is taken from UCI ML repository.
<br>Here is the link: https://archive.ics.uci.... | github_jupyter |
# Keras Functional API
```
# sudo pip3 install --ignore-installed --upgrade tensorflow
import keras
import tensorflow as tf
print(keras.__version__)
print(tf.__version__)
# To ignore keep_dims warning
tf.logging.set_verbosity(tf.logging.ERROR)
```
Let’s start with a minimal example that shows side by side a simple Se... | github_jupyter |
```
# Libraries for R^2 visualization
from ipywidgets import interactive, IntSlider, FloatSlider
from math import floor, ceil
from sklearn.base import BaseEstimator, RegressorMixin
# Libraries for model building
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squ... | github_jupyter |
<div>
<img src="https://drive.google.com/uc?export=view&id=1vK33e_EqaHgBHcbRV_m38hx6IkG0blK_" width="350"/>
</div>
#**Artificial Intelligence - MSc**
##ET5003 - MACHINE LEARNING APPLICATIONS
###Instructor: Enrique Naredo
###ET5003_NLP_SpamClasiffier-2
### Spam Classification
[Spamming](https://en.wikipedia.org/w... | github_jupyter |
<a href="https://colab.research.google.com/github/amathsow/wolof_speech_recognition/blob/master/Speech_recognition_project.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!pip3 install torch
!pip3 install torchvision
!pip3 install torchaudio
!pi... | github_jupyter |
<a href="https://colab.research.google.com/github/lmcanavals/algorithmic_complexity/blob/main/05_01_UCS_dijkstra.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Breadth First Search
BFS para los amigos
```
import graphviz as gv
import numpy as n... | github_jupyter |
# [Introduction to Data Science: A Comp-Math-Stat Approach](https://lamastex.github.io/scalable-data-science/as/2019/)
## YOIYUI001, Summer 2019
©2019 Raazesh Sainudiin. [Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
# 08. Pseudo-Random Numbers, Simulating from Some Dis... | github_jupyter |
# Convolutional Neural Networks: Application
Welcome to Course 4's second assignment! In this notebook, you will:
- Implement helper functions that you will use when implementing a TensorFlow model
- Implement a fully functioning ConvNet using TensorFlow
**After this assignment you will be able to:**
- Build and t... | github_jupyter |
# k-Nearest Neighbor (kNN) implementation
*Credits: this notebook is deeply based on Stanford CS231n course assignment 1. Source link: http://cs231n.github.io/assignments2019/assignment1/*
The kNN classifier consists of two stages:
- During training, the classifier takes the training data and simply remembers it
- D... | github_jupyter |
# Run Modes
Running MAGICC in different modes can be non-trivial. In this notebook we show how to set MAGICC's config flags so that it will run as desired for a few different cases.
```
# NBVAL_IGNORE_OUTPUT
from os.path import join
import datetime
import dateutil
from copy import deepcopy
import numpy as np
import... | github_jupyter |
```
import pandas as pd
import numpy as np
import os
from matplotlib.pyplot import *
from IPython.display import display, HTML
import glob
import scanpy as sc
import pandas as pd
import seaborn as sns
import scipy.stats
%matplotlib inline
file = '/nfs/leia/research/stegle/dseaton/hipsci/singlecell_neuroseq/data/ipsc_s... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
```
# Generate images
```
from pathlib import Path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
SMALL_SIZE = 15
MEDIUM_SIZE = 20
BIGGER_SIZE = 25
plt.rc("font", size=SMALL_SIZE)
plt.rc("axes", titlesize=SMALL_SIZE)
plt.rc("axes", labelsize=MEDIUM_SIZ... | github_jupyter |
# Processing Milwaukee Label (~3K labels)
Building on `2020-03-24-EDA-Size.ipynb`
Goal is to prep a standard CSV that we can update and populate
```
import pandas as pd
import numpy as np
import os
import s3fs # for reading from S3FileSystem
import json # for working with JSON files
import matplotlib.pyplot as pl... | github_jupyter |
```
!nvidia-smi
# unrar x "/content/drive/MyDrive/IDC_regular_ps50_idx5.rar" "/content/drive/MyDrive/"
# !unzip "/content/drive/MyDrive/base_dir/train_dir/b_idc.zip" -d "/content/drive/MyDrive/base_dir/train_dir"
import os
! pip install -q kaggle
from google.colab import files
files.upload()
! mkdir ~/.kaggle
! cp kag... | github_jupyter |
# Security Master Analysis
by @marketneutral
```
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly.plotly as py
from plotly.offline import init_notebook_mode, iplot
import plotly.graph_objs as go
import cufflinks as cf
init_notebook_mode(connected=False)
cf.set_config_file(offline=T... | github_jupyter |
**Chapter 5 – Support Vector Machines**
_This notebook contains all the sample code and solutions to the exercises in chapter 5._
# Setup
First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figu... | github_jupyter |
# Introduction to Biomechanics
> Marcos Duarte
> Laboratory of Biomechanics and Motor Control ([http://demotu.org/](http://demotu.org/))
> Federal University of ABC, Brazil
## Biomechanics @ UFABC
```
from IPython.display import IFrame
IFrame('http://demotu.org', width='100%', height=500)
```
## Biomechanics
T... | github_jupyter |
# Keras Exercise
## Predict political party based on votes
As a fun little example, we'll use a public data set of how US congressmen voted on 17 different issues in the year 1984. Let's see if we can figure out their political party based on their votes alone, using a deep neural network!
For those outside the Unit... | github_jupyter |
# 6. External Libraries
<a href="https://colab.research.google.com/github/chongsoon/intro-to-coding-with-python/blob/main/6-External-Libraries.ipynb" target="_parent">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
Up till now, we have been using what ever is availab... | github_jupyter |
<a href="http://landlab.github.io"><img style="float: left" src="../../../landlab_header.png"></a>
# The Implicit Kinematic Wave Overland Flow Component
<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/la... | github_jupyter |
# SageMaker Debugger Profiling Report
SageMaker Debugger auto generated this report. You can generate similar reports on all supported training jobs. The report provides summary of training job, system resource usage statistics, framework metrics, rules summary, and detailed analysis from each rule. The graphs and tab... | github_jupyter |
# Overview
This lab has been adapted from the angr [motivating example](https://github.com/angr/angr-doc/tree/master/examples/fauxware). It shows the basic lifecycle and capabilities of the angr framework.
Note this lab (and other notebooks running angr) should be run with the Python 3 kernel!
Look at fauxware.c! T... | github_jupyter |
```
pip install mlxtend --upgrade --no-deps
import mlxtend
print(mlxtend.__version__)
from google.colab import drive
drive.mount('/content/gdrive')
import cv2
import skimage
import keras
import tensorflow
import numpy as np
import matplotlib.pyplot as plt
import p... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W3D1_RealNeurons/W3D1_Tutorial2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Neuromatch Academy: Week 3, Day 1, Tutorial 2
# Real Neurons: ... | github_jupyter |
# Initialize a game
```
from ConnectN import ConnectN
game_setting = {'size':(6,6), 'N':4, 'pie_rule':True}
game = ConnectN(**game_setting)
% matplotlib notebook
from Play import Play
gameplay=Play(ConnectN(**game_setting),
player1=None,
player2=None)
```
# Define our policy
Please ... | github_jupyter |
# Cleaning Your Data
Let's take a web access log, and figure out the most-viewed pages on a website from it! Sounds easy, right?
Let's set up a regex that lets us parse an Apache access log line:
```
import re
format_pat= re.compile(
r"(?P<host>[\d\.]+)\s"
r"(?P<identity>\S*)\s"
r"(?P<user>\S*)\s"
r... | github_jupyter |
# CORDIS FP7
```
import json
import re
import urllib
from titlecase import titlecase
import pandas as pd
pd.set_option('display.max_columns', 50)
```
## Read in Data
```
all_projects = pd.read_excel('input/fp7/cordis-fp7projects.xlsx')
all_projects.shape
all_organizations = pd.read_excel('input/fp7/cordis-fp7organ... | github_jupyter |
# Step 2: Building GTFS graphs and merging it with a walking graph
We heavily follow Kuan Butts's Calculating Betweenness Centrality with GTFS blog post: https://gist.github.com/kuanb/c54d0ae7ee353cac3d56371d3491cf56
### The peartree (https://github.com/kuanb/peartree) source code was modified. Until code is merged yo... | github_jupyter |
<a href="https://colab.research.google.com/github/mengwangk/dl-projects/blob/master/04_02_auto_ml_2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Automated ML
```
COLAB = True
if COLAB:
!sudo apt-get install git-lfs && git lfs install
!rm -... | github_jupyter |
# Print Compact Transitivity Tables
```
import qualreas as qr
import os
import json
path = os.path.join(os.getenv('PYPROJ'), 'qualreas')
```
## Algebras from Original Files
## Algebras from Compact Files
```
alg = qr.Algebra(os.path.join(path, "Algebras/Misc/Linear_Interval_Algebra.json"))
alg.summary()
alg.check_... | github_jupyter |
# Logistic Regression With Linear Boundary Demo
> ☝Before moving on with this demo you might want to take a look at:
> - 📗[Math behind the Logistic Regression](https://github.com/trekhleb/homemade-machine-learning/tree/master/homemade/logistic_regression)
> - ⚙️[Logistic Regression Source Code](https://github.com/tre... | github_jupyter |
# Working with Tensorforce to Train a Reinforcement-Learning Agent
This notebook serves as an educational introduction to the usage of Tensorforce using a gym-electric-motor (GEM) environment. The goal of this notebook is to give an understanding of what tensorforce is and how to use it to train and evaluate a reinfor... | github_jupyter |
<a href="https://colab.research.google.com/github/martin-fabbri/colab-notebooks/blob/master/deeplearning.ai/nlp/c3_w1_03_trax_intro_2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Trax : Ungraded Lecture Notebook
In this notebook you'll get to ... | github_jupyter |
# XGBoost model for Bike sharing dataset
```
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
# preprocessing methods
from sklearn.preprocessing import StandardScaler
# accuracy measures and data spliting
from sklearn.metrics import mean_squar... | github_jupyter |
# Weather Data Collection
```
import pandas as pd
import numpy as np
from selenium import webdriver
import time
races = pd.read_csv('./data/races.csv')
races.head()
races.shape
weather = races.iloc[:,[0,1,2]]
info = []
for link in races.url:
try:
df = pd.read_html(link)[0]
if 'Weather' in list(df.... | github_jupyter |
# Temporal Congruency Experiments
```
from scripts.imports import *
from scripts.df_styles import df_highlighter
out = Exporter(paths['outdir'], 'clause')
# redefine df_sg to include adverbs
df_sg = df[df.n_times == 1]
df_sg.columns
```
# Tense Collocations with tokens
```
token_ct = df_sg.pivot_table(
index=[... | github_jupyter |
## APIs
Let's start by looking at [OMDb API](https://www.omdbapi.com/).
The OMDb API is a free web service to obtain movie information, all content and images on the site are contributed and maintained by users.
The Python package [urllib](https://docs.python.org/3/howto/urllib2.html) can be used to fetch resources ... | github_jupyter |
CWPK \#34: A Python Module, Part II: Packaging and The Structure Extractor
=======================================
Moving from Notebook to Package Proved Perplexing
--------------------------
<div style="float: left; width: 305px; margin-right: 10px;">
<img src="http://kbpedia.org/cwpk-files/cooking-with-kbpedia-305... | github_jupyter |
```
############## PLEASE RUN THIS CELL FIRST! ###################
# import everything and define a test runner function
from importlib import reload
from helper import run
import ecc, helper, tx, script
# Signing Example
from ecc import G, N
from helper import hash256
secret = 1800555555518005555555
z = int.from_byte... | github_jupyter |
# Advanced Matplotlib Concepts Lecture
In this lecture we cover some more advanced topics which you won't usually use as often. You can always reference the documentation for more resources!
### Logarithmic Scale
* It is also possible to set a logarithmic scale for one or both axes. This functionality is in fact on... | github_jupyter |
# Practice Exercise: Exploring data (Exploratory Data Analysis)
## Context:
- The data includes 120 years (1896 to 2016) of Olympic games with information about athletes and medal results.
- We'll focus on practicing the summary statistics and data visualization techniques that we've learned in the course.
- In gener... | github_jupyter |
```
# hide
%load_ext nb_black
# default_exp clients
from will_it_saturate.clients import BaseClient
from will_it_saturate.registry import register_model
# export
import os
import math
import time
import httpx
import asyncio
import aiohttp
import subprocess
from pathlib import Path
from datetime import datetime
from ... | 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 |
```
import netCDF4
from netCDF4 import Dataset
import matplotlib.pyplot as plt
import numpy as np
import sys
import math
import os
import glob
import pandas
import re
from scipy.interpolate import griddata
%matplotlib inline
plt.rcParams["figure.figsize"] = (10,6)
plt.rcParams.update({'font.size': 20})
data_path = "/p... | github_jupyter |
```
! pip install fastcore --upgrade -qq
! pip install fastai --upgrade -qq
from fastai.vision.all import *
import fastai
from sys import exit
from operator import itemgetter
import re
import torch
from torch.nn import functional as F
import numpy as np
from time import process_time_ns, process_time
import gc
def scale... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
%matplotlib inline
class NNModel:
def __init__(self,learning_rate, n_iter, args):
self.learning_rate = learning_rate
self.ar... | github_jupyter |
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