code stringlengths 2.5k 150k | kind stringclasses 1
value |
|---|---|
<a href="https://colab.research.google.com/github/mancunian1792/causal_scene_generation/blob/master/causal_model/game_characters/GameCharacter_ImageClassification.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
from google.colab import drive
dr... | github_jupyter |
```
%reload_ext autoreload
%autoreload 2
from fastai.tabular import *
```
# Rossmann
## Data preparation
To create the feature-engineered train_clean and test_clean from the Kaggle competition data, run `rossman_data_clean.ipynb`. One important step that deals with time series is this:
```python
add_datepart(train,... | github_jupyter |
```
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib
matplotlib.pyplot.style.use('seaborn')
matplotlib.rcParams['figure.figsize'] = (15, 5)
%matplotlib inline
import math
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_nod... | github_jupyter |
Code testing for https://github.com/pymc-devs/pymc3/pull/2986
```
import numpy as np
import pymc3 as pm
import pymc3.distributions.transforms as tr
import theano.tensor as tt
from theano.scan_module import until
import theano
import matplotlib.pylab as plt
import seaborn as sns
%matplotlib inline
```
# Polar transfo... | github_jupyter |
# Mean Normalization
In machine learning we use large amounts of data to train our models. Some machine learning algorithms may require that the data is *normalized* in order to work correctly. The idea of normalization, also known as *feature scaling*, is to ensure that all the data is on a similar scale, *i.e.* that... | github_jupyter |
In this notebook we will use the boundary exploration algorithm to fully explore the parameter space of a generic Markov chain.
Last updated by: Jonathan Liu, 10/22/2020
```
#Import necessary packages
%matplotlib inline
import numpy as np
from scipy.spatial import ConvexHull
import matplotlib.pyplot as plt
import sci... | github_jupyter |
```
import pandas as pd
import os
import hashlib
import requests
from bs4 import BeautifulSoup
from bs4.element import Comment
import urllib.parse
from tqdm.notebook import tqdm
import random
from multiprocessing import Pool
import spacy
import numpy as np
industries = pd.read_csv("industry_categories.csv")
industries.... | github_jupyter |
```
from nltk.corpus import stopwords
from nltk.cluster.util import cosine_distance
import numpy as np
import networkx as nx
from nltk.corpus import stopwords
from nltk.cluster.util import cosine_distance
import numpy as np
import networkx as nx
def read_article(file_name):
file = open(file_name, "r")
filedata... | github_jupyter |
## Dependencies
```
import json, warnings, shutil, glob
from jigsaw_utility_scripts import *
from scripts_step_lr_schedulers import *
from transformers import TFXLMRobertaModel, XLMRobertaConfig
from tensorflow.keras.models import Model
from tensorflow.keras import optimizers, metrics, losses, layers
SEED = 0
seed_ev... | github_jupyter |
# Amazon SageMaker Model Monitor
This notebook shows how to:
* Host a machine learning model in Amazon SageMaker and capture inference requests, results, and metadata
* Analyze a training dataset to generate baseline constraints
* Monitor a live endpoint for violations against constraints
---
## Background
Amazon Sa... | github_jupyter |
### Importing the required libraries ###
```
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
%matplotlib inline
import seaborn as sns
import re
import zipfile
```
### UNZIP files ###
```
# Will unzip the files so that you can see them..
with zipfile.ZipFile("/kaggle/input/jigsaw-toxic-com... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive')
import numpy as np
import pandas as pd
train_srp53 = pd.read_csv('/content/drive/MyDrive/Molecular Exploration/Data/sr-p53.smiles',
sep='\t',
names=['smiles', 'id', 'target'])
train_srp53.head()
len(trai... | github_jupyter |
# Plagiarism Detection Model
Now that you've created training and test data, you are ready to define and train a model. Your goal in this notebook, will be to train a binary classification model that learns to label an answer file as either plagiarized or not, based on the features you provide the model.
This task wi... | github_jupyter |
```
# 任意选一个你喜欢的整数，这能帮你得到稳定的结果
seed = 2333 # todo
```
# 欢迎来到线性回归项目
若项目中的题目有困难没完成也没关系，我们鼓励你带着问题提交项目，评审人会给予你诸多帮助。
所有选做题都可以不做，不影响项目通过。如果你做了，那么项目评审会帮你批改，也会因为选做部分做错而判定为不通过。
其中非代码题可以提交手写后扫描的 pdf 文件，或使用 Latex 在文档中直接回答。
# 1 矩阵运算
## 1.1 创建一个 4*4 的单位矩阵
```
# 这个项目设计来帮你熟悉 python list 和线性代数
# 你不能调用任何NumPy以及相关的科学计算库来完成作业
# 本... | github_jupyter |
# Batch Processing!
#### A notebook to show some of the capilities available through the pCunch package
This is certainly not an exhaustive look at everything that the pCrunch module can do, but should hopefully provide some insight.
...or, maybe I'm just procrastinating doing more useful work.
```
# Python Modules ... | github_jupyter |
<a href="https://colab.research.google.com/github/laicheil/force2019/blob/master/tf_keras_test.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
!pip3 install --upgrade laicheil.force2019==0.post0.dev6
from laicheil.force2019 import something
som... | github_jupyter |
# MXNet with DALI - ResNet 50 example
## Overview
This example shows, how to use DALI pipelines with Apache MXNet.
## ResNet 50 pipeline
Let us first define a few global constants.
```
from __future__ import print_function
from nvidia.dali.pipeline import Pipeline
import nvidia.dali.ops as ops
import nvidia.dali.t... | github_jupyter |
#manipulate_regonline_output
This notebook reads the RegOnline output into a pandas DataFrame and reworks it to have each row contain the attendee, the Doppler Primer Session, the Monday Breakout session, and the Tuesday breakout session in each row.
```
import re
import numpy as np
import pandas as pd
import matplot... | github_jupyter |
# <center>Using Optimization in Hyperparameter settings in Deep Learning</center>
<center>by Cecilie Dura André</center>
<img src="https://blog.ml.cmu.edu/wp-content/uploads/2018/12/heatmap.001-min.jpeg" width="90%">
<p style="text-align: right;">Image from: https://blog.ml.cmu.edu/2018/12/12/massively-parallel-hy... | github_jupyter |
```
from __future__ import print_function
import argparse
import os
import random
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as dset
import torchvision.transforms as transforms
import t... | github_jupyter |
# Deep learning for computer vision
This notebook will teach you to build and train convolutional networks for image recognition. Brace yourselves.
# CIFAR dataset
This week, we shall focus on the image recognition problem on cifar10 dataset
* 60k images of shape 3x32x32
* 10 different classes: planes, dogs, cats, t... | github_jupyter |
# 基本程序设计
- 一切代码输入，请使用英文输入法
```
print('hello word')
print('hello word')
print 'hello'
```
## 编写一个简单的程序
- 圆公式面积： area = radius \* radius \* 3.1415
```
radius = 1
area = radius * radius * 3.1415
print(area)
radius = 1.0
area = radius * radius * 3.14 # 将后半部分的结果赋值给变量area
# 变量一定要有初始值！！！
# radius: 变量.area: 变量！
# int 类型
pri... | github_jupyter |
# Loading Image Data
강아지와 고양이를 구분하는 이미지 분류기를 생성하기 위해서는 고양이와 강아지 사진을 모아야 한다. 임의로 수집된 다음과 같은 고양이/강아지 사진을 사용하자.
이 사진을 사용하여 CNN으로 이미지 분류기를 만들기 위해서는 해당 사진을 적절히 전처리하여야 한다.
```
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
import torch
from ... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pymc3 as pm
from theano import tensor as T
import arviz
import os
import sys
from jupyterthemes import jtplot
jtplot.style(theme="monokai")
os.listdir()
lng = pd.read_csv("LNG.csv", index_col="Date")[["Adj Close"]]
dji = pd.read_csv("^D... | github_jupyter |
# Load MXNet model
In this tutorial, you learn how to load an existing MXNet model and use it to run a prediction task.
## Preparation
This tutorial requires the installation of Java Kernel. For more information on installing the Java Kernel, see the [README](https://github.com/awslabs/djl/blob/master/jupyter/READM... | github_jupyter |
## Aligning rasters: A step-by-step breakdown
This notebook aligns input rasters with a base reference raster. The implict purpose, reflected in the datasets used here, is to align rasters so that raster math operations can be performed between the rasters
```
import os, sys
import re
import pprint
# from pprint impo... | 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 |
```
%matplotlib inline
from matplotlib import style
style.use('fivethirtyeight')
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy import stats
from scipy.stats import ttest_ind, ttest_ind_from_stats
import datetime as dt
from datetime import datetime,timedelta
from itertools import chai... | github_jupyter |
```
import numpy as np
import cv2
import matplotlib.pyplot as plt
import math
def rgb2hsi(rgb):
# separar
R,G,B= cv2.split(rgb)
# normalizar
R =R/255
G =G/255
B =B/255
# cantidad de elementos
x=R.shape[0]
y=R.shape[1]
# crear arrays
r=np.empty([x,y])
g=np.empty([x,y])
... | github_jupyter |
<a href="https://colab.research.google.com/github/andrewm4894/colabs/blob/master/some_json_wrangling.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import pandas as pd
data = [{"event_date":"20201107","event_timestamp":"1604801718108000","even... | github_jupyter |
## Analyze A/B Test Results
This project will assure you have mastered the subjects covered in the statistics lessons. The hope is to have this project be as comprehensive of these topics as possible. Good luck!
## Table of Contents
- [Introduction](#intro)
- [Part I - Probability](#probability)
- [Part II - A/B Te... | github_jupyter |
# Modeling and Simulation in Python
Chapter 18
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 |
```
%load_ext autoreload
%autoreload 2
import ambry
l = ambry.get_library()
b = l.bundle('d04w001') # Geoschemas
sumlevels_p = l.partition('census.gov-acs_geofile-schemas-2009e-sumlevels')
sumlevels = {}
for row in sumlevels_p.stream(as_dict=True):
sumlevels[row['sumlevel']] = row['description']
from collections im... | github_jupyter |
```
import sys
from pathlib import Path
sys.path.append(str(Path.cwd().parent.parent))
import numpy as np
from kymatio.scattering2d.core.scattering2d import scattering2d
import matplotlib.pyplot as plt
import torch
import torchvision
from kymatio import Scattering2D
from PIL import Image
from IPython.display import di... | github_jupyter |
# Basic training functionality
```
from fastai.basic_train import *
from fastai.gen_doc.nbdoc import *
from fastai.vision import *
from fastai.distributed import *
```
[`basic_train`](/basic_train.html#basic_train) wraps together the data (in a [`DataBunch`](/basic_data.html#DataBunch) object) with a pytorch model to... | github_jupyter |
```
# load text
filename = 'metamorphosis_clean.txt'
file = open(filename, 'rt')
text = file.read()
file.close()
# open('metamorphosis_clean.txt', rt).read()
```
### split by whitespace
```
# load text
filename = 'metamorphosis_clean.txt'
file = open(filename, 'rt')
text = file.read()
file.close()
# split into words ... | github_jupyter |
```
import datetime as dt
import pandas as pd
# Get dataframe of boroughs
df = pd.read_csv("taxi_zone_lookup.csv")
df
# Create dictionary of boroughs, and build the list of locations for each borough
# (6 boroughs vs of 265 NY locations)
dfdict = {'EWR': [], 'Queens': [], 'Bronx': [], 'Manhattan': [], 'Staten Island... | github_jupyter |
# Todoist Data Analysis
This notebook processed the downloaded history of your todoist tasks. See [todoist_downloader.ipynb](https://github.com/markwk/qs_ledger/blob/master/todoist/todoist_downloader.ipynb) to export and download your task history from Todoist.
---
```
from datetime import date, datetime as dt, time... | github_jupyter |
# Training and Evaluating Machine Learning Models in cuML
This notebook explores several basic machine learning estimators in cuML, demonstrating how to train them and evaluate them with built-in metrics functions. All of the models are trained on synthetic data, generated by cuML's dataset utilities.
1. Random Fores... | github_jupyter |
```
%matplotlib inline
import json
import os
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from functools import reduce
from matplotlib.ticker import FuncFormatter
DAMORE = '@Fired4Truth'
DATA_DIR = os.path.join("data", "clean")
sns.set_palette(sns.xkcd_palette(["windows blue", "amber",... | github_jupyter |
```
import scanpy as sc
import pandas as pd
import numpy as np
import scipy as sp
from statsmodels.stats.multitest import multipletests
import matplotlib.pyplot as plt
import seaborn as sns
import os
from os.path import join
import time
plt.rcParams['pdf.fonttype'] = 42
plt.rcParams['ps.fonttype'] = 42
# scTRS tools
... | github_jupyter |
# Probando el ajuste de distribuciones hipotéticas
A veces, el conocimiento específico sugiere fuertes razones que justifiquen alguna suposición; de lo contrario, esto debería probarse de alguna manera. Cuando comprobamos si los datos experimentales se ajustan a una distribución de probabilidad dada, no estamos realme... | github_jupyter |
# Properties of drugs
Find various properties of the individual drugs
1.) ATC
2.) GO Annotations
3.) Disease
4.) KeGG Pathways
5.) SIDER (known effects)
6.) Offside (known off sides)
7.) TwoSides
8.) Drug Properties (physico-chemical properties)
9.) Enzymes, Transporters and Carriers
10.) Chemic... | github_jupyter |
# Graded Programming Assignment
In this assignment, you will implement re-use the unsupervised anomaly detection algorithm but turn it into a simpler feed forward neural network for supervised classification.
You are training the neural network from healthy and broken samples and at later stage hook it up to a messag... | github_jupyter |
# Reproduce Allen smFISH results with Starfish
This notebook walks through a work flow that reproduces the smFISH result for one field of view using the starfish package.
```
from copy import deepcopy
from glob import glob
import json
import os
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
im... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import PIL
import skimage as sk
import random
from PIL import Image
def prepare_dataset(path):
#declare arrays
x=[]
y=[]
# 이미지와 label을 리스트에 넣기
data_folders = os.listdir(path)
for folder in data_folders:
... | github_jupyter |
<a href="https://colab.research.google.com/github/LucyKinyua/Week2_MS/blob/main/Moringa_Data_Science_Prep_W2_Independent_Project_2021_05_Lucy_Kinyua_SQL_Notebook.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Overview
In this part of the assessm... | github_jupyter |
# Autonomous driving - Car detection
Welcome to your week 3 programming assignment. You will learn about object detection using the very powerful YOLO model. Many of the ideas in this notebook are described in the two YOLO papers: [Redmon et al., 2016](https://arxiv.org/abs/1506.02640) and [Redmon and Farhadi, 2016](h... | github_jupyter |
```
# default_exp helpers
```
# helpers
> this didn't fit anywhere else
```
#export
import numpy as np
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.pyplot as plt
#ToDo: Propagate them through the methods
iters = 10
l2 = 1
n_std = 4
from pygments import highlight
from pygments.lexers impor... | github_jupyter |
```
#export
from local.torch_basics import *
from local.test import *
from local.core import *
from local.data.all import *
from local.tabular.core import *
try: import cudf,nvcategory
except: print("This requires rapids, see https://rapids.ai/ for installation details")
from local.notebook.showdoc import *
#default_ex... | github_jupyter |
# Inspirational Notebooks
### Generating new festures according to these notebooks
* https://www.kaggle.com/nuhsikander/lgbm-new-features-corrected
* https://www.kaggle.com/rteja1113/lightgbm-with-count-features
* https://www.kaggle.com/aharless/swetha-s-xgboost-revised
* https://www.kaggle.com/bk0000/non-blending-lig... | github_jupyter |
<a href="https://colab.research.google.com/github/findingfoot/ML_practice-codes/blob/master/principal_component_analysis_.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
from sklearn import datasets
import numpy as np
import matplotlib.pyplot as... | github_jupyter |
What you should know about C
----
- Write, compile and run a simple program in C
- Static types
- Control flow especially `for` loop
- Using functions
- Using structs
- Pointers and arrays
- Function pointers
- Dynamic memory allocation
- Separate compilation and `make`
### Structs
**Exercise 1**
Write and use a `s... | github_jupyter |
## Training Network
In supervised training, the network processes inputs and compares its resulting outputs against the desired outputs.
Errors are propagated back through the system, causing the system to adjust the weights which control the network. This is done using the Backpropagation algorithm, also called bac... | github_jupyter |
# Chapter 3 - a binary classification example
```
from keras.datasets import imdb
from keras import models, layers
from keras import optimizers
from keras import losses
from keras import metrics
import numpy as np
import matplotlib.pyplot as plt
```
## Loading dataset
```
# Suggested code - doesn't work
# (train_da... | github_jupyter |
# Natural language inference: task and datasets
```
__author__ = "Christopher Potts"
__version__ = "CS224u, Stanford, Fall 2020"
```
## Contents
1. [Overview](#Overview)
1. [Our version of the task](#Our-version-of-the-task)
1. [Primary resources](#Primary-resources)
1. [Set-up](#Set-up)
1. [SNLI](#SNLI)
1. [SNLI ... | github_jupyter |
```
import glob
import os
import pandas as pd
import numpy as np
##################### Traces description
# 1. CLT_PUSH_START - SENDING Time between the scheduling of the request and its actual processing
# 2. CLT_PUSH_END - CLT_PUSH_START Time to prepare the packet, send it to the NIC driver through rt... | github_jupyter |
# Self-Driving Car Engineer Nanodegree
## Project: **Finding Lane Lines on the Road**
***
In this project, you will use the tools you learned about in the lesson to identify lane lines on the road. You can develop your pipeline on a series of individual images, and later apply the result to a video stream (really j... | github_jupyter |
```
from os.path import exists, join, isfile
from os import listdir, makedirs
from obspy.geodetics import kilometer2degrees
import numpy as np
from obspy.taup import TauPyModel
import matplotlib.pyplot as plt
from SS_MTI import Inversion
import threading
import subprocess
import Create_Vmod
from SS_MTI import Gradien... | github_jupyter |
```
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import cross_validate
import numpy as np
import xgboost as xgb
import pandas as pd
train_datasetL = pd.read_csv("../data/ori_data/train_process.csv", header=None, sep="\t").iloc[:, 0].values
dev_datasetL = pd.read_csv("../data/ori_d... | github_jupyter |
# Basic Motion
Welcome to JetBot's browser based programming interface! This document is
called a *Jupyter Notebook*, which combines text, code, and graphic
display all in one! Prett neat, huh? If you're unfamiliar with *Jupyter* we suggest clicking the
``Help`` drop down menu in the top toolbar. This has useful r... | github_jupyter |
# 머신 러닝 교과서 3판
# 14장 - 텐서플로의 구조 자세히 알아보기 (2/3)
**아래 링크를 통해 이 노트북을 주피터 노트북 뷰어(nbviewer.jupyter.org)로 보거나 구글 코랩(colab.research.google.com)에서 실행할 수 있습니다.**
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://nbviewer.jupyter.org/github/rickiepark/python-machine-learning-book-3r... | github_jupyter |
# Posthoc Inference on Contrasts
In this notebook, we provide examples of how to run posthoc inference to infer on contrasts in the linear model.
## Set Up
#### Import the required python packages.
```
import numpy as np
import numpy.matlib as npm
import matplotlib.pyplot as plt
import sanssouci as ss
import pyr... | github_jupyter |
<h1 align="center">Welcome to SimpleITK Jupyter Notebooks</h1>
## Newcomers to Jupyter Notebooks:
1. We use two types of cells, code and markdown.
2. To run a code cell, select it (mouse or arrow key so that it is highlighted) and then press shift+enter which also moves focus to the next cell or ctrl+enter which does... | github_jupyter |
[View in Colaboratory](https://colab.research.google.com/github/PranY/FastAI_projects/blob/master/TSG.ipynb)
```
!pip install fastai
!pip install torch_nightly -f https://download.pytorch.org/whl/nightly/cu92/torch_nightly.html
! pip install kaggle
! pip install tqdm
from google.colab import drive
drive.mount('/conten... | github_jupyter |
```
import pandas as pd #数据分析
import numpy as np #科学计算
from pandas import Series,DataFrame
data_train = pd.read_csv("/Users/zhijun/Desktop/Titanic/all/train.csv")
data_train.columns
data_train.info()
data_train.describe()
import matplotlib.pyplot as plt
fig = plt.figure()
fig.set(alpha=0.2) # 设定图表颜色alpha参数
plt.subpl... | github_jupyter |
# Measuring Monotonic Relationships
By Evgenia "Jenny" Nitishinskaya and Delaney Granizo-Mackenzie with example algorithms by David Edwards
Reference: DeFusco, Richard A. "Tests Concerning Correlation: The Spearman Rank Correlation Coefficient." Quantitative Investment Analysis. Hoboken, NJ: Wiley, 2007
Part of the ... | github_jupyter |
# Spacy
### Models
Spacy comes with a variety of different models that can used per language. For instance, the models for English are available [here](https://spacy.io/models/en). You'll need to download each model separately:
```python
python3 -m spacy download en_core_web_sm
python3 -m spacy download en_core_web_... | github_jupyter |
# In this notebook an estimator for the Volume will be trained. No hyperparameters will be searched for, and the ones from the 'Close' values estimator will be used instead.
```
# Basic imports
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
import scipy.optimize ... | github_jupyter |
## Dependencies
```
import json, glob
from tweet_utility_scripts import *
from tweet_utility_preprocess_roberta_scripts_aux import *
from transformers import TFRobertaModel, RobertaConfig
from tokenizers import ByteLevelBPETokenizer
from tensorflow.keras import layers
from tensorflow.keras.models import Model
```
# L... | github_jupyter |
# Monitoring Data Drift
Over time, models can become less effective at predicting accurately due to changing trends in feature data. This phenomenon is known as *data drift*, and it's important to monitor your machine learning solution to detect it so you can retrain your models if necessary.
In this lab, you'll conf... | github_jupyter |
# Self-Driving Car Engineer Nanodegree
## Project: **Finding Lane Lines on the Road**
***
In this project, you will use the tools you learned about in the lesson to identify lane lines on the road. You can develop your pipeline on a series of individual images, and later apply the result to a video stream (really j... | github_jupyter |
[View in Colaboratory](https://colab.research.google.com/github/thonic92/chal_TM/blob/master/model_tweets.ipynb)
```
import json
import numpy as np
import pandas as pd
import tensorflow as tf
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import LS... | github_jupyter |
<div align="center">
<h1>Homework 7</h1>
<p>
<div align="center">
<h2>Yutong Dai yutongd3@illinois.edu</h2>
</div>
</p>
</div>
## 6.33
The dual problem is
$$
\begin{align}
& \min \quad 3 w_1 + 6 w_2\\
& s.t \quad w_1 + 2w_2 \geq 2\\
& \qquad w_1 + 3w_2 \geq -3\\
& \qquad w_1\leq 0,... | github_jupyter |
# Assignment: Global average budgets in the CESM pre-industrial control simulation
## Learning goals
Students completing this assignment will gain the following skills and concepts:
- Continued practice working with the Jupyter notebook
- Familiarity with atmospheric output from the CESM simulation
- More complete c... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import butcher
import bro
import os
from astropy.io import ascii
from astropy.timeseries import LombScargle
#Reading in the data
#Get the data directory
cwd = os.getcwd()
data_dir = cwd.replace('Figure_4', 'Data\\')
#ASAS data
orgasas_data = ascii.read(data_dir ... | github_jupyter |
# IMDb Movie Reviews Classifier
```
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemme... | github_jupyter |
# Time Series Modeling
In this lecture, we'll do some **basic** work with time series modeling. Time series are surprisingly complicated objects to work with and model, and many people spend their careers considering statistical questions related to effective modeling of timeseries. In this set of lecture notes, we wo... | github_jupyter |
```
import numpy as np
import pandas as pd
import math
from math import sin, cos, radians
import os
import matplotlib.pyplot as plt
import datetime
import scipy.stats as st
import scipy.signal as sgl
pd.set_option('display.max_columns', 500)
#import fastdtw
from scipy.spatial.distance import euclidean
from fastdtw i... | github_jupyter |
# SentencePiece and BPE
## Introduction to Tokenization
In order to process text in neural network models it is first required to **encode** text as numbers with ids, since the tensor operations act on numbers. Finally, if the output of the network is to be words, it is required to **decode** the predicted tokens ids... | github_jupyter |
### *IPCC SR15 scenario assessment*
<img style="float: right; height: 80px; padding-left: 20px;" src="../_static/IIASA_logo.png">
<img style="float: right; height: 80px;" src="../_static/IAMC_logo.jpg">
# Characteristics of four illustrative model pathways
## Figure 3b of the *Summary for Policymakers*
This notebook... | github_jupyter |
<h1 align=center><font size = 6> Crop Yield Prediction. </font></h1>
## import required libraries.
```
import numpy as np #Library to handle data in vectorized manner.
import pandas as pd #library for data analysis.
#Plotting libray matplotlib and associated ploting modules.
import matplotlib.pyplot as plt
impo... | github_jupyter |
# MaterialsCoord benchmarking – sensitivity to perturbation analysis
This notebook demonstrates how to use MaterialsCoord to benchmark the sensitivity of bonding algorithms to structural perturbations. Perturbations are introduced according the Einstein crystal test rig, in which site is perturbed so that the distribu... | github_jupyter |
<a href="https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_12_02_qlearningreinforcement.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# T81-558: Applications of Deep Neural Networks
**Module 12: Re... | github_jupyter |
# BikeBuyer Regression
```
# importing libraries
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import numpy.random as nr
import math
%matplotlib inline
# loading data
customer_info = pd.read_csv('Data/AdvWorksCusts.csv')
customer_spending = pd.read_csv('Data/AW_A... | github_jupyter |
___
<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
___
# NumPy Exercises
Now that we've learned about NumPy let's test your knowledge. We'll start off with a few simple tasks, and then you'll be asked some more complicated questions.
#### Import NumPy as np
```
import numpy as np... | github_jupyter |
```
"""
Update Parameters Here
"""
COLLECTION_NAME = "Quaks"
CONTRACT = "0x07bbdaf30e89ea3ecf6cadc80d6e7c4b0843c729"
BEFORE_TIME = "2021-09-02T00:00:00" # One day after the last mint (e.g. https://etherscan.io/tx/0x206c846d0d1739faa9835e16ff419d15708a558357a9413619e65dacf095ac7a)
# these should usually stay the same
... | github_jupyter |
```
from matplotlib import pyplot as plt
%matplotlib notebook
from matplotlib import animation
import numpy as np
#make a fake galaxy distribution from a MOG
mean1, std1 = (np.random.rand()*2-1, np.random.rand()*2-1), (np.random.rand()*3+0.5, np.random.rand()*3+0.5)
mean2, std2 = (np.random.rand()*2+1, np.random.rand()... | github_jupyter |
## INTRODUCTION
- It’s a Python based scientific computing package targeted at two sets of audiences:
- A replacement for NumPy to use the power of GPUs
- Deep learning research platform that provides maximum flexibility and speed
- pros:
- Iinteractively debugging PyTorch. Many users who have used both fr... | github_jupyter |
Random Sampling
=============
Copyright 2016 Allen Downey
License: [Creative Commons Attribution 4.0 International](http://creativecommons.org/licenses/by/4.0/)
```
from __future__ import print_function, division
import numpy
import scipy.stats
import matplotlib.pyplot as pyplot
from ipywidgets import interact, i... | github_jupyter |
# Tutorial: CommonRoad Route Planner
This tutorial demonstrates how the CommonRoad Route Planner package can be used to plan high-level routes for planning problems given in CommonRoad scenarios.
## 0. Preparation
* you have gone through the tutorial for **CommonRoad Input-Output**
* you have installed the [route pla... | github_jupyter |
# Porting genome scale metabolic models for metabolomics
**rat-GEM as default rat model, for better compatibility**
https://github.com/SysBioChalmers/rat-GEM
**Use cobra to parse SBML models whereas applicable**
Not all models comply with the formats in cobra. Models from USCD and Thiele labs should comply.
**Base ... | github_jupyter |
```
#The birth of skynet, always good to start with a joke
print("hello World!")
# Dependencies
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import os
import scipy.stats as st
import numpy as np
import requests
import time
import gmaps
import json
from pprint import pprint
from statsmodels.... | github_jupyter |
# TTV Retrieval for Kepler-36 (a well-studied, dynamically-interacting system)
In this notebook, we will perform a dynamical retrieval for Kepler-36 = KOI-277. With two neighboring planets of drastically different densities (the inner planet is rocky and the outer planet is gaseous; see [Carter et al. 2012](https://ui... | github_jupyter |
```
# group_by_SNR.ipynb
# Many stars that have mulitple APF spectra have some spectra from different nights of observation.
# Calculates the SNR for each group of spectra from one night of observing (calc_SNR combines all observations of one
# star and returns an SNR for the star instead), then finds for each star w... | github_jupyter |
<a href="https://colab.research.google.com/github/kishkath/Data_Structures-Hashing-/blob/main/Tensorflow_fundamentals_withoutcode.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# **Roadmap of this assignment**
**This assignment is divided into fol... | github_jupyter |
<a href="https://colab.research.google.com/github/AmberLJC/FedScale/blob/master/dataset/Femnist_stats.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# **[Jupyter notebook] Understand the heterogeneous FL data.**
# Download the Femnist dataset and ... | github_jupyter |
```
# Code ported from laptop onto 10.12.68.72 starting on 8/24/2020 (Gregory Rouze)
# To-do:
# 1) need to separate user functions and main code - I have done this successfully in the offline version, but I'm having a
# little more trouble in the cloud version
# 2) Add comments on putpose of individual user functions... | github_jupyter |
## Code for policy section
```
# Load libraries
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mlp
# Ensure type 1 fonts are used
mlp.rcParams['ps.useafm'] = True
mlp.rcParams['pdf.use14corefonts'] = True
mlp.rcParams['text.usetex'] = True
import seaborn as sns
import pandas as pd
import pic... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
```
# Sampling from a Bayesian network: an open problem
A Bayesian network encodes a probability distribution. It is often desirable to be able to sample from a Bayesian network. The most common way to do this is via forward sampling (also called prior sampling). It's a really d... | github_jupyter |
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