code stringlengths 2.5k 150k | kind stringclasses 1
value |
|---|---|
# Overview
Ensemble combined with LDA is effective in predicting age based on gene expression data. However, this method is prone to batch problem. The batch problem may caused by the different techniques in breeding cells that lead to difference in the mean gene expression level of cells between batches.
kTSP is a ... | github_jupyter |
# `layers.loss`
```
%reload_ext autoreload
%autoreload 2
# %load ../../HPA-competition-solutions/bestfitting/src/layers/loss.py
#default_exp layers.loss
#export
import math
from torch import nn
import torch.nn.functional as F
from kgl_humanprotein.config.config import *
from kgl_humanprotein.layers.hard_example impo... | github_jupyter |
## Load Library And Data
```
# importing the library
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# to know the ecoding type
import chardet
with open('E:\\Recommendation System\\book.csv', 'rb') as rawdata:
result = chardet.detect(rawdata.read(100000))
result
```
- ... | github_jupyter |
# Example Feature-Based Cluster Queries
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
import os
import sys
module_path = os.path.abspath(os.path.join('..'))
if module_path not in sys.path:
sys.path.append(module_path)
import h5py
import math
import numpy as np
chrom = 'chr7'
bin_size = 100000
cluste... | github_jupyter |
## Eng+Wales well-mixed example model
This is the inference notebook with increased inference window. There are various model variants as encoded by `expt_params_local` and `model_local`, which are shared by the notebooks in a given directory.
Outputs of this notebook:
(same as `inf` notebook with added `tWin` labe... | github_jupyter |
```
#Use this command to run it on floydhub: floyd run --gpu --env tensorflow-1.4 --data emilwallner/datasets/imagetocode/2:data --data emilwallner/datasets/html_models/1:weights --mode jupyter
from os import listdir
from numpy import array
from keras.preprocessing.text import Tokenizer, one_hot
from keras.preprocessin... | github_jupyter |
# import required library
```
# Import numpy, pandas for data manipulation
import numpy as np
import pandas as pd
# Import matplotlib, seaborn for visualization
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
# Import the data
weather_data = pd.read_csv('weather... | github_jupyter |
# Seattle Airbnb
My significant foci are listing and calendar to display data from my business understanding.
* Read dataset - read csv files to pandas dataframe.
* Data manipulation - data cleaning and data wrangling to make quality data to visualization .
* Exploratory data analysis (EDA) - Data visualization... | github_jupyter |
```
#Relevant video:
#http://www.youtube.com/watch?v=VIt2z6zJrMs&t=1m52s
#My output from code:
#https://www.youtube.com/watch?v=E_yE2Q0ArpM
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import numpy as np
import scipy.integrate as ing
d2r = np.pi/180. #deg to radian
k2f = 1.68781 #knots ... | github_jupyter |
# Pandeia for WFIRST Imaging
How to cite this code:
> Klaus M. Pontoppidan ; Timothy E. Pickering ; Victoria G. Laidler ; Karoline Gilbert ; Christopher D. Sontag, et al.
"Pandeia: a multi-mission exposure time calculator for JWST and WFIRST", Proc. SPIE 9910, Observatory Operations: Strategies, Processes, and System... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W2D2_LinearSystems/student/W2D2_Tutorial3.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Tutorial 3: Combining determinism and stochasticity
... | github_jupyter |
# String
## `print()`
Fungsi `print()` mencetak seluruh argumennya sebagai *string*, dipisahkan dengan spasi dan diikuti dengan sebuah *line break*:
```
name = "Budi"
print("Hello World")
print("Hello", 'World')
print("Hello", name)
```
> Catatan: Fungsi untuk mencetak di Python 2.7 dan Python 3 berbeda. Di Python 2... | github_jupyter |
<style>div.container { width: 100% }</style>
<img style="float:left; vertical-align:text-bottom;" height="65" width="172" src="../assets/holoviz-logo-unstacked.svg" />
<div style="float:right; vertical-align:text-bottom;"><h2>Tutorial 5. Interactive Pipelines</h2></div>
The plots built up over the first few tutorials... | github_jupyter |
```
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
x = np.linspace(0, 5, 11)
y = x ** 2
x
y
#Functional
plt.plot(x,y,"r")
plt.xlabel("X Axis")
plt.ylabel("Y Axis")
plt.title("Title")
plt.show()
plt.subplot(1,2,1)
plt.plot(x,y,"r-")
plt.subplot(1,2,2)
plt.plot(y,x,"g*-")
# OOP Method
fig = plt.fig... | github_jupyter |
# Cell Basic Filtering
## Content
The purpose of this step is to get rid of cells having **obvious** issues, including the cells with low mapping rate (potentially contaminated), low final reads (empty well or lost a large amount of DNA during library prep.), or abnormal methylation fractions (failed in bisulfite conv... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive')
```
`cd /content/drive/My\ Drive/Transformer-master/` -> `cd /content/drive/My\ Drive/Colab\ Notebooks/Transformer`
```
cd /content/drive/My\ Drive/Colab\ Notebooks/Transformer
```
# ライブラリ読み込み
```
!apt install aptitude
!aptitude install mecab libmec... | github_jupyter |
```
%matplotlib inline
import numpy as np
import scipy.stats as stats
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import random
import statsmodels.api as sm
sns.set(style="whitegrid")
```
# Example
We're now in a position to return to our housing data for King County, Washington to make... | github_jupyter |
```
# i 可能的取值：0、2、4、6、len(A)
from collections import Counter
class Solution:
def canReorderDoubled(self, A):
if not A: return True
a_freq = Counter(A)
seen = set()
for a in A:
if a in seen: continue
if a_freq[a] == 0:
seen.add(a)
... | github_jupyter |
# Recommendations with IBM
In this notebook, you will be putting your recommendation skills to use on real data from the IBM Watson Studio platform.
You may either submit your notebook through the workspace here, or you may work from your local machine and submit through the next page. Either way assure that your ... | github_jupyter |
# Part 1: Data Wrangling
## Introduction
This project is a self-made end to end machine learning project in which I scrape a website called 'Jendela 360'. The scraped dataset is saved in a csv file named 'Apartment Data Raw'. The dataset contains the details of apartment units available to be rented in Jakarta and it... | github_jupyter |
# Reference
To run this code you will need to install [Matplotlib](https://matplotlib.org/users/installing.html) and [Numpy](https://www.scipy.org/install.html)
If you like to run the example locally follow the instructions provided on [Keras website](https://keras.io/#installation)
It's __strongly__ suggested to us... | github_jupyter |
# ELG Signal-to-Noise Calculations
This notebook provides a standardized calculation of the DESI emission-line galaxy (ELG) signal-to-noise (SNR) figure of merit, for tracking changes to simulation inputs and models. See the accompanying technical note [DESI-3977](https://desi.lbl.gov/DocDB/cgi-bin/private/ShowDocume... | github_jupyter |
```
#hide
#default_exp cli
from nbdev.showdoc import show_doc
```
# Command line functions
> Console commands added by the nbdev library
```
#export
from nbdev.imports import *
from chisel_nbdev.export_scala import *
from chisel_nbdev.sync_scala import *
from nbdev.merge import *
from chisel_nbdev.export_scala2html ... | github_jupyter |
<a href="https://colab.research.google.com/github/probml/pyprobml/blob/master/book1/intro/pandas_intro.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Manipulating and visualizing tabular data using pandas
[Pandas](https://pandas.pydata.org/) is... | github_jupyter |
# Maxpooling Layer
In this notebook, we add and visualize the output of a maxpooling layer in a CNN.
A convolutional layer + activation function, followed by a pooling layer, and a linear layer (to create a desired output size) make up the basic layers of a CNN.
<img src='notebook_ims/CNN_all_layers.png' height=50%... | github_jupyter |
```
# default_exp filter
#hide
from nbdev.showdoc import *
#hide
# stellt sicher, dass beim verändern der core library diese wieder neu geladen wird
%load_ext autoreload
%autoreload 2
```
# 01_06_Pivot_BS_Data
In this notebook, we will transform the verticalized data rows of the BalanceSheet into a horizontalized dat... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import random
#using the monte carlo method to approximate the value of pi
N_array = np.arange(1,5000)
pi_array = []
x_array_points = []
y_array_points = []
for n in N_array:
num_in = 0
num_out = 0
for i in range(n):
x =... | github_jupyter |
Analysing GPS data from Jaume University
Defining functions
```
import json
import math
import numpy as np
import matplotlib.pyplot as plt
def getmeasurementTimestamp(item):
return int(item['measurementTimestamp'])
def getProcessingTimestamp(item):
return int(item['processingTimestamp'])
def get_x_error(it... | github_jupyter |
# Face Generation
In this project, you'll define and train a DCGAN on a dataset of faces. Your goal is to get a generator network to generate *new* images of faces that look as realistic as possible!
The project will be broken down into a series of tasks from **loading in data to defining and training adversarial net... | github_jupyter |
```
%matplotlib inline
import pymc3 as pm
import numpy as np
import pandas as pd
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
palette = 'muted'
sns.set_palette(palette); sns.set_color_codes(palette)
np.set_printoptions(precision=2)
```
# Simple example
```
clusters = 3
n_cluster ... | github_jupyter |
```
"""
You can run either this notebook locally (if you have all the dependencies and a GPU) or on Google Colab.
Instructions for setting up Colab are as follows:
1. Open a new Python 3 notebook.
2. Import this notebook from GitHub (File -> Upload Notebook -> "GITHUB" tab -> copy/paste GitHub URL)
3. Connect to an in... | github_jupyter |
# 빠른 학습을 위한 tfrecords 데이터셋 생성
- 컴페티션 기본 데이터는 data/public 하위 폴더에 있다고 가정합니다. (train.csv, sample_submission.csv, etc)
- 또한 train.zip, test.zip 역시 data/public 하위에 압축을 풀어놓았다고 가정하고 시작하겠습니다.
```
import os
import os.path as pth
import json
import shutil
import pandas as pd
from tqdm import tqdm
data_base_path = pth.join('dat... | github_jupyter |
# Batch Normalization – Solutions
Batch normalization is most useful when building deep neural networks. To demonstrate this, we'll create a convolutional neural network with 20 convolutional layers, followed by a fully connected layer. We'll use it to classify handwritten digits in the MNIST dataset, which should be ... | github_jupyter |
## In situ data and trajectories incl. Bepi Colombo, PSP, Solar Orbiter
https://github.com/cmoestl/heliocats
Author: C. Moestl, IWF Graz, Austria
twitter @chrisoutofspace, https://github.com/cmoestl
last update: 2021 August 24
needs python 3.7 with the conda helio environment (see README.md)
uses heliopy for ge... | github_jupyter |
```
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
import torch
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
from lightgbm import LGBMRegressor
from sklearn.metrics import mean_... | github_jupyter |
this notebook will be used to show the performance of the first attempt at learning reward.
first load the trained reward network anbd setup methods.
```
from baselines.common.vec_env import VecFrameStack
from LearningModel.AgentClasses import *
from baselines.common.cmd_util import make_vec_env
import tensorflow as ... | github_jupyter |
# 深度学习工具 PyTorch 简介
在此 notebook 中，你将了解 [PyTorch](http://pytorch.org/)，一款用于构建和训练神经网络的框架。PyTorch 在很多方面都和 Numpy 数组很像。毕竟，这些 Numpy 数组也是张量。PyTorch 会将这些张量当做输入并使我们能够轻松地将张量移到 GPU 中，以便在训练神经网络时加快处理速度。它还提供了一个自动计算梯度的模块（用于反向传播），以及另一个专门用于构建神经网络的模块。总之，与 TensorFlow 和其他框架相比，PyTorch 与 Python 和 Numpy/Scipy 堆栈更协调。
## 神经网络
深度学习以人工神经网络为... | github_jupyter |
# Project 3: Implement SLAM
---
## Project Overview
In this project, you'll implement SLAM for robot that moves and senses in a 2 dimensional, grid world!
SLAM gives us a way to both localize a robot and build up a map of its environment as a robot moves and senses in real-time. This is an active area of research... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from bokeh.plotting import *
from sklearn.cluster.bicluster import SpectralCoclustering
from bokeh.models import HoverTool, ColumnDataSource
from itertools import product
whisky = pd.read_csv('whiskies.txt')
whisky["Region"] = pd.read_csv('regio... | github_jupyter |
```
import datetime as dt
import panel as pn
pn.extension()
```
The ``DateRangeSlider`` widget allows selecting a date range using a slider with two handles.
For more information about listening to widget events and laying out widgets refer to the [widgets user guide](../../user_guide/Widgets.ipynb). Alternatively y... | github_jupyter |
### Imports
```
import pandas as pd
import os
import numpy as np
from category_encoders import TargetEncoder
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import Lasso
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import Standar... | github_jupyter |
**Important**: Click on "*Kernel*" > "*Restart Kernel and Clear All Outputs*" *before* reading this chapter in [JupyterLab <img height="12" style="display: inline-block" src="static/link_to_jp.png">](https://jupyterlab.readthedocs.io/en/stable/)
# An Introduction to Python and Programming
This course is a *thorough* ... | github_jupyter |
```
import os
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
from tensorflow.keras import datasets
from pyvizml import CreateNBAData
import requests
from sklearn.linear_model import LinearRegression
from sklearn.model_se... | github_jupyter |
# MBZ-XML-TO-EXCEL
First pubished version May 22, 2019. This is version 0.0004 (revision July 26, 2019)
Licensed under the NCSA Open source license
Copyright (c) 2019 Lawrence Angrave
All rights reserved.
Developed by: Lawrence Angrave
Permission is hereby granted, free of charge, to any person obtaining a copy ... | github_jupyter |
# **DBSCAN**
## **Implementacion**
```
import numpy as np
import matplotlib.pyplot as plt
from math import e, inf
from random import randint, uniform
from sklearn.datasets import make_circles
```
### KNN
```
class Node:
def __init__(self, parent, x, area):
self.parent = parent
self.x = x
self.childs ... | github_jupyter |
# HOMEWORK 2 - ADM
```
import pandas as pd
import matplotlib.pyplot as plt
import methods
import datetime
```
## READ THE DATA
```
df_names = ["./datasets/2019-Nov.csv", "./datasets/2019-Oct.csv"]
```
## UNDERSTAND THE DATA
The data that we handle for this homework come from an online store. We are going to analyz... | github_jupyter |
# Histograms
This notebook demonstrates simple use of histograms in wn.
### Set up libraries and load exemplar dataset
```
# load libraries
import os
import opendp.whitenoise.core as wn
import numpy as np
import math
import statistics
# establish data information
data_path = os.path.join('.', 'data', 'PUMS_californi... | github_jupyter |
```
import os
import matplotlib.pyplot as plt
%matplotlib inline
import torch
import torchvision.transforms as T
import numpy as np
from PIL import Image
```
## Verifying image loading is the right format
```
path = '/home/yamins/.local/lib/python3.7/site-packages/model_tools/check_submission/images'
from model_tools... | github_jupyter |
```
!pip install econml
# Some imports to get us started
import warnings
warnings.simplefilter('ignore')
# Utilities
import os
import urllib.request
import numpy as np
import pandas as pd
from networkx.drawing.nx_pydot import to_pydot
from IPython.display import Image, display
# Generic ML imports
from sklearn.prepro... | github_jupyter |
# _*Qiskit Finance: Loading and Processing Stock-Market Time-Series Data*_
The latest version of this notebook is available on https://github.com/qiskit/qiskit-tutorial.
***
### Contributors
Jakub Marecek<sup>[1]</sup>
### Affiliation
- <sup>[1]</sup>IBMQ
### Intr... | github_jupyter |
##### Copyright 2021 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 |
# Here we will learn topics like
1. Universal function
2. aggreagate function
3. Broadcasting
## Universal function
1. A big difference between python array and numpy array is execution speed.
2. python array iterate through each element and then process it.
3. numpy array use the concept of vectorized operation, wh... | github_jupyter |
Azure ML & Azure Databricks notebooks by Parashar Shah.
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
We support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as y... | github_jupyter |
# 🔢 Vectorizing Guide
Firstly, we must import what we need from Relevance AI
```
from relevanceai import Client
from relevanceai.utils.datasets import (
get_iris_dataset,
get_palmer_penguins_dataset,
get_online_ecommerce_dataset,
)
client = Client()
```
## Example 1
For this first example we going to w... | github_jupyter |
# **Amazon Lookout for Equipment** - Getting started
*Part 6 - Cleanup*
## Initialization
---
This repository is structured as follow:
```sh
. lookout-equipment-demo
|
├── data/
| ├── interim # Temporary intermediate data are stored here
| ├── processed # Finalized ... | github_jupyter |
# Convert a SolidMesh into its BoundaryRepresentation
The goal is to transform a volumetric mesh into a model as defined here: https://docs.geode-solutions.com/datamodel
The core of the problem is to identify and to extract the topological information from the mesh.
There are two ways to realize this identification:
-... | github_jupyter |
```
import pandas as pd # To convert data into pandas dataframe
import numpy as np # For data and large type of arrays manipulation
import matplotlib.pyplot as plt #For data visualisation
import seaborn as sns # For data visualisation
import plotly.express as px #For data visualisation
```
# Data Preprocessing
```
# ... | github_jupyter |
1. Recap
==
In the last mission, we explored how to use a simple k-nearest neighbors machine learning model that used just one feature, or attribute, of the listing to predict the rent price. We first relied on the <span style="background-color: #F9EBEA; color:##C0392B">accommodates</span> column, which describes the ... | github_jupyter |
```
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import sklearn
import pandas as pd
import os
import sys
import time
import tensorflow as tf
from tensorflow import keras
print(tf.__version__)
print(sys.version_info)
for module in mpl, np, pd, sklearn, tf, keras:
p... | github_jupyter |
# Problem set 5
### Copied database from /blue/bsc4452/share/Class_Files
```
# Import only the modules needed from sqlalchemy
from sqlalchemy import create_engine
from sqlalchemy import MetaData
from sqlalchemy import Table, Column
from sqlalchemy import Integer, String
from sqlalchemy import sql, select, join, desc
f... | github_jupyter |
```
#convert
```
# babilim.model.layers.roi_ops
> Operations for region of interest extraction.
```
#export
from babilim.core.annotations import RunOnlyOnce
from babilim.core.module_native import ModuleNative
#export
def _convert_boxes_to_roi_format(boxes):
"""
Convert rois into the torchvision format.
... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import sys
import pathlib
sys.path.append(str(pathlib.Path().cwd().parent))
from typing import Tuple
from load_dataset import Dataset
from plotting import plot_ts
dataset = Dataset('../data/dataset/')
```
### В чем заключаются недостатки полносвязных сетей?
* невозможность ула... | github_jupyter |
# Milestone2 Document
## Feedback
- Introduction: A nice introduction!
- Background -0.5: It would be hard for users to understand automatic differentiation, computational graph, and evaluation trace if you don't give the corresponding illustrations in the Background section
**Revision: provided a concrete ex... | github_jupyter |
```
import pandas as pd
from joblib import dump, load
import os
#set up directory
#os.chdir()
#Drug dic
#open file
df_drugs=pd.read_csv(r"C:\Users\mese4\Documents\The Data incubator\project\Drugmap\drugbank vocabulary.csv", encoding='ISO-8859-1')
synonyms = []
drug_names = df_drugs['Common_name'].tolist()
drug_names... | github_jupyter |
# Introduction to reproducibility and power issues
## Some Definitions
* $H_0$ : null hypothesis: The hypotheis that the effect we are testing for is null
* $H_A$ : alternative hypothesis : Not $H_0$, so there is some signal
* $T$ : The random variable that takes value "significant" or "not significant"
* $T_S$ : ... | github_jupyter |
```
import os
import torch
import numpy as np
import pickle
import matplotlib.pyplot as plt
from torch.optim.lr_scheduler import LambdaLR, StepLR
#@title
import gzip
import html
import os
from functools import lru_cache
import ftfy
import regex as re
@lru_cache()
def bytes_to_unicode():
"""
Returns list of ... | github_jupyter |
## Libraries
```
import pandas as pd
import numpy as np
import scipy.stats as stat
from math import sqrt
from mlgear.utils import show, display_columns
from surveyweights import normalize_weights, run_weighting_iteration
def margin_of_error(n=None, sd=None, p=None, type='proportion', interval_size=0.95):
z_look... | github_jupyter |
# Live Twitter Sentiments for Cryptocurrencies
Plot the evolution in time of the tweets sentiment for a cryptocurrency. We will use the *tweepy*'s streaming to see the live evolution of the Twitter sentiments for the cryptocurrencies.
* *Inputs*: currency keywords to seach in Twitter, number of tweets to analyse the ... | github_jupyter |
# CNN MNIST
```
#importing functions from python3 to python2
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
#importing numpy and tensorflow
import numpy as np
import tensorflow as tf
#ignore all the warnings and don't show them in the notebook
import warn... | github_jupyter |
### AD470 - Module 7 Introduction to Deep LearningProgramming Assignment
#### Andrew Boyer
#### Brandan Owens
```
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import scipy.io
from sklearn.preprocessing import StandardScaler
import tensorflow
from tensorflow import keras... | github_jupyter |
# OptNet/qpth Example Sudoku Notebook
*By [Brandon Amos](https://bamos.github.io) and [J. Zico Kolter](http://zicokolter.com/).*
---
This notebook is released along with our paper
[OptNet: Differentiable Optimization as a Layer in Neural Networks](https://arxiv.org/abs/1703.00443).
This notebook shows an example of... | github_jupyter |
```
#IMPORT SEMUA LIBARARY
#IMPORT LIBRARY PANDAS
import pandas as pd
#IMPORT LIBRARY UNTUK POSTGRE
from sqlalchemy import create_engine
import psycopg2
#IMPORT LIBRARY CHART
from matplotlib import pyplot as plt
from matplotlib import style
#IMPORT LIBRARY BASE PATH
import os
import io
#IMPORT LIBARARY PDF
from fpdf im... | github_jupyter |
# Index对象的创建,、查、改、增、删和使用
想要用好pandas，必须了解其核心对象之一的**索引**。
- 索引类似于元组，其本身是不能赋值修改的；
- 其在数据进行整体运算时，辅助自动对齐，这是pandas不同于其他数据处理库的一大特征；
- 多层索引可以帮助改变表的形态，如透视表等。
所以，这一章要仔细学习。
```
import numpy as np
import pandas as pd
```
# 1. 单层索引
## 1.1 创建
##### `pd.Index(data, dtype=Object, name=None)`
- name：一维列表
- dtype：索引元素的类型，默认为object型
... | github_jupyter |
```
%matplotlib inline
import numpy as np
import pandas as pd
import os
import sys
sys.path.append('..')
import geopandas as gpd
from shapely.geometry import Point
from shapely.geometry import LineString
from shapely.geometry import MultiPoint
GAS_STATIONS_PATH = os.path.join('..', 'data', 'raw', 'input_data', 'Eingabe... | github_jupyter |
```
"""
You can run either this notebook locally (if you have all the dependencies and a GPU) or on Google Colab.
Instructions for setting up Colab are as follows:
1. Open a new Python 3 notebook.
2. Import this notebook from GitHub (File -> Upload Notebook -> "GITHUB" tab -> copy/paste GitHub URL)
3. Connect to an in... | github_jupyter |
# Jupyter UX Survey 2015 - Initial Sandbox
* Goal: Start looking at how we can surface insights from the data.
* Description: https://github.com/jupyter/surveys/tree/master/surveys/2015-12-notebook-ux
* Data: https://raw.githubusercontent.com/jupyter/surveys/master/surveys/2015-12-notebook-ux/20160115235816-SurveyExpo... | github_jupyter |
# Objective
Build a binary classifier that given a sequence of lap times will predict if a pit-stop will happen or not the next lap .. in other words I call this project End-of-Stint-or-NOT
Data Source:
- Ergast Developer API: https://ergast.com/mrd/
## Table of Content:
* [Data Preparation](#Section1)
* [Import ... | github_jupyter |
<a href="https://colab.research.google.com/github/Nadda1004/Intro_Machine_learning/blob/main/W1_D1_ML_HeuristicModel.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Predicting Rain in Seattle
Seattle is one of the rainiest places in the world. Ev... | github_jupyter |
---
_You are currently looking at **version 1.0** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-text-mining/resources/d9pwm) course resource._
---
*Note: Some of the cell... | github_jupyter |
<b>Detection of Sargassum on the coast and coastal waters</b>
Notebook for classifying and analyzing Sargassum in Bonaire with Sentinel-2 images
* Decision Tree Classifier (DTC) and Maximum Likelihood Classifier (MLC) are employed
* Training sites covering 8 different classes are used to extract pixel values (traini... | github_jupyter |
```
import pandas as pd
and_data = pd.read_csv('ANDHRA_PD.csv')
and_data.head()
del_data = pd.read_csv('DELHI.csv')
del_data.head()
kar_data = pd.read_csv('KARNATAKA.csv')
kar_data.head()
mah_data = pd.read_csv('MAHARASHTRA.csv')
mah_data.head()
tam_data = pd.read_csv('TAMIL_NADU.csv')
tam_data.head()
utt_data = pd.rea... | github_jupyter |
# Anisha Parikh
## Research question/interests
My research question is what are the top 10 most remembered songs and the bottom 10 least remembered songs. As well as how does recollection of the songs compare across generations.
Imports
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
impor... | github_jupyter |
# Hands-on 2: How to create a fMRI analysis workflow
The purpose of this section is that you setup a fMRI analysis workflow.
# 1st-level Analysis Workflow Structure
In this notebook we will create a workflow that performs 1st-level analysis and normalizes the resulting beta weights to the MNI template. In concrete s... | github_jupyter |
```
import scipy.io as sio
mat = sio.loadmat("./imdb/imdb.mat")
from IPython.core.display import Image
idx = 11114
path ='./imdb_crop/' + mat['imdb'].item()[2][0][idx][0]
print(mat['imdb'].item()[4][0][idx][0])
print(mat['imdb'].item()[2][0][idx][0])
Image(filename=path)
import numpy
embeddings = numpy.load('./embeddi... | github_jupyter |
## UCI SMS Spam Collection Dataset
* **Input**: sms textual content. **Target**: ham or spam
* **data representation**: each sms is repesented with a **fixed-length vector of word indexes**. A word index lookup is generated from the vocabulary list.
* **words embedding**: A word embedding (dense vector) is learnt for ... | github_jupyter |
# Assignment 1: Neural Networks
Implement your code and answer all the questions. Once you complete the assignment and answer the questions inline, you can download the report in pdf (File->Download as->PDF) and send it to us, together with the code.
**Don't submit additional cells in the notebook, we will not check... | github_jupyter |
```
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
import numpy as np
import scipy.stats as stats
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import random
import patsy
sns.set(style="whitegrid")
```
# Logistic Regression
In the last section, we looked at how we ca... | github_jupyter |
# More Pandas
```
# Load the necessary libraries
import pandas as pd
%matplotlib inline
```
## Vectorized String Operations
* There is a Pandas way of doing this that is much more terse and compact
* Pandas has a set of String operations that do much painful work for you
* Especially handling bad data!
```
data = [... | github_jupyter |
<a href="https://colab.research.google.com/github/lmiroslaw/DeOldify/blob/master/VideoColorizerColab.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
### **<font color='blue'> Video Colorizer </font>**
#◢ DeOldify - Colorize your own videos!
_FYI:... | github_jupyter |
# $\lambda$对CMA性能影响研究
<link rel="stylesheet" href="http://yandex.st/highlightjs/6.2/styles/googlecode.min.css">
<script src="http://code.jquery.com/jquery-1.7.2.min.js"></script>
<script src="http://yandex.st/highlightjs/6.2/highlight.min.js"></script>
<script>hljs.initHighlightingOnLoad();</script>
<script type="... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W3D1_BayesianDecisions/W3D1_Tutorial3.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Bonus Tutorial : Fitting to data
**Week 3, Day 1: Bayesi... | github_jupyter |
This material should help you get the ideas clearer from the first meeting:
```
names=["Tomás", "Pauline", "Pablo", "Bjork","Alan","Juana"]
woman=[False,True,False,False,False,True]
ages=[32,33,28,30,32,27]
country=["Chile", "Senegal", "Spain", "Norway","Peru","Peru"]
education=["Bach", "Bach", "Master", "PhD","Bach",... | github_jupyter |
# Graphing network packets
This notebook currently relies on HoloViews 1.9 or above. Run `conda install -c ioam/label/dev holoviews` to install it.
## Preparing data
The data source comes from a publicly available network forensics repository: http://www.netresec.com/?page=PcapFiles. The selected file is https://dow... | github_jupyter |
# Tutorial 1: Part 2
Objectives:
- Learn how to define a simple lattice and compute the TWISS functions using MAD-X.
- Thick vs thin lens approximation TWISS comparison for a lattice with only quadrupoles.
- Tune and $\beta$-function dependence on K1.
**My first accelerator: a FODO cell**
1. Make a simple lattice FO... | github_jupyter |
# QuickSort
- Based on Divide and Conquer Technique
- The array is divided into **Partitions Recursively**
- The technique to create the partitions is the **backbone** of this Algorithm
### QuickSort - What it is
In Short:
- 1. Define a Pivot element ( can be first element, last element or any random element from... | 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 |
```
# Copyright 2021 Google LLC
# Use of this source code is governed by an MIT-style
# license that can be found in the LICENSE file or at
# https://opensource.org/licenses/MIT.
# Notebook authors: Kevin P. Murphy (murphyk@gmail.com)
# and Mahmoud Soliman (mjs@aucegypt.edu)
# This notebook reproduces figures for chap... | github_jupyter |
```
import pandas as pd
df = pd.DataFrame({'num_legs': [2, 4, 8, 0],
'num_wings': [2, 0, 0, 0],
'num_specimen_seen': [10, 2, 1, 8]},
index=['falcon', 'dog', 'spider', 'fish'])
from ipyaggrid import Grid
grid_options_1 = {
'enableSorting': 'false',
'enabl... | github_jupyter |
# Lab 11 Download Census Data into Python
```
from urllib import request
import json
from pprint import pprint
census_api_key = 'f84452395038a4790772cc768cb13ecbe0e6a636' #get your key from https://api.census.gov/data/key_signup.html
url_str = 'https://api.census.gov/data/2019/acs/acs5?get=B01001_001E,NAME&for=cou... | github_jupyter |
# <img style="float: left; padding-right: 10px; width: 45px" src="https://raw.githubusercontent.com/Harvard-IACS/2018-CS109A/master/content/styles/iacs.png"> CS109A Introduction to Data Science:
## Homework 2: Linear and k-NN Regression
**Harvard University**<br/>
**Fall 2019**<br/>
**Instructors**: Pavlos Protopap... | github_jupyter |
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