text stringlengths 2.5k 6.39M | kind stringclasses 3
values |
|---|---|
```
#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 |
```
%load_ext autoreload
%autoreload 2
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torchsummaryX import summary
import matplotlib.pyplot as plt
import time
import os
from UnarySim.sw.kernel.nn_utils i... | github_jupyter |
# Auditory P300 with Muse
This experiment is similar to the visual P300 experiment, but used auditory stimuli instead to produce an oddball.
Stimuli were presented for 200 ms with an intertrial interval of 400 ms and random jitter of ±100ms. The task was to count the number of times the oddball stimulus is played. S... | github_jupyter |
# Introduction to the Indian Buffet Process
# DEMO_MATRIX_COMPLETION
```
## import necessary packages
import numpy as np # library to work with numpy arrays and math operations
from random import sample
import sys
import GLFM
import csv
import matplotlib.pyplot as plt
# ---------------------------------------------
# ... | github_jupyter |
The objective of this notebook is to show how to read and plot the trajectory and the temperature measured by a drifting buoy.
```
%matplotlib inline
import netCDF4
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import colors
from mpl_toolkits.basemap import Basemap
```
# ... | github_jupyter |
```
%pylab inline
%config InlineBackend.figure_format = 'retina'
from SuchTree import SuchTree, SuchLinkedTrees, pearson
import pandas as pd
import seaborn
#T1 = SuchTree( 'data/gopher-louse/gopher.tree' )
#T2 = SuchTree( 'data/gopher-louse/lice.tree' )
#links = pd.DataFrame.from_csv( 'data/gopher-louse/gopher-louse-as... | github_jupyter |
```
#!/Tsan/bin/python
# -*- coding: utf-8 -*-
# Libraries To Use
from __future__ import division
from CloudQuant import MiniSimulator
import numpy as np
import pandas as pd
import statsmodels.api as sm
import scipy.stats
import os
from sklearn import linear_model
from datetime import datetime,time,date
import matplo... | github_jupyter |
# Swaption Pricing Part 2: American Monte Carlo for Bermudas
*Copyright (c) 2015 Matthias Groncki*
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
- Redistributions of source code must retain the above copyright notic... | 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 |
# Time Series Forecasting with Linear Learner
_**Using Linear Regression to Forecast Monthly Demand**_
---
---
## Contents
1. [Background](#Background)
1. [Setup](#Setup)
1. [Data](#Data)
1. [Train](#Train)
1. [Host](#Host)
1. [Forecast](#Forecast)
1. [Extensions](#Extensions)
---
## Background
Forecasting is ... | github_jupyter |
```
m = "testing"
m[1:5]
start=2
take=2
m[start:take+start]
m[2:4]
state = "mississippi"
state[5:9]
grades = [ 'A', 'A', 'B+', 'C+', 'A-']
grades[2:3]
grades[2:5]
grades[2:7]
print(grades)
grades[3] = 'A-'
print(grades)
name ="mike"
name[0] = "M"
numbers = [10, 15, 99, 42, 7]
print(numbers[1:4])
for number in numbers:
... | github_jupyter |
<h1 align =center>Statistics With Numpy</h1>
#### Order statistics
|Funcation|Decreption|
|----------|----------|
|**amin(a[, axis, out, keepdims, initial, where])**|Return the minimum of an array or minimum along an axis.|
|**amax(a[, axis, out, keepdims, initial, where])**|Return the maximum of an array or maximum ... | github_jupyter |
# CS446/546 Class Session 2 - Adjacency Forests
## Comparing asymptotic running time for testing two vertices for an edge
In this exercise, we'll compare the asymptotic computational running time for testing if there is an edge between a pair of vertices, averaged over all pairs of vertices in the graph. We'll do it ... | github_jupyter |
```
import dash
import dash_core_components as dcc
import dash_html_components as html
import plotly.graph_objs as go
import plotly.figure_factory as FF
import plotly.offline as offline
from datetime import datetime
import glob
import os.path
import pymysql
import sqlconfig # From sqlconfig.py
import pandas as pd
impor... | github_jupyter |
On va maintenant utiliser une image naturelle comme entrée.
At each time, the pipeline is the following:
* take an image,
* turn into blocks corresponding to the edges' centers,
* into each block determine the most likely orientation
<!-- TEASER_END -->
Let's first create a dummy movie:
```
import os
import mat... | github_jupyter |
# Fit Functions
[Fit functions]: ../../api_static/plasmapy.analysis.fit_functions.rst
[linregress()]: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html
[curve_fit()]: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html
[fsolve()]: https://docs.scipy.org/doc... | github_jupyter |
```
import os
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import torch
from torch.utils.data import Dataset
im_dir = '/Volumes/Tatum_SSD-1/Grad_school/m2py/Morphology_labels/OFET_morph_maps/Default_params/'
im_files = os.listdir(im_dir)
print(len(im_files))
# p... | github_jupyter |
##### Copyright 2020 The OpenFermion 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 ... | github_jupyter |
# **DISTIL Installation**
```
!git clone https://github.com/decile-team/distil.git
!pip install apricot-select
%cd distil/examples/
```
**Imports**
```
import pandas as pd
import numpy as np
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import torch.nn.functional as F
from torch impor... | github_jupyter |
# Settings
```
EXP_NO = 30
SEED = 1
N_SPLITS = 5
TARGET = 'target'
GROUP = 'art_series_id'
REGRESSION = False
assert((TARGET, REGRESSION) in (('target', True), ('target', False), ('sorting_date', True)))
CV_THRESHOLD = None
PAST_EXPERIMENTS = tuple(exp_no for exp_no in range(4, 28 + 1)
# 7 は予測... | github_jupyter |
### Note
Please view the [README](https://github.com/deeplearning4j/deeplearning4j/tree/master/dl4j-examples/tutorials/README.md) to learn about installing, setting up dependencies, and importing notebooks in Zeppelin
### Background
In this tutorial we will use a LSTM neural network to predict instacart users' purch... | github_jupyter |
##### Copyright 2019 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License");
```
#@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.o... | github_jupyter |
# Ward
This notebook illustrates the hierarchical clustering of graphs by the [Ward method](https://scikit-network.readthedocs.io/en/latest/reference/hierarchy.html), after embedding in a space of low dimension.
```
from IPython.display import SVG
import numpy as np
from sknetwork.data import karate_club, painters, m... | github_jupyter |
```
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from sklearn.metrics import classification_report, confusion_matrix, f1_score
from sklearn.metrics import make_scorer, f1_score, accuracy_score, recall_score, precis... | github_jupyter |
# Crawler
[Click here to run this chapter on Colab](https://colab.research.google.com/github/AllenDowney/DSIRP/blob/main/notebooks/crawler.ipynb)
## Crawling the web
At this point we have all the pieces we need to build a web crawler; it's time to bring them together.
First, from `philosophy.ipynb`, we have `WikiFe... | github_jupyter |
```
import pandas as pd
import datetime
from tqdm import tqdm
'''Getting names of all csv files(stocks) in the folder'''
import os
path = '/home/dhruv/Downloads/f&o iday data'
csv_files_initial = [f for f in os.listdir(path) if f.endswith('.CSV')]
stocks_initial = [sub[ : -4] for sub in csv_files_initial]
for i in ran... | github_jupyter |
<img src="../../../images/qiskit-heading.gif" alt="Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook" width="500 px" align="left">
## _*Entanglement*_
The latest version of this notebook is available on https://github.com/qiskit/qiskit-tutorial.
***
### Contr... | github_jupyter |
# Tutorial 4: Angiography Reconstruction
```
#Import required system libraries for file management
import sys,importlib,os
# Provide path to oct-cbort library
module_path=os.path.abspath('/Users/damondepaoli/Documents/GitHub/oct-cbort')
if module_path not in sys.path:
sys.path.append(module_path)
# Import oct-cb... | github_jupyter |
# Lesson 4: Deploy your first domain node with Hagrid
<b><u>Instructors</b></u>: Ishan Mishra, Phil Culliton
This tutorial is a step-by-step walkthrough of how to <b>setup your domain node </b> for the very first time. This will enable you to start your peer-to-peer network of data owner and data scientists and coll... | github_jupyter |
```
%load_ext sql
%sql ibm_db_sa://***@dashdb-txn-sbox-yp-dal09-14.services.dal.bluemix.net:50000/BLUDB
```
#Problem 1: Find the total number of crimes recorded in the crime table.
```
%%sql
select COUNT(*) as Number_of_Crimes from CHICAGO_CRIME_DATA LIMIT 10
```
Problem 2: Retrieve first 10 rows from the CRIME tab... | github_jupyter |
# Debug housing price predictions
This notebook demonstrates the use of the AzureML RAI components to assess a classification model trained on Kaggle's apartments dataset (https://www.kaggle.com/alphaepsilon/housing-prices-dataset). The model predicts if the house sells for more than median price or not. It is a reimp... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive')
import torch.nn as nn
import torch.nn.functional as F
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import torch
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import Dataset, DataLoader
fro... | github_jupyter |
# Metrics - SST2 (id)
## Setup
```
from sklearn.metrics import roc_auc_score, roc_curve
import numpy as np
def compute_auroc(id_pps, ood_pps, normalize=False, return_curve=False):
y = np.concatenate((np.ones_like(ood_pps), np.zeros_like(id_pps)))
scores = np.concatenate((ood_pps, id_pps))
if normalize:
... | github_jupyter |
# Compare Gal4 to biomarkers
```
import os
import sys
import re
from pathlib import Path
from io import StringIO
from IPython.display import display, HTML, Markdown
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
# Project level imports
from larv... | github_jupyter |
# Robert Koch-Institut: COVID-19-Dashboard
Data from https://experience.arcgis.com/experience/478220a4c454480e823b17327b2bf1d4 dashboard using the arcgis REST API.
```
import pandas as pd
import requests
import json
from datetime import datetime
import re
import pycountry
# papermill parameters
output_folder = "../ou... | github_jupyter |
```
# Copyright 2021 NVIDIA Corporation. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | github_jupyter |
# Introduction to Linear Regression
## Learning Objectives
1. Analyze a Pandas Dataframe
2. Create Seaborn plots for Exporatory Data Analysis
2. Train a Linear Regression Model using Scikit-Learn
## Introduction
This lab is in introduction to linear regression using Python and Scikit-Learn. This lab serves as... | github_jupyter |
# MT Quality Score Calculator for Embedding-based Metrics
This is a Jupyter Notebook for calculating modern embedding-based metrics for MT quality evaluation, here *BERTScore* and *COMET*. Traditional MT quality scores such as F-Measure, BLEU or TER rely on exact string matching to calculate the similarity or dissimila... | github_jupyter |
```
import numpy as np
import constants
import quaternion as qt
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
def RK4(f, x0x1, y0, step, step_process = lambda y: y):
# f MUST takes x and y as arguments: f(x, y)
# It solves equation y' = f(x, y), y(x0) = y0 (everything is a vector)
... | github_jupyter |
# Optimization of a State-to-State Transfer in a Lambda System in the RWA
```
# NBVAL_IGNORE_OUTPUT
%load_ext watermark
import os
import numpy as np
import scipy
import matplotlib
import matplotlib.pylab as plt
import krotov
import qutip
from qutip import Qobj
%watermark -v --iversions
```
$\newcommand{tr}[0]{\operat... | github_jupyter |
## Kubeflow Primitives Examples
Kubeflow supports many machine learning and deep learnin frameworks. Katib provides hyper parameter tunning
We will run Katib Experiments using `TFjob` and `PyTorchJob`
### Tensorlow Job (TFJob)
```
!kubectl create -f tfjob-example.yaml
```
If you check manifest, you will see
```... | github_jupyter |
<font size = 1 color="gray">Introducción a la computación numérica y simbólica con Python</font>
<img src="img/logo-vector-u-tad.jpg" align="left" >
# 1. Introducción a NumPy, SciPy y Matplotlib
En esta primera lección vamos a ver algunos ejemplos muy simples de uso de los paquetes de cálculo numérico NumPy y SciPy... | github_jupyter |
```
#!conda install pytorch==1.7.1 torchvision
!pip install ftfy regex tqdm
!pip install git+https://github.com/openai/CLIP.git
```
### clip zero-shot
```
import torch
import clip
from PIL import Image
device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load("ViT-B/32", device=device)
in... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W3D4_ReinforcementLearning/W3D4_Tutorial4.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Tutorial 4: From Reinforcement Learning to Planning
... | github_jupyter |
# Constellation and Chain Analysis: Transmitters and Receivers Example
<img src="TransmitterAndReceiver.jpg" alt="Drawing" style="width: 800px;"/>
**Terminology**
* Node = Object in STK
* Edge = Access between two objects in STK
* Strand = The sequence of nodes and edges to complete access in a chain
**This notebook... | github_jupyter |
```
import os
import json
import dimensions_search_api_client as dscli
import RichContextAPI
import pandas as pd
datasets_path = "/Users/sophierand/RCDatasets/datasets.json"
with open(datasets_path) as json_file:
datasets = json.load(json_file)
def flatten(l):
fl = [item for sublist in l for item in sublist]
... | github_jupyter |
<a href="https://colab.research.google.com/github/mincloud1501/Bokeh/blob/master/googlesheet.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Getting setup
Install boketh `pip install bokeh`.
Setup-test, run the next cell. Hopefully you should see... | github_jupyter |
```
import cudf
import nvstrings
import rmm
import numpy as np
lines = [
'this IS a',
'word COUNT TEST',
'with a few word repeats',
'and some junk filler words'
]
test_strings = nvstrings.to_device(lines)
# lowercase all words
tmp = test_strings.lower()
# remove stop words
for word in ['some', 'junk', 'filler']... | github_jupyter |
# k-NN实现
大家都知道k-NN，即k邻近。今天我们用Python来实现它。
```
iris_data="""
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,Iris-setosa
5.0,3.6,1.4,0.2,Iris-setosa
5.4,3.9,1.7,0.4,Iris-setosa
4.6,3.4,1.4,0.3,Iris-setosa
5.0,3.4,1.5,0.2,Iris-setosa
4.4,2.9,1.4,0.2,Iris-setosa
4.9,3.1... | github_jupyter |
# Balanced Network De-embedding
Demonstration of *balanced*, i.e. 2N-port, network de-embedding.
## Setup
```
import numpy as np
import skrf as rf
from skrf.media import CPW
rf.stylely()
import matplotlib.pyplot as plt
# base parameters
freq = rf.Frequency(1e-3,10,1001,'ghz')
cpw = CPW(freq, w=0.6e-3, s=0.25e-3, ... | github_jupyter |
# Lesson - Machine Learning II: Evaluating Model Performance
We have developed a function that can predict the price for any living space we want to list as long as we know the number of people it can accommodate. The function we have written represents a **machine learning model**, which means that it outputs a predi... | github_jupyter |
# Overview: Extinction Modeling
This notebook provides an example of how to use output stellar posterior samples (from `BruteForce` or other sources) in `brutus` to model the 3-D distribution of dust via extinction.
# Preamble
As always, let's just set up a nice environment to run these tests.
```
from __future__ i... | github_jupyter |
```
import pandas as pd
import numpy as np
import random
import os
import sys
from time import time
import datetime
from scipy.optimize import minimize, fsolve
from collections import Counter
from sklearn.metrics import log_loss
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
from... | github_jupyter |
# Visualizing Grover's Search Algorithm
This is a companion notebook to the [Exploring Grover's Search Algorithm](./ExploringGroversAlgorithmTutorial.ipynb) tutorial. It helps you explore the algorithm by plotting several graphs that are a lot easier to build using Python than in a purely Q# notebook.
> <font color="... | github_jupyter |
# Mount Drive
```
from google.colab import drive
drive.mount('/content/drive')
```
# Install packages
List of packages that work for the code
* https://github.com/saeed-anwar/DRLN/issues/12
```
!pip uninstall --y torchvision
!pip uninstall --y torch
!pip uninstall --y scipy
!pip install -q torch==1.1.0 torchvision... | github_jupyter |
<a href="https://www.bigdatauniversity.com"><img src="https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png" width="400" align="center"></a>
<h1><center>Multiple Linear Regression</center></h1>
<h4>About this Notebook</h4>
In this notebook, we learn how to use scikit-learn to implement Multiple linea... | github_jupyter |
# Converters for Quadratic Programs
Optimization problems in Qiskit's optimization module are represented with the `QuadraticProgram` class, which is a generic and powerful representation for optimization problems. In general, optimization algorithms are defined for a certain formulation of a quadratic program, and we... | github_jupyter |
<h1><center><font size="6">Hyperparameter Tuning Notebook</font></center></h1>
# Contents
- <a href='#1'>Importing Packages</a>
- <a href='#2'>Preparing Data for Modeling</a>
- <a href='#3'>Hyperparameter Tuning</a>
- <a href='#31'>Logistic Regression with GridSearchCV</a>
- <a href='#32'>Random Forest Clas... | github_jupyter |
<i>Copyright (c) Microsoft Corporation. All rights reserved.</i>
<i>Licensed under the MIT License.</i>
# Estimating Baseline Performance
<br>
Estimating baseline performance is as important as choosing right metrics for model evaluation. In this notebook, we briefly discuss about why do we care about baseline perfor... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sb
from sklearn import metrics
from datetime import datetime
from pandas_profiling import ProfileReport
# installer packages
!pip uninstall --yes
!pip install --yes
!conda install -c anaconda pandas-profiling --yes
#lister tous... | github_jupyter |
### In this notebook, we will analyze csv and json files with pandas and numpy, then plot with Bokeh, to visualize how your favorite sports team really is going to be the death of you.
```
#import the needed modules -- we have some extra bokeh packages in case we want to apply more stylings.
import json
import pandas ... | github_jupyter |
# Data science in Python
- Course GitHub repo: https://github.com/pycam/python-data-science
- Python website: https://www.python.org/
## Session 1.2: Using existing python modules to explore data in files
- [Importing module `statistics`](#Importing-module-statistics)
- [Exercise 1.2.1](#Exercise-1.2.1)
- [Python... | github_jupyter |
```
import syft as sy
```
# Domain Applies to Network
```
import syft as sy
sy.networks
un = sy.networks[1]
un.search("https://aclanthology.org/P07-2045.pdf")
un.domains
ca = un.domains[0]
ca.routes[0].connection.base_url
out = sy.login(email="info@openmined.org", password="changethis", port=8081)
sy.register(name="A... | github_jupyter |
Lgal mini mocha data set looks strange because the stellar mass and redshift are highly correlated. All of the $r$ magnitudes are roughly 19.5
```
import os
import numpy as np
from gqp_mc import data as Data
# --- plotting ---
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.ticker as tick... | github_jupyter |
[🥭 Mango Markets](https://mango.markets/) support is available at: [Docs](https://docs.mango.markets/) | [Discord](https://discord.gg/67jySBhxrg) | [Twitter](https://twitter.com/mangomarkets) | [Github](https://github.com/blockworks-foundation) | [Email](mailto:hello@blockworks.foundation)
[![Binder](https://mybinder... | github_jupyter |
```
import pandas as pd
pd.set_option("display.max_columns",250)
import os
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import xlsxwriter
import yaml as yaml
sns.set()
with open(r'E:/Projects/Clients/MetCouncilASIM/tasks/survey_data_processing/metc_inputs.yml') as file:
... | github_jupyter |
## Trimming Characters from Strings
Let us go through how to trim unwanted characters using Spark Functions.
```
%%HTML
<iframe width="560" height="315" src="https://www.youtube.com/embed/qWv8zfSOPZM?rel=0&controls=1&showinfo=0" frameborder="0" allowfullscreen></iframe>
```
* We typically use trimming to remo... | github_jupyter |
* basic roberta ft: 0.6589791487657798 (thr 0.3)
* basic roberta ft (head first): 0.6768011808573329 (thr 0.42)
* fine tune roberta on weird clf, then only head on spans, then whole: 0.6853127403287083 (thr 0.32)
*
```
from transformers import RobertaTokenizer, RobertaForTokenClassification
from transformers import Be... | github_jupyter |
# Detect and Delete outliers with Optimus
An outlier is an observation that lies an abnormal distance from other values in a random sample from a population. In a sense, this definition leaves it up to the analyst (or a consensus process) to decide what will be considered abnormal. Before abnormal observations can be ... | github_jupyter |
<a href="https://colab.research.google.com/github/sibyjackgrove/SolarPV-DER-simulation-utility/blob/master/examples/PV-DER_usage_example.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## Accessing and updating model parameters
### Import classes
I... | github_jupyter |
```
%matplotlib notebook
import matplotlib.pyplot as plt
from matplotlib_scalebar.scalebar import ScaleBar
import numpy as np
from colicoords import Cell, load, CellPlot, iter_subplots, IterCellPlot, CellListPlot
from colicoords.support import pad_cell
from colicoords.config import cfg
import tifffile
import os
data_di... | github_jupyter |
# 注意
这个例子因为年久失修，已经跑不起来了。网上推荐看 rbg 的 faster-rcnn 代码。
[R-CNN](https://github.com/rbgirshick/rcnn) is a state-of-the-art detector that classifies region proposals by a finetuned Caffe model. For the full details of the R-CNN system and model, refer to its project site and the paper:
> *Rich feature hierarchies for accu... | github_jupyter |
```
import pickle
import pandas as pd
from sklearn.preprocessing import StandardScaler, MinMaxScaler
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
from copy import copy
def load_pickle(filename):
with open(filename, 'rb') as f:
return pickle.load(f) # might be pickle.lo... | github_jupyter |
```
import pandas as pd
from pathlib import Path
train_csv_file = Path('./train.csv')
traincsv = pd.read_csv(train_csv_file, usecols=[1, 4])
traincsv.replace('\s', '-', regex=True)
train_csv = traincsv.replace('/', '_', regex=True)
traincsv.to_csv('train_only_labels.csv', index=False)
def all_cols_to_only_labels(inp_cs... | github_jupyter |
# eICU Collaborative Research Database
# Notebook 1: Accessing the data
The aim of this notebook is to get set up with access to a demo version of the [eICU Collaborative Research Database](http://eicu-crd.mit.edu/). The demo is a subset of the full database, limited to 100 patients.
## Prerequisites
- If you do no... | github_jupyter |
# Regression with Amazon SageMaker XGBoost algorithm
_**Distributed training for regression with Amazon SageMaker XGBoost script mode**_
---
## Contents
1. [Introduction](#Introduction)
2. [Setup](#Setup)
1. [Fetching the dataset](#Fetching-the-dataset)
2. [Data Ingestion](#Data-ingestion)
3. [Training the XGBoos... | github_jupyter |
```
from __future__ import print_function, division
import os
import sys
spark_home = os.environ['SPARK_HOME']
sys.path.insert(0, os.path.join(spark_home, 'python'))
sys.path.insert(0, os.path.join(spark_home, 'python/lib/py4j-0.10.4-src.zip'))
from pyspark.sql import SparkSession
spark = SparkSession.builder.master... | github_jupyter |
Linear Regression
===============
<div class="overview-this-is-a-title overview">
<p class="overview-title">Overview</p>
<p>Questions</p>
<ul>
<li>How can I complete linear regression with statistics in Python?
</ul>
<p>Objectives:</p>
<ul>
<li>Use a pandas dataframe for data analysis
... | github_jupyter |
# Initialization
Test notebook for the C-MAPPS benchmark. Test different MLP architectures.
First we import the necessary packages and create the global variables.
```
import math
import numpy as np
import csv
import copy
from scipy import stats
from sklearn.preprocessing import StandardScaler, MinMaxScaler
import... | github_jupyter |
```
%matplotlib inline
import gym
import matplotlib
import numpy as np
import sys
from collections import defaultdict
if "../" not in sys.path:
sys.path.append("../")
from lib.envs.blackjack import BlackjackEnv
from lib import plotting
matplotlib.style.use('ggplot')
env = BlackjackEnv()
def create_random_policy(n... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Reducer/stats_of_an_image_region.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<td><a target="_bla... | github_jupyter |
<img src="../img/logo_amds.png" alt="Logo" style="width: 128px;"/>
# AmsterdamUMCdb - Freely Accessible ICU Database
version 1.0.1 January 2020
Copyright © 2003-2020 Amsterdam UMC - Amsterdam Medical Data Science
# <a id='processitems'></a>processitems table
The *processitems* table contains all catheters, dr... | github_jupyter |
# Visualize your location history with Python and Leaflet
See [this blog post](http://geoffboeing.com/2016/06/mapping-everywhere-ever-been/) for my full write-up of this project.
This project collects, clusters, geocodes, merges, and now visualizes location history data from Foursquare, Google, and a spreadsheet of p... | github_jupyter |
# ADVANCED SQL 1: Window Functions & Percentiles
Window Functions is one of the most powerful concepts in SQL data analysis. The Window Function allows us to compare one row to another without doing any joins. This can allow us do simple things like create a running-total as well as tricky things like determine if on... | github_jupyter |
### 上期所写入数据库
```
from bs4 import BeautifulSoup
import urllib.parse
import pandas as pd
import re
import pandas as pd
def askURL(url):
head = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36 Edg/89.0.774.63"}
req = urllib.request.... | github_jupyter |
<a href="https://colab.research.google.com/github/Lawan-L/LinearProgramming/blob/main/simplex.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
from scipy.optimize import linprog
```
# Simplex à 2 variables
```
obj = [-1, -2]
# ─┬ ─┬
# ... | github_jupyter |
<a href="https://colab.research.google.com/github/HeyLaxmi/DS-Unit-1-Sprint-1-Dealing-With-Data/blob/master/LS_DS_114_Making_Data_backed_Assertions.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Lambda School Data Science - Making Data-backed Ass... | github_jupyter |
# 两个融合
```
import csv
import numpy as np
with open('/home/freedom/caffe/tianchifusai/InceptionV3/20180522/result2/result.csv','r') as a:
reader = csv.reader(a)
rows_a = [row for row in reader]
with open('/home/freedom/caffe/tianchifusai/InceptionV3/20180522/result2/submission.csv','r') as b:
reader = csv.... | github_jupyter |
```
#make sure you are in gis-env to run this code
#try to read in gis files
import shapefile
import numpy as np
import matplotlib.pyplot as plt
import geopandas as gpd
import matplotlib
from shapely.geometry import Polygon
import pyproj
from shapely.geometry import Point
import matplotlib.path as mpltPath
from shapely... | github_jupyter |
# 1.0 IMPORT
```
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
import re
from scipy import stats as ss
from geopy.geocoders import Nominatim
from tqdm import tqdm
warnings.filterwarnings('ignore')
```
## 1.1 Helper Fuctions
```
def grafdisplot(dt):
... | github_jupyter |
```
import re
import json
import editdistance
import numpy as np
import pandas as pd
from tqdm import tqdm
from collections import Counter
from PIL import Image
import os
from matplotlib import font_manager as fm, rcParams
from nltk.tokenize import sent_tokenize
from unidecode import unidecode
from rich import print
... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
Pre_data = pd.read_csv("C:\\Users\\2019A00303\\Desktop\\Code\\Airbnb Project\\Data\\PreProcessingBelgium.csv")
Pre_data
Pre_data['Price'].plot(kind='hist', bins=100)
Pre_data['group'] = pd.cut(x=Pre_data['Price'],
bins=[0, 50, 100, 150, 200, 10... | github_jupyter |
### Scroll Down Below to start from Exercise 8.04
```
# Removes Warnings
import warnings
warnings.filterwarnings('ignore')
#import the necessary packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
```
## Reading the data using pandas
```
data= pd.read_csv('Churn_Mode... | github_jupyter |
# Bayesian Optimization for Rosenbrock Function
```
%load_ext autoreload
%autoreload 2
import numpy as np
from scipy.optimize import rosen
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C
import matplotlib.pyplot as plt
%matplotlib inl... | github_jupyter |
```
# lists are mutable
# lists are like arrays in java except it can store any datatype
l1 = []
print(type(l1))
l2 = [1,2,3]
print(type(l2))
l3 = [1, 2.34, "aaa", True]
print(type(l3))
l4 = list()
print(l4)
print(type(l4))
l5 = list(l2)
print(type(l5))
print(l5)
l6 = list("abc123")
print(l6)
# another way of cre... | github_jupyter |
# Simple `openrouteservice-py` examples
This notebook is just a small reference on how to use `openrouteservice` package to request:
- [directions](https://openrouteservice.org/dev)
- [isochrones](https://openrouteservice.org/dev/#/api-docs/v2/isochrones/{profile}/post)
- [matrix](https://openrouteservice.org/dev/#/ap... | github_jupyter |
<a href="https://qworld.net" target="_blank" align="left"><img src="../qworld/images/header.jpg" align="left"></a>
$ \newcommand{\bra}[1]{\langle #1|} $
$ \newcommand{\ket}[1]{|#1\rangle} $
$ \newcommand{\braket}[2]{\langle #1|#2\rangle} $
$ \newcommand{\dot}[2]{ #1 \cdot #2} $
$ \newcommand{\biginner}[2]{\left\langle... | github_jupyter |
### Sea Level Rise in New York City
Data Bootcamp Final Project (Spring 2016)
by Daniel Jung (dmj307@stern.nyu.edu)
** About This Project **
The levels of ocean surfaces (henceforth referred to as the 'sea level') have been observed to be rising around the world. While sea levels are known to fluctuate due to natur... | github_jupyter |
# Configuration
```
import os
from tqdm.notebook import tqdm
from tqdm import tqdm
import librosa
import pandas as pd
import pandas as np
from sklearn.metrics import classification_report
```
# Clean Data - Compute dataframes for datasets and split in Train, Val, Test
```
main_path = '/Users/helemanc/Documents/Mast... | github_jupyter |
```
# import packages
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_sco... | github_jupyter |
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