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## 2-1. NISQアルゴリズムとlong-termアルゴリズム
現在発明・発見されている量子アルゴリズムは、実現可能性の観点から2つのグループに大別できる。
一つは**NISQアルゴリズム**、もう一つは**long-termアルゴリズム**である。（これらの単語は一般的ではないので、他の文献を見る際には注意すること。また、**この２つの区別は絶対的なものではなく、解くべき問題の大きさや技術の進歩などによって移り変るものであることに留意されたい。**）それらの代表例を表に示す。
(VQE = Variational Quantum Eigen... | github_jupyter |
```
# --- added to file ----
# Takes in a String, "bucket_name", a string, "remote_folder",
# and a list of strings or a single string, "keywords". Gets all
# s3 keys for bucket_name/remote_folder. Uses a list convention
# to go through keywords (i.e): ['a', 'b', 'c OR d OR e'] will
# find all files containing 'a' and... | github_jupyter |
# Databolt Flow
For data scientists and data engineers, d6tflow is a python library which makes building complex data science workflows easy, fast and intuitive.
https://github.com/d6t/d6tflow
## Benefits of using d6tflow
[4 Reasons Why Your Machine Learning Code is Probably Bad](https://medium.com/@citynorman/4-rea... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
cd /Users/martin/Git/estates/src/data/gold
from rentals import load_rentals
import numpy as np
import pandas as pd
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, Binarizer, FunctionTransforme... | github_jupyter |
# 3.1 Constants and variables in programs
In this notebook, you will learn how to use constants and variables in a robot control program.
Once again, you will be creating programs to run in the RoboLab simulator, so load the simulator by running the following code cell:
```
from nbev3devsim.load_nbev3devwidget impor... | github_jupyter |
```
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
print('total train dataset', mnist.train.images.shape[0])
print('total test dataset', mn... | github_jupyter |
## Generating partial coherence phase screens for modeling rotating diffusers
```
%pylab
%matplotlib inline
import SimMLA.fftpack as simfft
import SimMLA.grids as grids
import SimMLA.fields as fields
from numpy.fft import fft, ifft, fftshift, ifftshift
from scipy.integrate import simps
from scipy.interpolate import... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# Since there is no column name so lets add that
# column 0 to 59 so they will be named as Feature 0,..,Feature 59
# and lets name target column as Class
# First lets make a lost of column name
new_column_names = []
for i in range(60):
new_... | github_jupyter |
```
import argparse
import csv
import matplotlib.pyplot as plt
import glob
import os
import json
import seaborn as sns
import pandas as pd
import mpld3
from IPython import display
from process_log import Tags, Log, Epochs
leonhard_directory = "../logs/naive_scaling_Nov_15_073912"
tags = Tags("tags.hpp")
all_names = os.... | github_jupyter |
Note: It is recommended to run this notebook from an [Azure DSVM](https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview) instance.
```
# Useful for being able to dump images into the Notebook
import IPython.display as D
```
# Big Picture
In the previous notebooks, we tried tog... | github_jupyter |
# Intro to profiling
Python's dirty little secret is that it can be made to run pretty fast.
The bare-metal HPC people will be angrily tweeting at me now, or rather, they would be if they could get their wireless drivers working.
Still, there are some things you *really* don't want to do in Python. Nested loops a... | github_jupyter |
```
import numpy as np
import pandas as pd
import scipy.stats as ss
closest_collection = "typeIII_submission_collection_closest.csv"
hungarian_collection = "typeIII_submission_collection_hungarian.csv"
```
## How many predicted pKas are matched differently between closest and hungarian algorithms?
```
df_closest = pd... | github_jupyter |
# SLU06 - File & String handling
Now we're going to test how well you understood the learning notebook.
Also, this notebook is going to often require some googling skills. It's very important to learn to google anything you don't remember or don't know how to do.
A small hint: list comprehensions might make it easie... | github_jupyter |
# Scheduling a Doubles Pickleball Tournament
My friend Steve asked for help in creating a schedule for a round-robin doubles pickleball tournament with 8 or 9 players on 2 courts. ([Pickleball](https://en.wikipedia.org/wiki/Pickleball) is a paddle/ball/net game played on a court that is smaller than tennis but larger ... | github_jupyter |
# Introduction #
In the previous lesson we looked at our first model-based method for feature engineering: clustering. In this lesson we look at our next: principal component analysis (PCA). Just like clustering is a partitioning of the dataset based on proximity, you could think of PCA as a partitioning of the variat... | github_jupyter |
# Modelagem magnética 3D de uma esfera
## Importando as bibliotecas
```
import numpy as np
import matplotlib.pyplot as plt
import sphere_mag
```
## Gerando os parâmetros do sistema de coordenadas
```
Nx = 100
Ny = 50
area = [-1000.,1000.,-1000.,1000.]
shape = (Nx,Ny)
x = np.linspace(area[0],area[1],num=Nx)
y = np.l... | github_jupyter |
```
import numpy as np
import pylab as plt
import swyft
swyft.set_verbosity(0)
import torch
from scipy import stats
%load_ext autoreload
%autoreload 2
DEVICE = 'cuda'
```
## Torus model
```
def model(params, center = np.array([0.6, 0.8])):
a, b, c = params['a'], params['b'], params['c']
r = ((a-center[0])**2+... | github_jupyter |
<font style="font-size:96px; font-weight:bolder; color:#0040a0"><img src="http://montage.ipac.caltech.edu/docs/M51_logo.png" alt="M" style="float: left; padding: 25px 30px 25px 0px;" /></font>
<i><b>Montage</b> Montage is an astronomical image toolkit with components for reprojection, background matching, coaddition a... | github_jupyter |
<a href="https://colab.research.google.com/github/JSJeong-me/KOSA-Big-Data_Vision/blob/main/Roboflow_CLIP_Zero_Shot_Cake.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# How to use CLIP Zero-Shot on your own classificaiton dataset
This notebook pr... | github_jupyter |
- import lib
```
# What version of Python do you have?
import sys
from collections import Counter
import tensorflow.keras
import pandas as pd
import sklearn as sk
from imblearn.over_sampling import SMOTE
from imblearn.over_sampling import SMOTENC
import tensorflow as tf
import seaborn as sns
import math
import matpl... | github_jupyter |
# Measuring Quantum Volume
## Introduction
**Quantum Volume (QV)** is a single-number metric that can be measured using a concrete
protocol on near-term quantum computers of modest size. The QV method quantifies
the largest random circuit of equal width and depth that the computer successfully implements.
Quantum com... | github_jupyter |
# Retail Demo Store Experimentation Workshop - A/B Testing Exercise
In this exercise we will define, launch, and evaluate the results of an A/B experiment using the experimentation framework implemented in the Retail Demo Store project. If you have not already stepped through the **[3.1-Overview](./3.1-Overview.ipynb)... | github_jupyter |
<i>Copyright (c) Microsoft Corporation. All rights reserved.</i>
<i>Licensed under the MIT License.</i>
# Vowpal Wabbit Deep Dive
<center>
<img src="https://github.com/VowpalWabbit/vowpal_wabbit/blob/master/logo_assets/vowpal-wabbits-github-logo.png?raw=true" height="30%" width="30%" alt="Vowpal Wabbit">
</center>... | github_jupyter |
```
"""
Implementation of the CPC baseline based on the code available on
https://openreview.net/forum?id=8qDwejCuCN
"""
import os
import random
import pickle
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
os.chdir("../") #Load from parent directory
from dat... | github_jupyter |
# SPARTA QuickStart
-----------------------------------
## 1. Extracting Radial Velocities
### 1.1 Reading and handling spectra
#### `Observations` (class)
`Observations` class enables one to load data from a given folder
and place it into a TimeSeries object.
```
from sparta import Observations
```
The `ob.Obser... | github_jupyter |
# Machine Learning Exercise 7 - K-Means Clustering & PCA
This notebook covers a Python-based solution for the seventh programming exercise of the machine learning class on Coursera. Please refer to the [exercise text](https://github.com/jdwittenauer/ipython-notebooks/blob/master/exercises/ML/ex7.pdf) for detailed des... | github_jupyter |
# Lab 2: Welcome to Python + Data Structures
## Overview
Welcome to your first lab! Labs in CS41 are designed to be your opportunity to experiment with Python and gain hands-on experience with the language.
The primary goal of the first half is to ensure that your Python installation process went smoothly, and that ... | github_jupyter |
```
import os
import sys
import pandas as pd
from sklearn.model_selection import train_test_split
import numpy as np
from tensorflow.keras import backend as K
from tensorflow.keras.layers import Conv1D, BatchNormalization, GlobalAveragePooling1D, Permute, Dropout, Flatten
from tensorflow.keras.layers import Input, Dens... | github_jupyter |
# PDP Team 11 Design/Course Prep Planning Meeting
## August 9, 2018
Meeting goal: discuss PDP team 11's current status and plan August activities.
[Design notebook](https://docs.google.com/document/d/1iexo2xeYYIVDWD_pfgW4sOA5sRZSStsGNth-_bU_lrU/edit#)
[Teaching plan](https://docs.google.com/document/d/1xb4omX9AnZTl... | github_jupyter |
```
# testing installation
import pandas as pd
import matplotlib.pyplot as plt
conf = pd.read_csv('sensingbee.conf', index_col='param')
import sys, os
sys.path.append(conf.loc['GEOHUNTER_PATH','val'])
sys.path.append(conf.loc['SOURCE_PATH','val'])
import geohunter
import sensingbee
conf
```
# Data preparation
``... | github_jupyter |
# Dingocar Demo
This Notebook will take allow you to train a Dingocar (_Donkeycar, down-under_). The model will be trained using data uploaded to your Google Drive. The trained model will be saved in your nominated Google Drive Folder .
## Requirements
A zip file of training data. I recomend a zip file because you'... | github_jupyter |
```
#hide
#skip
! [ -e /content ] && pip install -Uqq fastai # upgrade fastai on colab
#default_exp vision.data
#export
from fastai.torch_basics import *
from fastai.data.all import *
from fastai.vision.core import *
#hide
from nbdev.showdoc import *
# from fastai.vision.augment import *
```
# Vision data
> Helper f... | github_jupyter |
```
import cv2
import numpy as np
import matplotlib.pyplot as plt
import scipy.ndimage
def overlay(overlay_img, bg_img, scale, starting_y, starting_x, rotate=False, choice=''):
# Rotating image 45 degrees if it has to be rotated
if rotate:
img = overlay_img
overlay_img = scipy.ndimage.rotate(ove... | github_jupyter |
### Plotting the ADCP spectra
```
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
```
Little tweaks in the matplotlib configuration to make nicer plots
```
plt.rcParams.update({'font.size': 25, 'legend.handlelength' : 2.0
, 'legend.markerscale': 1., 'legend.fontsize' : 20, 'axes.titlesize... | github_jupyter |
<img src="interactive_image.png"/>
# Interactive image
The following interactive widget is intended to allow the developer to explore
images drawn with different parameter settings.
```
# preliminaries
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets
from jp_doodle im... | github_jupyter |
Before running this notebook, it's helpful to
`conda install -c conda-forge nb_conda_kernels`
`conda install -c conda-forge ipywidgets`
and set the kernel to the conda environment in which you installed glmtools (typically, `glmval`)
```
import os
%matplotlib inline
import numpy as np
import matplotlib.pyplot as p... | github_jupyter |
```
from PIL import Image
import glob
from keras.applications.inception_v3 import InceptionV3
from keras.applications.inception_v3 import preprocess_input, decode_predictions
from keras.preprocessing import image
import numpy as np
import json
```
## Define data path
#### You can add multiple file extensions by extend... | github_jupyter |
# Exorad 2.0
This Notebook will show you how to use exorad library to build your own pipeline.
Before we start, let's silent the exorad logger.
```
import warnings
warnings.filterwarnings("ignore")
from exorad.log import disableLogging
disableLogging()
```
## Preparing the instrument
### Load the instrument des... | github_jupyter |
```
from netCDF4 import Dataset
path = '/home/joao/Downloads/'
ds = Dataset(path+'OR_ABI-L2-CMIPF-M6C13_G16_s20192781230281_e20192781240001_c20192781240078.nc')
import GOES
import numpy as np
SatHeight = ds.variables['goes_imager_projection'].perspective_point_height
SatLon = ds.variables['goes_imager_projection'].lo... | github_jupyter |
# Eurostat bioenergy balance 2018
Extract bioenergy related data from an archive containing XLSB files, one for each EU country which contain multiple sheets for each year (1990-2018).
Walk through excel files (country spreadsheets) and parse selected variables and fuels for each year (sheet in country's spreadsheet)... | github_jupyter |
[Table of Contents](http://nbviewer.ipython.org/github/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/table_of_contents.ipynb)
# Discrete Bayes Filter
```
#format the book
%matplotlib inline
from __future__ import division, print_function
from book_format import load_style
load_style()
```
The Kalman filte... | github_jupyter |
```
import matplotlib.pyplot as plt
import iris
import iris.plot as iplt
import numpy
import iris.coord_categorisation
import re
%matplotlib inline
infile = '/g/data/ua6/DRSv2/CMIP5/CSIRO-Mk3-6-0/rcp85/mon/ocean/r1i1p1/tauuo/latest/tauuo_Omon_CSIRO-Mk3-6-0_rcp85_r1i1p1_200601-210012.nc'
cube = iris.load_cube(infile, 's... | github_jupyter |
```
import os
%env DEVICE = CPU
%env MODEL=/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/intel/person-detection-retail-0013/FP32/person-detection-retail-0013.xml
"""Restricted Zone Notifier."""
"""
Copyright (c) 2018 Intel Corporation.
Permission is hereby granted, free of charge, to any pers... | github_jupyter |
# Wasserstein GAN
<img src="https://miro.medium.com/max/3200/1*M_YipQF_oC6owsU1VVrfhg.jpeg" width="800" height="400">
##### Importing libraries
```
import numpy as np
import matplotlib.pyplot as plt
from glob import glob
from PIL import Image
from time import time
import pandas as pd
import argparse
import math
imp... | github_jupyter |
👇 (Press on the three dots to expand the code)
```
# Code preamble: we'll need some packages to display the information in the notebook.
# Feel free to ignore this cell unless you're running the code.
import folium # Map visualizations
import requests # Basic http requests
import json # For handling... | github_jupyter |
## 1. Which college majors will pay the bills?
<p><img src="https://s3.amazonaws.com/assets.datacamp.com/production/project_584/img/salary.png" width="400" align="center"></p>
<p>Wondering if that Philosophy major will really help you pay the bills? Think you're set with an Engineering degree? Choosing a college major ... | github_jupyter |
### LSTM Model v2
```
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from utils import split_sequence, get_apple_close_price, plot_series
from utils import plot_residual_forecast_error, print_performance_metrics
from utils import get_range, difference, inverse_difference
from utils import train... | github_jupyter |
# Summary:
This notebook contains the soft smoothing figures for Swarthmore (Figure 2(a)).
## Load libraries
```
# import packages
from __future__ import division
import networkx as nx
import os
import numpy as np
from sklearn import metrics
from sklearn.preprocessing import label_binarize
from sklearn.metrics imp... | github_jupyter |
## Required extra package:
For hypergraphs:
* pip install hypernetx
```
import pandas as pd
import numpy as np
import igraph as ig
import partition_igraph
import hypernetx as hnx
import pickle
import matplotlib.pyplot as plt
%matplotlib inline
from collections import Counter
from functools import reduce
import iterto... | github_jupyter |
```
import numpy as np
import pandas as pd
from IPython.display import clear_output
from matplotlib import pyplot as plt
from matplotlib import style
style.use('fivethirtyeight')
dftrain = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv')
dfeval = pd.read_csv('https://storage.googleapis.com/tf... | github_jupyter |
```
#default_exp basics
#export
from fastcore.imports import *
import builtins
from fastcore.test import *
from nbdev.showdoc import *
from fastcore.nb_imports import *
```
# Basic functionality
> Basic functionality used in the fastai library
## Basics
```
# export
defaults = SimpleNamespace()
# export
def ifnone(... | github_jupyter |
## Introduction to Exploratory Data Analysis and Visualization
In this lab, we will cover some basic EDAV tools and provide an example using _presidential speeches_.
## Table of Contents
[ -Step 0: Import modules](#step0)
[-Step 1: Read in the speeches](#step1)
[-Step 2: Text processing](#step2)
-Ste... | github_jupyter |
```
import matplotlib.pyplot as plt
import numpy as np
colors=['darkorange', 'crimson', 'darkseagreen', 'navy', 'wheat', 'gray', 'palevioletred', 'gold', 'lightcoral', 'forestgreen', 'cornflowerblue']
participants = ['p{:02}'.format(index) for index in range(15)]
# 0 1 2* 3* 4** 5 6 ... | github_jupyter |
# Step input, output, and substeps
* **Difficulty level**: easy
* **Time need to lean**: 10 minutes or less
* **Key points**:
* Input files are specified with the `input` statement, which defines variable `_input`
* Output files are specified with the `output` statement, which defines variable `_output`
* Input ... | github_jupyter |
**This notebook is an exercise in the [Intermediate Machine Learning](https://www.kaggle.com/learn/intermediate-machine-learning) course. You can reference the tutorial at [this link](https://www.kaggle.com/alexisbcook/cross-validation).**
---
In this exercise, you will leverage what you've learned to tune a machine... | github_jupyter |
# Intro to Python!
Stuart Geiger and Yu Feng for The Hacker Within
# Contents
## 1. Installing Python
## 2. The Language
- Expressions
- List, Tuple and Dictionary
- Strings
- Functions
## 3. Example: Word Frequency Analysis with Python
- Reading text files
- Geting and using python packages : wordcloud
-... | github_jupyter |
# Verifying Kinetics Models: Part 2 - Writing Tests
Writing verification tests for kinetics models requires having insights as to the dynamic behavior expected of model variables. This discussion focuses on the concentration of molecules (floating species in ``tellurium``).
```
import numpy as np
import tellurium as ... | github_jupyter |
# SIF4Sci 使用示例
## 概述
SIFSci 是一个提供试题切分和标注的模块。它可定制化的将文本切分为令牌（token）序列，为后续试题的向量化做准备。
本文将以下面这道题目（来源自 LUNA 题库）为例，展示 SIFSci 的使用方法。
- 符合 [SIF 格式](https://edunlp.readthedocs.io/en/docs_dev/tutorial/zh/sif.html) 的题目录入格式为：
```
item = {
"stem": r"如图来自古希腊数学家希波克拉底所研究的几何图形．此图由三个半圆构... | github_jupyter |
# Altair Data Server
This notebook shows an example of using the [Altair data server](https://github.com/altair-viz/altair_data_server), a lightweight plugin for [Altair](http://altair-viz.github.io) that lets you efficiently and transparently work with larger datasets.
Altair data server can be installed with pip:
... | github_jupyter |
# Vanilla Recurrent Neural Network
<br>
Character level implementation of vanilla recurrent neural network
## Import dependencies
```
import numpy as np
import matplotlib.pyplot as plt
```
## Parameters Initialization
```
def initialize_parameters(hidden_size, vocab_size):
'''
Returns:
parameters -- a t... | github_jupyter |
```
import numpy as np
from keras import layers
from keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D
from keras.models import Model, load_model
from keras.preprocessing import image
from keras.utils import layer_ut... | github_jupyter |
```
import cv2
import numpy as np
import matplotlib.pyplot as plt
# Resize window to display all image
def ResizeWithAspectRatio(image, width=None, height=None, inter=cv2.INTER_AREA):
dim = None
(h, w) = image.shape[:2]
if width is None and height is None:
return image
if width is None:
... | github_jupyter |
# Sonar - Decentralized Model Training Simulation (local)
DISCLAIMER: This is a proof-of-concept implementation. It does not represent a remotely product ready implementation or follow proper conventions for security, convenience, or scalability. It is part of a broader proof-of-concept demonstrating the vision of the... | github_jupyter |
```
!pip install neural-tangents
```
## Imports
```
import time
import itertools
import numpy.random as npr
import jax.numpy as np
from jax.config import config
from jax import jit, grad, random
from jax.nn import log_softmax
from jax.experimental import optimizers
import jax.experimental.stax as jax_stax
import ... | github_jupyter |
##### Copyright 2019 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
```
import pandas as pd
import numpy as np
import scanpy as sc
import os
from sklearn.cluster import KMeans
from sklearn.cluster import AgglomerativeClustering
from sklearn.metrics.cluster import adjusted_rand_score
from sklearn.metrics.cluster import adjusted_mutual_info_score
from sklearn.metrics.cluster import homog... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
eth = pd.read_csv("ETH.csv").set_index("Date")
rai = pd.read_csv("RAI.csv").set_index('Date')
rai.index = pd.to_datetime(rai.index)
rai.index = pd.to_datetime(rai.index.date)
eth.index = pd.to_datetime(eth.index)
prices = pd.concat([eth, rai], a... | github_jupyter |
# Dropout
Dropout [1] is a technique for regularizing neural networks by randomly setting some features to zero during the forward pass. In this exercise you will implement a dropout layer and modify your fully-connected network to optionally use dropout.
[1] [Geoffrey E. Hinton et al, "Improving neural networks by pr... | github_jupyter |
## Visualizing-Food-Insecurity-with-Pixie-Dust-and-Watson-Analytics
_IBM Journey showing how to visualize US Food Insecurity with Pixie Dust and Watson Analytics._
Often in data science we do a great deal of work to glean insights that have an impact on society or a subset of it and yet, often, we end up not communica... | github_jupyter |
# Iterators
# 迭代器
> Often an important piece of data analysis is repeating a similar calculation, over and over, in an automated fashion.
For example, you may have a table of a names that you'd like to split into first and last, or perhaps of dates that you'd like to convert to some standard format.
One of Python's a... | github_jupyter |
# 0. Import
```
import torch
```
# 1. Data
เราจะสร้างข้อมูลขึ้นมาเป็น Tensor ขนาด 10 Row, 3 Column [เรื่อง Tensor จะอธิบายต่อไป](https://www.bualabs.com/archives/1629/what-is-tensor-element-wise-broadcasting-operations-high-order-tensor-numpy-array-matrix-vector-tensor-ep-1/)
```
z = torch.tensor([
... | github_jupyter |
# PerfForesightConsumerType: Perfect foresight consumption-saving
```
# Initial imports and notebook setup, click arrow to show
from copy import copy
import matplotlib.pyplot as plt
import numpy as np
from HARK.ConsumptionSaving.ConsIndShockModel import PerfForesightConsumerType
from HARK.utilities import plot_func... | 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... | github_jupyter |
```
!nvidia-smi
!pip --quiet install transformers
!pip --quiet install tokenizers
from google.colab import drive
drive.mount('/content/drive')
!cp -r '/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Scripts/.' .
COLAB_BASE_PATH = '/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/'
M... | github_jupyter |
<h1>2b. Machine Learning using tf.estimator </h1>
In this notebook, we will create a machine learning model using tf.estimator and evaluate its performance. The dataset is rather small (7700 samples), so we can do it all in-memory. We will also simply pass the raw data in as-is.
```
import tensorflow as tf
import p... | github_jupyter |
#### Copyright 2017 Google LLC.
```
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | github_jupyter |
# Likelihood based models
This notebook will outline the likelihood based approach to training on Bandit feedback.
Although before proceeding we will study the output of the simmulator in a little more detail.
```
from numpy.random.mtrand import RandomState
from recogym import Configuration
from recogym.agents impor... | github_jupyter |
# Requirements
```
import numpy as np
import pandas as pd
```
# Dataframe
We create a very simple dataframe with three columns `alpha`, `beta` and `gamma` as well as and index that is non-trivial.
```
indices = 'ABCDEFGHIJK'
df = pd.DataFrame({
'alpha': [i for i in range(1, 1 + len(indices))],
'beta': [i**... | github_jupyter |
```
% matplotlib inline
import os
import numpy as np
import matplotlib.pyplot as plt
from keras.utils.np_utils import to_categorical
from snntoolbox.datasets.aedat.DVSIterator import DVSIterator, load_event_list, get_frames_from_sequence, extract_batch, next_eventframe_batch
data_path = '/home/rbodo/.snntoolbox/Datas... | github_jupyter |
```
if len(groups_desc) > 0:
markdown_str = ["## Differential feature functioning"]
markdown_str.append("This section shows differential feature functioning (DFF) plots "
"for all features and subgroups. The features are shown after applying "
"transformations (if... | github_jupyter |
# Bayesian Probabilistic Matrix Factorization
**Published**: November 6, 2020
**Author**: Xinyu Chen [[**GitHub homepage**](https://github.com/xinychen)]
**Download**: This Jupyter notebook is at our GitHub repository. If you want to evaluate the code, please download the notebook from the [**transdim**](https://git... | github_jupyter |
```
import numpy as np
from ctapipe.io import EventSource
from ctapipe.io import EventSeeker
import matplotlib.pyplot as plt
import numpy as np
from ctapipe.instrument import CameraGeometry
from ctapipe.visualization import CameraDisplay
%matplotlib inline
plt.rcParams['figure.figsize'] = (16, 9)
plt.rcParams['font.si... | github_jupyter |
```
import pandas as pd
import numpy as np
import os
from sklearn.model_selection import train_test_split
from matplotlib import pyplot as plt
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Activation, Dropout, Flatten, Dense
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing.im... | 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">
# _*Quantum Tic-Tac-Toe*_
The latest version of this notebook is available on https://github.com/qiskit/qiskit-tutorial.
***
### Cont... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv("data/Mall_Customers.csv")
df.head()
print("Size of the data : ", df.shape)
from sklearn.cluster import KMeans
```
### Segmentation usin... | github_jupyter |
## Data Source and Description:
Creator/Donor:
Jeffrey C. Schlimmer (Jeffrey.Schlimmer '@' a.gp.cs.cmu.edu)
Sources:
1. 1985 Model Import Car and Truck Specifications, 1985 Ward's Automotive Yearbook.
2. Personal Auto Manuals, Insurance Services Office, 160 Water Street, New York, NY 10038
3. Insurance Collisi... | github_jupyter |
```
!unzip spam.zip -d /
#importing libraries
import numpy as np
import random
import pandas as pd
import sys
import os
import time
import codecs
import collections
import numpy
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import LSTM
from keras.c... | github_jupyter |
# 数据集加载总览
`Ascend` `GPU` `CPU` `数据准备`
[](https://authoring-modelarts-cnnorth4.huaweicloud.com/console/lab?share-url-b64=aHR0cHM6Ly9taW5kc3BvcmUtd2Vic2l0ZS5vYnMuY24tbm9ydGgtNC5teWh1YXdlaWNsb3VkLmNvbS9ub3RlYm9vay9tb2RlbGFydHMvcHJvZ3... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
datafile = 'data/ex1data1.txt'
cols = np.loadtxt(datafile,delimiter=',',usecols=(0,1),unpack=True) #Read in comma separated data
#Form the usual "X" matrix and "y" vector
X = np.transpose(np.array(cols[:-1]))
y = np.transpose(np.array(cols[-1:]))
m = y.size # numbe... | github_jupyter |
# Welcome to Jupyter!
With Jupyter notebooks you can write and execute code, annotate it with Markdownd and use powerful visualization tools all in one document.
## Running code
Code cells can be executed in sequence by pressing Shift-ENTER. Try it now.
```
import math
from matplotlib import pyplot as plt
a=1
b=2
a... | github_jupyter |
# Using Deep Learning for Medical Imaging
In the United States, it takes an average of [1 to 5 days](https://www.ncbi.nlm.nih.gov/pubmed/29132998) to receive a diagnosis after a chest x-ray. This long wait has been shown to increase anxiety in 45% of patients. In addition, impoverished countries usually lack personnel ... | github_jupyter |
# Surname Generation
## Imports
```
import os
from argparse import Namespace
from collections import Counter
import json
import re
import string
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from torch.nn import functional as F
import torch.optim as optim
from torch.utils.data import Data... | github_jupyter |
## Collaborative filtering
```
from fastai.gen_doc.nbdoc import *
```
This package contains all the necessary functions to quickly train a model for a collaborative filtering task. Let's start by importing all we'll need.
```
from fastai.collab import *
```
## Overview
Collaborative filtering is when you're tasked... | github_jupyter |
# Aerospike Python Client Tutorial
### Refer to https://www.aerospike.com/docs/client/python/index.html for information on installing the Aerospike Python client.
#### Tested with Python 3.7
```
# IP Address or DNS name for one host in your Aerospike cluster
AS_HOST ="127.0.0.1"
# Please reach out to us if you do no... | github_jupyter |
```
import numpy as np
import tensorflow as tf
from sklearn.utils import shuffle
import re
import time
import collections
import os
def build_dataset(words, n_words, atleast=1):
count = [['GO', 0], ['PAD', 1], ['EOS', 2], ['UNK', 3]]
counter = collections.Counter(words).most_common(n_words)
counter = [i for... | github_jupyter |
#### New to Plotly?
Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
<br>You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-fo... | github_jupyter |
```
%matplotlib inline
import math
import scipy
from scipy.stats import *
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.lines as mlines
colorDic = {"blue" : "#6599FF", "yellow" : "#FFAD33", "purple": "#683b96", "green" : "#198D6D", "red" : "#FF523F"}
colors = list(colorDic.... | github_jupyter |
[](https://colab.sandbox.google.com/github/kornia/tutorials/blob/master/source/data_augmenation_segmentation.ipynb)
# **Data Augmentation Semantic Segmentation**
In this tutorial we will show how we can quickly perform **data augmentation for s... | github_jupyter |
# Pandas
Pandas ist ein Python-Modul, welches auf Tabellen sowie Tabellenkalkulationsprogrammen (wie es auch MS Excel tut) beruht. Eine besondere Fähigkeit von Pandas ist, dass es direkt CSV-, DSV- und Excel-Dateien einlesen und schreiben kann.
Mehr zu Pandas auf der offiziellen Website: http://pandas.pydata.org/
##... | github_jupyter |
```
!pip install pyforest
from pyforest import *
import warnings
!pip install quandl
import quandl
from pandas import DataFrame
!pip install tscv
from tscv import GapKFold
!pip install backtrader
import backtrader as bt
from backtrader.feeds import PandasData
from sklearn.linear_model import LogisticRegression
from skl... | github_jupyter |
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