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# Evaluation of doLLy in Google code jam
```
# Import Core libraries
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.font_manager as font_manager
import seaborn as sns
from sklearn.metrics import auc
# Graphics
%matplotlib inline
%config InlineBackend.... | github_jupyter |
```
'''
This notebook categorizes the splicing status of each intron in long read data and calculates CoSE values.
Figures 2 and S3
'''
import os
import numpy as np
import pandas as pd
from pandas.api.types import CategoricalDtype
import mygene
import itertools
import scipy
import pysam
import pybedtools
from pybedtoo... | github_jupyter |
# Description
This notebook computes predicted expression correlations between all genes in the MultiPLIER models.
It also has a parameter set for papermill to run on a single chromosome to run in parallel (see under `Settings` below).
# Modules
```
%load_ext autoreload
%autoreload 2
import numpy as np
from scipy.s... | github_jupyter |
# Logistic Regression
## Importing the libraries
```
#for debug purpose
%qtconsole --style solarized-dark
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
```
## Importing the dataset
```
dataset = pd.read_csv('Social_Network_Ads.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].va... | github_jupyter |
# Practice Exercise - 04 - Solution
### Question 1:
We have two sets given below. Print the set of elements that are present in either set1 or set2 but not both.
set1 = {1, 2, 3, 4, 5}
<br>set2 = {4, 5, 6, 7}
#### Expected Output:
{1, 2, 3, 6, 7}
```
set1 = {1, 2, 3, 4, 5}
set2 = {4, 5, 6, 7}
set1 ^ set2
```
##... | github_jupyter |
# Tensorflow and Keras Basics
Problem: we want to predict the price of the gem stone based on the features 1 and 2.
```
import numpy as np
import pandas as pd
import seaborn as sns
folder_path = 'drive/MyDrive/TensorFlow-Data'
file_path = folder_path + '/fake_reg.csv'
```
## 1) read in your data
```
sns.pairplot... | github_jupyter |
--- Debug Pod ---
```
from pathlib import Path
model_dir = f'/data/models'
model_main = f'sleep_main.py'
Path(model_dir).mkdir(exist_ok=True)
print("create model directory done.")
%%writefile {model_dir}/{model_main}
import time
time.sleep(6000)
print("task done.")
import requests
from requests.packages.urllib3.exce... | github_jupyter |
```
import pandas as pd
import numpy as np
import datetime as dt
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
%matplotlib inline
import matplotlib.style as style
style.use('seaborn-whitegrid')
import os
import re
# import googlemaps
# import time
import pickle
from collectio... | github_jupyter |
# Forward simulation the 1D Magnetotelluric (MT) problem
In the [previous notebook](./MT1D_Simulation.ipynb), we walked through how to discretize and solve the 1D Magnetotelluric (MT) problem using a finite difference approach. In this notebook, we will use the numerical simulation to simulate MT data and explore conc... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
import time
import pickle
from pytrends.request import TrendReq
pytrend = TrendReq()
class Trend:
# Required:
# import pandas as pd
# import pickle
# import matplotlib.pyplot as plt
# from pytrends.request import TrendReq
... | github_jupyter |
# Convolutional Networks
So far we have worked with deep fully-connected networks, using them to explore different optimization strategies and network architectures. Fully-connected networks are a good testbed for experimentation because they are very computationally efficient, but in practice all state-of-the-art resu... | github_jupyter |
<a href="https://colab.research.google.com/github/53X/53X.github.io/blob/master/Welcome_To_Colaboratory.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
<img height="45px" src="https://colab.research.google.com/img/colab_favicon.ico" align="left" hsp... | github_jupyter |
Hypothesis Testing
=========
Copyright 2015 Allen Downey
License: [Creative Commons Attribution 4.0 International](http://creativecommons.org/licenses/by/4.0/)
```
from __future__ import print_function, division
import numpy
import scipy.stats
import matplotlib.pyplot as pyplot
from IPython.html.widgets import i... | github_jupyter |
<a name="loesung05"></a>Lösung Übung 05
===
```
# 1.a
# Funktionskopf mit Definition von Name und
# Anzahl der Argumente
def rechenfunktion(x, y):
# Funktionskörper
# Berechnungen
summe = x + y
differenz = x - y
produkt = x * y
quotient = x / y
# Ausgabe
print('summe: {}'.fo... | github_jupyter |
<a href="https://colab.research.google.com/github/unicamp-dl/IA025_2022S1/blob/main/ex07/Leonardo_Pacheco.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
nome = 'Leonardo Augusto da Silva Pacheco'
print(f'Meu nome é {nome}.')
```
# Exercício: ... | github_jupyter |
# Automated Car Detection - All Damage 50 Epochs
- this method uses the Resnet model architecture
- we train the model and produce weights from our training set
- our 'levers' are the number of epochs, and the number of steps per epoch
- this method also uses transfer learning. i.e. before we do anything we use weigh... | github_jupyter |
# Predicting human wine test preference, Part 3
## XGBoost
The method that we used in the last notebook was boostrap agreggating (bagging). In this method we have 2 randomizing processes which ensure the uniqueness of the tree:
1. Pick a random number of features from the feature vector
2. Pick a random number of sa... | github_jupyter |
## Single-cancer holdout dimension reduction analysis
Following [this GitHub issue](https://github.com/greenelab/pancancer-evaluation/issues/39) and related discussions in lab meeting, we wanted to look at how separable the data is in the 95% holdout cases where performance isn't decreasing. Our hypothesis is that in ... | github_jupyter |
```
import numpy as np
import pandas as pd
import scipy
import matplotlib.pyplot as plt
import datetime
import math
from sklearn.datasets import dump_svmlight_file
# initialize spark
from pyspark.sql import SparkSession
from pyspark.sql import SQLContext
from pyspark.ml.regression import RandomForestRegressor
from pys... | github_jupyter |
```
import os
import matplotlib.pyplot as plt
from demo.healthcare.histogram_inspection import HistogramInspection
from demo.healthcare.missing_embeddings_inspection import MissingEmbeddingInspection
from demo.healthcare.lineage_demo_inspection import LineageDemoInspection
from mlinspect.inspections.materialize_first_... | github_jupyter |
# Data Augmentation for Deep Learning <a href="https://mybinder.org/v2/gh/InsightSoftwareConsortium/SimpleITK-Notebooks/master?filepath=Python%2F70_Data_Augmentation.ipynb"><img style="float: right;" src="https://mybinder.org/badge_logo.svg"></a>
This notebook illustrates the use of SimpleITK to perform data augmentat... | github_jupyter |
```
import requests
import pandas as pd
BASE_URL = "https://api.nb.no/ngram/db2"
BASE_URL1 = "https://api.nb.no/ngram/db1"
pd.options.display.max_rows = 100
def ngram_book(word = ['.'], title = None, period = None, publisher = None, lang=None, city = None, ddk = None, topic = None):
"""Get a time series for a wo... | github_jupyter |
# COMP9417 19T2 Homework 1: Applying Machine Learning
_Last revision: Wed Jun 26 17:49:45 AEST 2019_
The aim of this homework is to enable you to **apply** different machine learning algorithms implemented in the Python [scikit-learn](http://scikit-learn.org/stable/index.html) machine learning library on a variety o... | github_jupyter |
```
import open3d as o3d
import numpy as np
import copy
import time
import os
import sys
# monkey patches visualization and provides helpers to load geometries
sys.path.append('..')
import open3d_tutorial as o3dtut
# change to True if you want to interact with the visualization windows
o3dtut.interactive = not "CI" in... | github_jupyter |
```
import os
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
csv_directory = os.getcwd()[:-40] + 'dataset\\'
dataset = 'features.csv'
csv_path = os.path.join(csv_directory, dataset)
mydata = pd.read_csv(csv_path, delimiter=';', usecols=['is_featured', 'version', 'tags_num... | github_jupyter |
# Introduction
### This project report will include
1. Explanation of the purpose, and background on my project
2. A in-depth explanation of my data merging
## Purpose
The purpose of this project report (in notebook form) is to show some exploratory data analysis and data cleaning for this particular project. This pr... | github_jupyter |
TSG083 - Run kubectl cluster-info dump
======================================
NOTE: This kubectl command can produce a lot of output, and may take
some time (and produce a large notebook!). For Kubernetes clusters that
have been up for a long time, consider running this command outside of a
notebook.
Steps
-----
###... | github_jupyter |
#Splunk/Notebook/Graphistry Mashup
This notebook shows a different kind of way to explore alerts:
* **Exploratory notebook rather than an interactive dashboard.** This simplifies doing & sharing more complicated analysis, and with coming versions, can be quickly converted into a reusable dashboard.
* **node-link diagr... | github_jupyter |
# Logistic Regression with a Neural Network mindset
Welcome to your first (required) programming assignment! You will build a logistic regression classifier to recognize cats. This assignment will step you through how to do this with a Neural Network mindset, and so will also hone your intuitions about deep learning.... | github_jupyter |
# Creating a logistic regression to predict absenteeism
## Import the relevant libraries
```
import pandas as pd
import numpy as np
```
## Load the data
```
data_preprocessed = pd.read_csv('Absenteeism_preprocessed.csv')
data_preprocessed.head()
```
## Create the targets
```
data_preprocessed['Absenteeism Time in... | github_jupyter |
# Prepare zero-shot split
Based on the paper: Bansal, Ankan, et al. "Zero-shot object detection." Proceedings of the European Conference on Computer Vision (ECCV). 2018.
```
import json
import numpy as np
import torch
from maskrcnn_benchmark.config import cfg
from maskrcnn_benchmark.modeling.language_backbone.transfo... | github_jupyter |
```
# reload packages
%load_ext autoreload
%autoreload 2
```
### Choose GPU (this may not be needed on your computer)
```
%env CUDA_DEVICE_ORDER=PCI_BUS_ID
%env CUDA_VISIBLE_DEVICES=1
```
### load packages
```
from tfumap.umap import tfUMAP
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
... | github_jupyter |
# 28 - 03 - 2021
## First draft of our abstract
In our first meeting with the group, we discussed about the topic and created a first draft of our abstract. For this, everyone wrote a draft and we combined our results in the end.
# 30 - 03 - 2021
## Preliminary Analysis: exploring the classes of errors
I started to... | github_jupyter |
##### Copyright 2020 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
```
import pandas as pd
from clickhouse_driver import Client
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import TimeSeriesSplit, GridSearchCV
import numpy as np
import eli5
matplotlib.rcParams["figure.figsize"] = (1... | github_jupyter |
# Computer Vision Learner
[`vision.learner`](/vision.learner.html#vision.learner) is the module that defines the [`create_cnn`](/vision.learner.html#create_cnn) method, to easily get a model suitable for transfer learning.
```
from fastai.gen_doc.nbdoc import *
from fastai.vision import *
```
## Transfer learning
T... | github_jupyter |
# Setup
First, let us do some setup.
* Create a SageMaker execution role. This role should have access to S3 and permission to create SageMaker HPO jobs. Save the ARN of this role. We need to paste this ARN in the line of code that defines `role` in our Lambda function later.
* Create a SQS queue, note the URL of ... | github_jupyter |
```
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets,transforms
from torch.autograd import Variable
import matplotlib.pyplot as plt
%matplotlib inline
is_cuda=False
if torch.cuda.is_available():
is_cuda = True
transformation = transforms... | github_jupyter |
# spaCyTextBlob <a href='https://spacytextblob.netlify.app/'><img src='website/static/img/logo-thumb-circle-250x250.png' align="right" height="139" /></a>
A TextBlob sentiment analysis pipeline compponent for spaCy.
Version 3.0 is a major version update providing support for spaCy 3.0's new interface for adding pipe... | github_jupyter |
```
%matplotlib inline
import sys
sys.path.insert(0, "../..")
import random
import deeptrack as dt
import deeptrack.extras
import numpy as np
import skimage.color
import matplotlib.pyplot as plt
import tensorflow as tf
import scipy.io
import numpy as np
import matplotlib.pyplot as plt
crop_size = 40
padding = 32
wave... | github_jupyter |
## Primer Design
One of the first things anyone learns in a molecular biology lab is how to design primers. The exact strategies vary a lot and are sometimes polymerase-specific. `coral` uses the Klavins' lab approach of targeting a specific melting temperature (Tm) and nothing else, with the exact Tm targeted being b... | github_jupyter |
# Python | Pandas DataFrame
### What is Pandas?
<b>pandas</b> is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series.
### What is a Pandas DataFrame?
<b>Pandas Dat... | github_jupyter |
# Morphological tessellation
One of the main features of `momepy` is the ability to generate and analyse morphological tessellation (MT). One can imagine MT like Voronoi tessellation generated around building polygons instead of points. The similarity is not accidental - the core of MT is a Voronoi diagram generated b... | github_jupyter |
## Generate blend set
```
import pandas as pd
import numpy as np
import random
from rdkit.Chem import AllChem
from rdkit import RDLogger
from sklearn.manifold import TSNE
from rdkit.Chem import MolFromSmiles, DataStructs, rdMolDescriptors
import matplotlib.pyplot as plt
import seaborn as sns
RDLogger.DisableLog('r... | github_jupyter |
# Publications markdown generator for academicpages
Takes a set of bibtex of publications and converts them for use with [academicpages.github.io](academicpages.github.io). This is an interactive Jupyter notebook ([see more info here](http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_is_jupyter.html... | github_jupyter |
```
import os
import cv2
import math
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
import tensorflow as tf
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, fbeta_score
from keras import optimizers
from keras... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
import os, sys
import pandas as pd
sys.path.append('..')
from pyMultiOmics.constants import *
from pyMultiOmics.mapping import Mapper
from pyMultiOmics.common import set_log_level_info
```
# Demonstration of pyMultiOmics mapping
## Load the processed Zebrafi... | github_jupyter |
# Adams Moulton
The Adams Moulton method is an implicit multistep method. This notebook illustrates the 2 step Adams Moulton method for a linear initial value problem.
## Intial Value Problem
The general form of the population growth differential equation
$$ y^{'}=t-y, \ \ (0 \leq t \leq 2) $$
with the initial condit... | github_jupyter |
# The IBM Q Account
In Qiskit we have an interface for backends and jobs that is useful for running circuits and extending to third-party backends. In this tutorial, we will review the core components of Qiskit’s base backend framework, using the IBM Q account as an ... | github_jupyter |
```
#import packages
import gym
import random
import numpy as np
import time
#invoke the environment
env_name = "FrozenLake-v0"
# instantiate environment
env = gym.make(env_name)
# output variables for state and action
print("Observation space:", env.observation_space)
print("Action space:", env.action_space)
#create... | github_jupyter |
ERROR: type should be string, got "https://github.com/sn3fru/mensa_quadrant\n\n```\nimport pandas as pd\nimport numpy as np\nfrom sklearn.decomposition import PCA\nfrom math import sqrt\nfrom sklearn.preprocessing import normalize\nfrom sklearn import metrics\nfrom sklearn.cluster import KMeans\nimport matplotlib.pyplot as plt\n\n%matplotlib inline\ndf = pd.read_csv('dados_turma_03.csv', delimiter=';')\ndf.set_index('Nome',inplace=True)\ndf.drop(['Extremismo'], axis=1, inplace=True)\n\ndfn = normalize(df, norm='l2', axis=1, copy=True, return_norm=False)\npca = PCA(n_components=2, svd_solver='full')\npca.fit(dfn)\ndft = pca.transform(dfn)\nfinal = pd.merge(pd.DataFrame(dft),df.reset_index(),how='inner',left_index=True,right_index=True)\nfinal.rename(columns={0:'cp1',1:'cp2'},inplace=True)\nfinal.corr().round(3)\nfinal.reset_index(inplace=True)\ndist = lambda p1, p2: sqrt(((p1-p2)**2).sum())\ndm = np.asarray([[dist(p1, p2) for p2 in final[['cp1','cp2']].values] for p1 in final[['cp1','cp2']].values])\ndistance_matrix = pd.merge(pd.DataFrame(dm),final[['Nome']], how='inner',left_index=True,right_index=True)\ndistance_matrix.set_index('Nome', inplace=True)\ndistance_matrix.columns = list(final['Nome'])\ndistance_matrix.to_excel('distance_matrix.xlsx')\nkmeans = KMeans(init='k-means++', n_clusters=2, n_init=10)\nkmeans.fit(final[['cp1','cp2']])\n\nh = .02 # point in the mesh [x_min, x_max]x[y_min, y_max].\n\n# Plot the decision boundary. For that, we will assign a color to each\nx_min, x_max = final['cp1'].min() - 1, final['cp1'].max() + 1\ny_min, y_max = final['cp2'].min() - 1, final['cp2'].max() + 1\nxx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n\n# Obtain labels for each point in mesh. Use last trained model.\nZ = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])\n\n# Put the result into a color plot\nZ = Z.reshape(xx.shape)\nplt.figure(1).set_size_inches(18, 9)\nplt.clf()\nplt.imshow(Z, interpolation='nearest',\n extent=(xx.min(), xx.max(), yy.min(), yy.max()),\n cmap=plt.cm.Paired,\n aspect='auto', origin='lower')\n\nplt.plot(final['cp1'], final['cp2'], 'k.', markersize=2)\n# Plot the centroids as a white X\ncentroids = kmeans.cluster_centers_\nplt.scatter(centroids[:, 0], centroids[:, 1],\n marker='x', s=169, linewidths=3,\n color='w', zorder=10)\n\nplt.title('K-means clustering on the mensan dataset (PCA-reduced data)\\n'\n 'Centroids are marked with white cross')\n\nplt.xlim(-0.4,0.6)\nplt.ylim(-0.2,0.2)\n\nfig, ax = plt.subplots()\n\nfig.set_size_inches(14, 10)\n\ncircle = plt.Circle((0, 0), .11, color='b', fill=False)\nax.add_artist(circle)\n\nfinal.plot('cp1', 'cp2', kind='scatter', ax=ax)\n\nfor k, v in final.set_index('Nome')[['cp1','cp2']].iterrows():\n ax.annotate(k, v)\n```\n\n" | github_jupyter |
## Sparse Matrix Example
This notebook implements a SENSE Example with sparse interpolation matrices. Sparse matrix-based interpolation is usually slower than table-based interpolation, but can be a bit more accurate, or might be faster for certain problem structures.
### References
Fessler, J. A., & Sutton, B. P. (... | github_jupyter |
<a href="https://colab.research.google.com/github/kalz2q/mycolabnotebooks/blob/master/chartmathc01matrix.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# メモ
手元にある
基礎からのチャート式数学C
の
第1章行列
を読む。
いくつかの数や文字を長方形状に並べ、両側を括弧で囲んだものを行列といい、そのおのお... | github_jupyter |
## Midterm Exam
### Brandan Owens and Loan Pham
### Q.1
```
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import string
# (a) Set random seed to be 50.
np.random.seed(50)
np.random.randn(50)
# (b) Create a dataframe with four columns of data:-Each column has 26 random integers between -5 and... | github_jupyter |
<!--NOTEBOOK_HEADER-->
*This notebook contains material from [PyRosetta](https://RosettaCommons.github.io/PyRosetta.notebooks);
content is available [on Github](https://github.com/RosettaCommons/PyRosetta.notebooks.git).*
<!--NAVIGATION-->
< [Visualization with the `PyMOLMover`](http://nbviewer.jupyter.org/github/Rose... | github_jupyter |
# Bite Size Bayes
Copyright 2020 Allen B. Downey
License: [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/)
## Review
[In the previous notebook](https://colab.research.google.com/github/AllenDowney/BiteSizeBayes/blob/master/03_cookie.ipynb... | github_jupyter |
# SageMaker Edge Manager Example
1. [Introduction](#Introduction)
2. [Demo Setup](#Demo-Setup)
1. [Launch EC2 Instance](#Launch-EC2-Instance)
3. [Compile Model using SageMaker Neo](#Compile-Model-using-SageMaker-Neo)
1. [Load pretrained model](#Load-pretrained-model)
6. [Deploy Model using Sagemaker Edge Manag... | github_jupyter |
# MSCKF
MSCKF全称Multi-State Constraint Kalman Filter（多状态约束下的Kalman滤波器），是一种基于滤波的VIO算法，2007年由Mourikis在《A Multi-State Constraint Kalman Filter for Vision-aided Inertial Navigation》中首次提出。MSCKF在EKF框架下融合IMU和视觉信息，相较于单纯的VO算法，MSCKF能够适应更剧烈的运动、一定时间的纹理缺失等，具有更高的鲁棒性；相较于基于优化的VIO算法（VINS，OKVIS），MSCKF精度相当，速度更快，适合在计算资源有限的嵌入式平台运行。
MSCKF的... | github_jupyter |
# Fonctions
En programmation, une fonction est une suite d'instructions nommées.
Ceci permet d'éviter de devoir réécrire des longues suites d'instructions et les appeler juste en invoquant le nom de la fonction.
Nous pouvons ainsi définir la fonction `saluer()`
```
def saluer():
print('Bonjour')
print('Comme... | github_jupyter |
```
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
import pandas as pd
import pymc3 as pm
from scipy.stats import norm, multivariate_normal
import random
%matplotlib inline
df = pd.read_csv(pm.get_data('mastectomy.csv'))
df.event = df.event.astype(np.int64)
df.metastized = (df.metastized =... | github_jupyter |
# 重回帰分析
If you come here without expecting Japanese, please click [Google translated version](https://translate.google.com/translate?hl=&sl=ja&tl=en&u=https%3A%2F%2Fpy4etrics.github.io%2F9_Multiple_Regression.html) in English or the language of your choice.
---
```
import numpy as np
from scipy.stats import norm, un... | github_jupyter |
**<p style="font-size: 35px; text-align: center">Probability Distributions</p>**
***<center>Miguel Ángel Vélez Guerra</center>***
<hr/>
<hr/>
<hr/>
**<p id="tocheading">Tabla de con... | github_jupyter |
```
%reload_ext nb_black
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
from pyclustering.cluster.kmedoids import kmedoids
from sklearn.cluster import AgglomerativeClustering, DBSCAN
from umap import UMAP
from sklearn.preprocessing import StandardScaler
import prince
... | github_jupyter |
# cuML Cheat Sheets sample code
(c) 2020 NVIDIA, Blazing SQL
Distributed under Apache License 2.0
## Imports
```
import cudf
import cuml
import numpy as np
import cupy as cp
```
## Create regression dataset
```
X, y, c = cuml.make_regression(
n_samples=10000
, n_targets=1
, n_features=4
, n_inform... | github_jupyter |
# Conversational AI
Think about how often you communicate with other people through instant messaging, social media, email, or other online technologies. For many of us, it's our go-to form of contact. When you have a question at work, you might reach out to a colleague using a chat message, which you can use on mobil... | github_jupyter |
# DART Overview
This notebook provides an interactive interface to the DART dataset.
```
%matplotlib inline
import json
from pathlib import Path
from collections import Counter
from functools import partial
import logging
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import numpy as np
import... | github_jupyter |
# Custom Entity detection with Textract and Comprehend
## Contents
1. [Background](#Background)
1. [Setup](#Setup)
1. [Data Prep](#Data-Prep)
1. [Textract OCR++](#Textract-OCR++)
1. [Amazon GroundTruth Labeling](#Amazon-GroundTruth-Labeling)
1. [Comprehend Custom Entity Training](#Comprehend-Custom-Entity-Training)
1.... | github_jupyter |
# QAOA
It is almost the same as the VQE algorithm, but we use QAOA-specific ansatz for combinatorial optimization problem.
## What we learn
1. How QAOA works
2. Implement QAOA with a simple example
## Install Blueqat
Install Blueqat from pip.
```
!pip install blueqat
```
----
## Quantum Adiabatic Computation
QAOA ... | github_jupyter |
# Probability
Author: Vo, Huynh Quang Nguyen
```
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sb
```
# Acknowledgements:
The contents of this note are based on the lecture notes and the materials from the sources listed below:
1. _Essential Math for Data Science_ in 6 Weeks webinar given by D... | 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 |
```
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Hyper-parameters
sequence_length = 28
input_size = 28
hidden_size = 128
num_layers = 2
num_classes = 10
batch_size = 100
... | github_jupyter |
### H1-B Visa Wage Prediction.
-----
-----
```
%pwd
#import os
#os.chdir('E:\ML Project\Project1')
import pandas as pd
import numpy as np
import warnings
import collections
import seaborn as sns
from datetime import datetime
from matplotlib import pyplot as plt
from sklearn.model_selection import train_test_split
fro... | github_jupyter |
# RadarCOVID-Report
## Data Extraction
```
import datetime
import json
import logging
import os
import shutil
import tempfile
import textwrap
import uuid
import matplotlib.ticker
import numpy as np
import pandas as pd
import seaborn as sns
%matplotlib inline
current_working_directory = os.environ.get("PWD")
if curr... | github_jupyter |
```
import pydicom
from glob import glob
from random import randint
from copy import deepcopy
from datetime import datetime
import numpy as np
import pandas as pd
pydicom.config.enforce_valid_values = True
pd.set_option('display.max_rows', 500)
multi_arc_plan = pydicom.read_file('MVISO_VMATNEWSPLIT.dcm', force=True)
b... | github_jupyter |
```
import numpy as np
import pandas as pd
import networkx as nx
%matplotlib inline
import gspread
from oauth2client.service_account import ServiceAccountCredentials
import pandas as pd
scope = ['https://spreadsheets.google.com/feeds',
'https://www.googleapis.com/auth/drive']
credentials = ServiceAccountCre... | github_jupyter |
# Exp 136 analysis
See `./informercial/Makefile` for experimental
details.
```
import os
import numpy as np
from pprint import pprint
from IPython.display import Image
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import seaborn as sns
sns.set_... | github_jupyter |
# Flowers Dataset
http://www.robots.ox.ac.uk/~vgg/data/flowers/17/
```
import numpy as np
import matplotlib.pyplot as plt
import keras
# from keras.datasets import cifar10
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten, Reshape
from keras.layers import Conv2D, MaxPool... | github_jupyter |
```
#hide
#skip
! [ -e /content ] && pip install -Uqq fastai # upgrade fastai on colab
#default_exp data.core
#export
from fastai.torch_basics import *
from fastai.data.load import *
#hide
from nbdev.showdoc import *
```
# Data core
> Core functionality for gathering data
The classes here provide functionality for ... | github_jupyter |
```
import os
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sns
import tensorflow as tf
if os.getcwd().split(os.sep)[-1] == 'notebook':
os.chdir('..')
from cma import CMA
from notebook.u... | github_jupyter |
```
from dask.distributed import Client, LocalCluster
if __name__ == "__main__":
cluster=LocalCluster(host="tcp://127.0.0.1:2456",dashboard_address="127.0.0.1:2467",n_workers=4)
client = Client(cluster)
import numpy as np
import pandas as pd
import xarray as xr
import math
import dask
import skimage.feature
im... | github_jupyter |
# Example 6: Non-linear PCA
A non-linear principal component analysis (NLPCA) is quite similar to the standard PCA model presented in Example 1. The main difference comes
from the conditional distribution of $\boldsymbol{x}$. In the NLPCA model, the mean of the normal distribution of $\boldsymbol{x}$
linearly depend... | github_jupyter |
<a href="https://colab.research.google.com/github/noahbjohnson/advent-of-code-2020/blob/main/notebooks/Day_Three.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
inputText = [
"......#...........#...#........",
".#.....#...##.......#.....##...",
... | github_jupyter |
# Launch into interactive computing interfaces
Because Jupyter Books are built with Jupyter notebooks, you can allow users to launch live Jupyter sessions in the cloud directly from your book.
This lets readers quickly interact with your content in a traditional coding interface.
They do so by clicking a **Launch Butt... | github_jupyter |
```
import numpy as np
import mccd.mccd_utils as mccd_utils
import mccd.utils as utils
import mccd.auxiliary_fun as mccd_aux
import mccd
from astropy.io import fits
import random
import matplotlib.pyplot as plt
import matplotlib as mpl
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib.colors imp... | github_jupyter |
# Split Normal Mixture
Figure 2 in the [paper](https://arxiv.org/abs/2007.09670).
```
import os
import sys
os.chdir('..')
sys.path.append('..')
%config InlineBackend.figure_formats = ['svg']
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
from pprint import pprint
import ne... | github_jupyter |
```
import collections
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy import stats
import statsmodels.api as sm
np.set_printoptions(suppress=True)
cadralazine_data = pd.DataFrame(collections.OrderedDict([
('time', [2, 4, 6, 8, 10, 24, 28, 32]),
('drug concen... | github_jupyter |
# ABT
```
%matplotlib inline
import random
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import ttest_ind, ttest_rel, mannwhitneyu
# Matplotlib
plt.style.use('bmh')
plt.rcParams['figure.figsize'] = (16, 8)
%%capture
# R
from rpy2.robjects.packages impo... | github_jupyter |
# Hyperparameter tuning
In the previous section, we did not discuss the parameters of random forest
and gradient-boosting. However, there are a couple of things to keep in mind
when setting these.
This notebook gives crucial information regarding how to set the
hyperparameters of both random forest and gradient boost... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Goal" data-toc-modified-id="Goal-1"><span class="toc-item-num">1 </span>Goal</a></span></li><li><span><a href="#Var" data-toc-modified-id="Var-2"><span class="toc-item-num">2 </span>Va... | github_jupyter |
# Lab 03 : LeNet5 architecture - exercise
```
# For Google Colaboratory
import sys, os
if 'google.colab' in sys.modules:
# mount google drive
from google.colab import drive
drive.mount('/content/gdrive')
# find automatically the path of the folder containing "file_name" :
file_name = 'lenet5_exerci... | github_jupyter |
___
<a href='https://mp.weixin.qq.com/mp/appmsgalbum?__biz=Mzg2OTU4NzI3NQ==&action=getalbum&album_id=1764511202329624577&scene=126#wechat_redirect'> <img src=../../../../pic/project_logo.jpg></a>
___
# Missing Data
处理 pandas 中缺失数据的便捷方法
⚠️先说说 None/NaN 的区别
```
import numpy as np
import pandas as pd
# Pandas automati... | github_jupyter |
```
import os, re, copy, pickle
from collections import defaultdict
from tqdm import tqdm
from functools import partial
tqdm = partial(tqdm, position=0, leave=True)
import numpy as np
import pandas as pd
from scipy import linalg, special, stats
from sklearn.utils.extmath import stable_cumsum
from sklearn import (imp... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
%matplotlib notebook
import numpy as np
import math
import scipy as sp
import copy
import os
import matplotlib.pyplot as plt
from libwallerlab.projects.motiondeblur import blurkernel
import bluranalysis as analysis
# plt.style.use('deblur')
```
## Blur Len vs Beta
```
# blur_l... | github_jupyter |
# Part I. ETL Pipeline for Pre-Processing the Files
## PLEASE RUN THE FOLLOWING CODE FOR PRE-PROCESSING THE FILES
#### Import Python packages
```
# Import Python packages
import pandas as pd
import cassandra
import re
import os
import glob
import numpy as np
import json
import csv
```
#### Creating list of filepat... | 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 |
# Identify Fraud from Enron Email
[Cédric Campguilhem](https://github.com/ccampguilhem/Udacity-DataAnalyst), November 2017
<a id="Top">
## Table of contents
- [Introduction](#Introduction)
- [Project organisation](#Organisation)
- [Dataset](#Dataset)
- [Downloading dataset](#Download)
- [Data exploration](#... | github_jupyter |
Heroes Of Pymoli Challange
Part 1: I need to import dependencies
```
#Import dependencies
import pandas as pd
import numpy as np
```
Part 2: I need to Reference the file and import the data to pandas.
```
# Reference the file where the CSV is located
HeroesOfPymoli_csv_path = "Resources/purchase_data.csv"
# Import... | github_jupyter |
# 6. For Loop
## 6.1 Introduction
Well, in the first lesson about loops, I said I would put off teaching you the for loop, until we had reached lists. Well, here it is!
## 6.2 The `for` Loop
Basically, the `for` loop does something for every value in a list. The way it is set out is a little confusing, but otherwise is... | github_jupyter |
<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/">Creative C... | github_jupyter |
```
from bs4 import BeautifulSoup
from requests import get
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import math
import os
# Function for remove comma within numbers
def removeCommas(string):
string = string.replace(',','')
return string
```
# Scrap data from worldmeter... | github_jupyter |
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