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# Monte Carlo Simulations with Python (Part 1)
[Patrick Hanbury](https://towardsdatascience.com/monte-carlo-simulations-with-python-part-1-f5627b7d60b0)
- Notebook author: Israel Oliveira [\[e-mail\]](mailto:'Israel%20Oliveira%20'<prof.israel@gmail.com>)
```
%load_ext watermark
import numpy as np
import math
import r... | github_jupyter |
```
import sys; sys.path.append('../rrr')
from multilayer_perceptron import *
from figure_grid import *
from local_linear_explanation import *
from toy_colors import generate_dataset, imgshape, ignore_rule1, ignore_rule2, rule1_score, rule2_score
import lime
import lime.lime_tabular
```
# Toy Color Dataset
This is a ... | github_jupyter |
```
import os
import re
import torch
import pickle
import pandas as pd
import numpy as np
from tqdm.auto import tqdm
tqdm.pandas()
```
# 1. Pre-processing
### Create a combined dataframe
> This creates a dataframe containing the image IDs & labels for both original images provided by the Bristol Myers Squibb pharmac... | github_jupyter |
```
import functools
import pathlib
import numpy as np
import matplotlib.pyplot as plt
import shapely.geometry
import skimage.draw
import tensorflow as tf
import pydicom
import pymedphys
import pymedphys._dicom.structure as dcm_struct
# Put all of the DICOM data here, file structure doesn't matter:
data_path_root ... | github_jupyter |
```
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from keras.datasets import mnist
(x_train, y_train), _ = mnist.load_data()
x_train = x_train / 255.0
x_train = np.expand_dims(x_train, axis=3)
print(x_train.shape)
print(y_train.shape)
num_classes = 10
plt.imshow(np.squeeze(x_train[10]))
... | github_jupyter |
## Accessing High Resolution Electricity Access (HREA) data with the Planetary Computer STAC API
The HREA project aims to provide open access to new indicators of electricity access and reliability across the world. Leveraging VIIRS satellite imagery with computational methods, these high-resolution data provide new t... | github_jupyter |
```
import os
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd
from tqdm.auto import tqdm
import torch
from torch import nn
import gin
import pickle
import io
from sparse_causal_model_learner_rl.trainable.gumbel_switch import With... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn as sk
from sklearn import linear_model
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler
# Набор данных взят с https://www.kaggle.co... | github_jupyter |
```
from sklearn.datasets import make_blobs
X, y = make_blobs(n_samples=50, centers=2, cluster_std=0.5, random_state=4)
y = 2 * y - 1
plt.scatter(X[y == -1, 0], X[y == -1, 1], marker='o', label="-1 class")
plt.scatter(X[y == +1, 0], X[y == +1, 1], marker='x', label="+1 class")
plt.xlabel("x1")
plt.ylabel("x2")
plt.leg... | github_jupyter |
# LeetCode #804. Unique Morse Code Words
## Question
https://leetcode.com/problems/unique-morse-code-words/
International Morse Code defines a standard encoding where each letter is mapped to a series of dots and dashes, as follows: "a" maps to ".-", "b" maps to "-...", "c" maps to "-.-.", and so on.
Fo... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# 06. Distributed CNTK using custom docker images
In this tutorial, you will train a CNTK model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using a custom docker image and distributed training.
## Prerequisites
* ... | github_jupyter |
<a href="https://colab.research.google.com/github/skojaku/cidre/blob/second-edit/examples/example.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# About this notebook
In this notebook, we apply CIDRE to a network with communities and demonstrate h... | github_jupyter |
# Fine-Tuning a BERT Model and Create a Text Classifier
In the previous section, we've already performed the Feature Engineering to create BERT embeddings from the `reviews_body` text using the pre-trained BERT model, and split the dataset into train, validation and test files. To optimize for Tensorflow training, we ... | github_jupyter |
```
import pandas as pd
import numpy as np
from tqdm import tqdm
import seaborn as sns
import matplotlib.pyplot as plt
import re
from nltk.corpus import stopwords
stop = list(set(stopwords.words('english')))
f_train = open('../data/train_14k_split_conll.txt','r',encoding='utf8')
line_train = f_train.readlines()
f_val... | github_jupyter |
# 2-22: Intro to scikit-learn
<img src="https://www.cityofberkeley.info/uploadedImages/Public_Works/Level_3_-_Transportation/DSC_0637.JPG" style="width: 500px; height: 275px;" />
---
** Regression** is useful for predicting a value that varies on a continuous scale from a bunch of features. This lab will introduce th... | github_jupyter |
# Author: Faique Ali
## Task 01 : Prediction Using Supervised ML
<p>
Using Linear Regression, predict the percentage of an student based on his no. of study hours.
</p>
# Imports
```
import pandas as pd
from pandas import DataFrame
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_... | github_jupyter |
**Chapter 19 – Training and Deploying TensorFlow Models at Scale**
_This notebook contains all the sample code in chapter 19._
<table align="left">
<td>
<a target="_blank" href="https://colab.research.google.com/github/ageron/handson-ml2/blob/master/19_training_and_deploying_at_scale.ipynb"><img src="https://ww... | github_jupyter |
# Longest Palindromic Subsequence
In this notebook, you'll be tasked with finding the length of the *Longest Palindromic Subsequence* (LPS) given a string of characters.
As an example:
* With an input string, `ABBDBCACB`
* The LPS is `BCACB`, which has `length = 5`
In this notebook, we'll focus on finding an optimal... | github_jupyter |
# Centerpartiets budgetmotion 2022
https://www.riksdagen.se/sv/dokument-lagar/dokument/motion/centerpartiets-budgetmotion-2022_H9024121
```
import pandas as pd
import requests
pd.options.mode.chained_assignment = None
multiplier = 1_000_000
docs = [
{'utgiftsområde': 1, 'dok_id': 'H9024141'},
{'utgiftsområde... | github_jupyter |
## Plotting of profile results
```
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# common
import os
import os.path as op
# pip
import numpy as np
import pandas as pd
import math
import xarray as xr
import matplotlib.pyplot as plt
from matplotlib import gridspec
# DEV: override installed teslakit
import sys
sys.path... | github_jupyter |
# Dropout regularization with gluon
```
import mxnet as mx
import numpy as np
from mxnet import gluon
from tqdm import tqdm_notebook as tqdm
```
## Context
```
ctx = mx.cpu()
```
## The MNIST Dataset
```
batch_size = 64
num_inputs = 784
num_outputs = 10
def transform(data, label):
return data.astype(np.float32... | github_jupyter |
# Update rules
```
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d.axes3d as p3
import matplotlib.animation as animation
from IPython.display import HTML
from matplotlib import cm
from matplotlib.colors import LogNorm
def sgd(f, df, x0, y0, lr, steps):
x = np.zeros(steps + 1)
y ... | github_jupyter |
# Sorting Objects in Instance Catalogs
_Bryce Kalmbach_
This notebook provides a series of commands that take a Twinkles Phosim Instance Catalog and creates different pandas dataframes for different types of objects in the catalog. It first separates the full sets of objects in the Instance Catalogs before picking ou... | github_jupyter |
# Cloud-based machine learning || 云端机器学习
Thus far, we have looked at building and fitting ML models “locally.” True, the notebooks have been located in the cloud themselves, but the models with all of their predictive and classification power are stuck in those notebooks. To use these models, you would have to load d... | github_jupyter |
# Mumbai House Price Prediction - Supervised Machine Learning-Regression Problem
## Data Preprocessing
# The main goal of this project is to Predict the price of the houses in Mumbai using their features.
# Import Libraries
```
# importing necessary libraries
import pandas as pd
import matplotlib.pyplot as plt
%ma... | github_jupyter |
```
from scipy.stats import ranksums, sem
import numpy as np
from statannot import add_stat_annotation
import copy
import os
import matplotlib.pyplot as plt
import matplotlib
save_dir = os.path.join("/analysis/fabiane/documents/publications/patch_individual_filter_layers/MIA_revision")
plt.style.use('ggplot')
matplotli... | github_jupyter |
```
# Import dependencies pandas,
# requests, gmaps, census, and finally config's census_key and google_key
# Declare a variable "c" and set it to the census with census_key.
# https://github.com/datamade/census
# We're going to use the default year 2016, however feel free to use another year.
# Run a censu... | github_jupyter |
# Speech Identity Inference
Let's check if the pretrained model can really identify speakers.
```
import os
import numpy as np
import pandas as pd
from sklearn import metrics
from tqdm.notebook import tqdm
from IPython.display import Audio
from matplotlib import pyplot as plt
%matplotlib inline
import tensorflow as... | github_jupyter |
```
import numpy as np
import cs_vqe as c
import ast
import os
from openfermion import qubit_operator_sparse
import conversion_scripts as conv_scr
import scipy as sp
from openfermion import qubit_operator_sparse
import conversion_scripts as conv_scr
from openfermion.ops import QubitOperator
# with open("hamiltonians.t... | github_jupyter |
> **Note:** In most sessions you will be solving exercises posed in a Jupyter notebook that looks like this one. Because you are cloning a Github repository that only we can push to, you should **NEVER EDIT** any of the files you pull from Github. Instead, what you should do, is either make a new notebook and write you... | github_jupyter |
# MASH analysis pipeline with data-driven prior matrices
This notebook is a pipeline written in SoS to run `flashr + mashr` for multivariate analysis described in Urbut et al (2019). This pipeline was last applied to analyze GTEx V8 eQTL data, although it can be used as is to perform similar multivariate analysis for ... | github_jupyter |
# Visualisation in Python - Matplotlib
Here is the sales dataset for an online retailer. The data is collected over a period of three years: 2012 to 2015. It contains the information of sales made by the company.
The products captured belong to three categories:
Furniture
Office Supplies
Technology
Also, the comp... | github_jupyter |
### Set Data Path
```
from pathlib import Path
base_dir = Path("data")
train_dir = base_dir/Path("train")
validation_dir = base_dir/Path("validation")
test_dir = base_dir/Path("test")
```
### Image Transform Function
```
from torchvision import transforms
transform = transforms.Compose([
transforms.Resize((22... | github_jupyter |
# Partitioning feature space
**Make sure to get latest dtreeviz**
```
! pip install -q -U dtreeviz
! pip install -q graphviz==0.17 # 0.18 deletes the `run` func I need
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression, Ridge, Lasso, LogisticRegression
from sklearn.ensemble impo... | github_jupyter |
## Dataset
The CIFAR-10 dataset (Canadian Institute For Advanced Research) is a collection of images that are commonly used to train machine learning and computer vision algorithms. It is one of the most widely used datasets for machine learning research. The CIFAR-10 dataset contains 60,000 32x32 color images in 10 d... | github_jupyter |
# Self Supervised Learning Fastai Extension
> Implementation of popular SOTA self-supervised learning algorithms as Fastai Callbacks.
You may find documentation [here](https://keremturgutlu.github.io/self_supervised) and github repo [here](https://github.com/keremturgutlu/self_supervised/tree/master/)
## Install
`p... | github_jupyter |
# Compute norm from function space
```
from dolfin import *
import dolfin as df
import numpy as np
import logging
df.set_log_level(logging.INFO)
df.set_log_level(WARNING)
mesh = RectangleMesh(0, 0, 1, 1, 10, 10)
#mesh = Mesh(Rectangle(-10, -10, 10, 10) - Circle(0, 0, 0.1), 10)
V = FunctionSpace(m... | github_jupyter |
## Create Data
```
import numpy as np
import matplotlib.pyplot as plt
from patsy import dmatrix
from statsmodels.api import GLM, families
def simulate_poisson_process(rate, sampling_frequency):
return np.random.poisson(rate / sampling_frequency)
def plot_model_vs_true(time, spike_train, firing_rate, conditional_... | github_jupyter |
```
# Dependencies
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import timedelta
import time
from datetime import date
# Import SQL Alchemy
from sqlalchemy import create_engine, ForeignKey, func
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
... | github_jupyter |
## Global Air Pollution Measurements
* [Air Quality Index - Wiki](https://en.wikipedia.org/wiki/Air_quality_index)
* [BigQuery - Wiki](https://en.wikipedia.org/wiki/BigQuery)
In this notebook data is extracted from *BigQuery Public Data* assesible exclusively only in *Kaggle*. The BigQurey Helper Object will convert ... | github_jupyter |
# Breast Cancer Wisconsin (Diagnostic) Data Set
* **[T81-558: Applications of Deep Learning](https://sites.wustl.edu/jeffheaton/t81-558/)**
* Dataset provided by [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29)
* [Download Here](https://raw.githubuserco... | github_jupyter |
# Digital Signal Processing
This collection of [jupyter](https://jupyter.org/) notebooks introduces various topics of [Digital Signal Processing](https://en.wikipedia.org/wiki/Digital_signal_processing). The theory is accompanied by computational examples written in [IPython 3](http://ipython.org/). The sources of the... | github_jupyter |
# Downloading GNSS station locations and tropospheric zenith delays
**Author**: Simran Sangha, David Bekaert - Jet Propulsion Laboratory
This notebook provides an overview of the functionality included in the **`raiderDownloadGNSS.py`** program. Specifically, we outline examples on how to access and store GNSS statio... | github_jupyter |
# MACHINE LEARNING LAB - 4 ( Backpropagation Algorithm )
**4. Build an Artificial Neural Network by implementing the Backpropagation algorithm and test the same using appropriate data sets.**
```
import numpy as np
X = np.array(([2, 9], [1, 5], [3, 6]), dtype=float) # X = (hours sleeping, hours studying)
y = np... | github_jupyter |
# VarEmbed Tutorial
Varembed is a word embedding model incorporating morphological information, capturing shared sub-word features. Unlike previous work that constructs word embeddings directly from morphemes, varembed combines morphological and distributional information in a unified probabilistic framework. Varembed... | github_jupyter |
# FMskill assignment
You are working on a project modelling waves in the Southern North Sea. You have done 6 different calibration runs and want to choose the "best". You would also like to see how your best model is performing compared to a third-party model in NetCDF.
The data:
* SW model results: 6 dfs0 files t... | github_jupyter |
## Instalación de numpy
```
! pip install numpy
import numpy as np
```
### Array creation
```
my_int_list = [1, 2, 3, 4]
#create numpy array from original python list
my_numpy_arr = np.array(my_int_list)
print(my_numpy_arr)
# Array of zeros
print(np.zeros(10))
# Array of ones with type int
print(np.ones(10, dtype... | github_jupyter |
# Lets-Plot in 2020
### Preparation
```
import numpy as np
import pandas as pd
import colorcet as cc
from PIL import Image
from lets_plot import *
from lets_plot.bistro.corr import *
LetsPlot.setup_html()
df = pd.read_csv("https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/lets_plot_git_history.c... | github_jupyter |
# LSV Data Analysis and Parameter Estimation
##### First, all relevent Python packages are imported
```
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from scipy.optimize import curve_fit
from scipy.signal import savgol_filter, find_peaks, find_peaks_cwt
import pandas as pd
import math
import g... | github_jupyter |
# Test: Minimum error discrimination
In this notebook we are testing the evolution of the error probability with the number of evaluations.
```
import sys
sys.path.append('../../')
import itertools
import numpy as np
import matplotlib.pyplot as plt
from numpy import pi
from qiskit.algorithms.optimizers import SPSA... | github_jupyter |
# Terminologies
<img src="https://github.com/dorisjlee/remote/blob/master/astroSim-tutorial-img/terminology.jpg?raw=true",width=20%>
- __Domain__ (aka Grids): the whole simulation box.
- __Block__(aka Zones): group of cells that make up a larger unit so that it is more easily handled. If the code is run in parallel, y... | github_jupyter |
# Single layer Neural Network
In this notebook, we will code a single neuron and use it as a linear classifier with two inputs. The tuning of the neuron parameters is done by backpropagation using gradient descent.
```
from sklearn.datasets import make_blobs
import numpy as np
# matplotlib to display the data
import... | github_jupyter |
# From raw *.ome.tif file to kinetic properties for immobile particles
This notebook will run ...
* picasso_addon.localize.main()
* picasso_addon.autopick.main()
* spt.immobile_props.main()
... in a single run to get from the raw data to the fully evaluated data in a single stroke. We therefore:
1. Define the full ... | github_jupyter |
# What are Tensors?
```
# -*- coding: utf-8 -*-
import numpy as np
# N is batch size; D_in is input dimension;
# H is hidden dimension; D_out is output dimension.
N, D_in, H, D_out = 64, 1000, 100, 10
# Create random input and output data
x = np.random.randn(N, D_in)
y = np.random.randn(N, D_out)
# Randomly initial... | 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 |
<small><small><i>
All the IPython Notebooks in **[Python Seaborn Module](https://github.com/milaan9/12_Python_Seaborn_Module)** lecture series by **[Dr. Milaan Parmar](https://www.linkedin.com/in/milaanparmar/)** are available @ **[GitHub](https://github.com/milaan9)**
</i></small></small>
<a href="https://colab.resea... | github_jupyter |
```
import pandas as pd
import numpy as np
import mxnet as mx
from mxnet import nd, autograd, gluon, init
from mxnet.gluon import nn, rnn
import gluonnlp as nlp
import pkuseg
import multiprocessing as mp
import time
from d2l import try_gpu
import itertools
import jieba
from sklearn.metrics import accuracy_score, f1_sco... | github_jupyter |
TSG088 - Hadoop datanode logs
=============================
Steps
-----
### Parameters
```
import re
tail_lines = 500
pod = None # All
container = "hadoop"
log_files = [ "/var/log/supervisor/log/datanode*.log" ]
expressions_to_analyze = [
re.compile(".{23} WARN "),
re.compile(".{23} ERROR ")
]
log_analyz... | github_jupyter |
## Project 2: Exploring the Uganda's milk imports and exports
A country's economy depends, sometimes heavily, on its exports and imports. The United Nations Comtrade database provides data on global trade. It will be used to analyse the Uganda's imports and exports of milk in 2015:
* How much does the Uganda export an... | github_jupyter |
```
#################
# Preprocessing #
#################
# Scores by other composers from the Bach family have been removed beforehand.
# Miscellaneous scores like mass pieces have also been removed; the assumption here is that
# since different interpretations of the same piece (e.g. Ave Maria, etc) exist, including... | github_jupyter |
# Multilayer Perceptron
Some say that 9 out of 10 people who use neural networks apply a Multilayer Perceptron (MLP). A MLP is basically a feed-forward network with 3 layers (at least): an input layer, an output layer, and a hidden layer in between. Thus, the MLP has no structural loops: information always flows from ... | github_jupyter |
```
from sklearn.datasets import load_iris # iris dataset
from sklearn import tree # for fitting model
# for the particular visualization used
from six import StringIO
import pydot
import os.path
# to display graphs
%matplotlib inline
import matplotlib.pyplot
# get dataset
iris = load_iris()
iris.keys()
import pand... | github_jupyter |
## 1. Volatility changes over time
<p>What is financial risk? </p>
<p>Financial risk has many faces, and we measure it in many ways, but for now, let's agree that it is a measure of the possible loss on an investment. In financial markets, where we measure prices frequently, volatility (which is analogous to <em>standa... | github_jupyter |
```
# Copyright 2019 The Kubeflow Authors. 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 appli... | github_jupyter |
# トークトリアル 4
# リガンドベーススクリーニング：化合物類似性
#### Developed in the CADD seminars 2017 and 2018, AG Volkamer, Charité/FU Berlin
Andrea Morger and Franziska Fritz
## このトークトリアルの目的
このトークトリアルでは、化合物をエンコード（記述子、フィンガープリント）し、比較（類似性評価）する様々なアプローチを取り扱います。さらに、バーチャルスクリーニングを実施します。バーチャルスクリーニングは、ChEMBLデータベースから取得し、リピンスキーのルールオブファイブでフィルタリングをか... | github_jupyter |
<a href="https://colab.research.google.com/github/adasegroup/ML2021_seminars/blob/master/seminar13/gp.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## Gaussian Processes (GP) with GPy
In this notebook we are going to use GPy library for GP modeli... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from importlib import reload
from deeprank.dataset import DataLoader, PairGenerator, ListGenerator
from deeprank import utils
seed =... | github_jupyter |
```
from IPython.display import Image
```
This is a follow on from Tutorial 1 where we browsed the Ocean marketplace and downloaded the imagenette dataset. In this tutorial, we will create a model that trains (and overfits) on the small amount of sample data. Once we know that data interface of the input is compatible... | github_jupyter |
```
from matplotlib import pyplot as plt
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
tf.__version__
model = tf.keras.models.load_model("runs/machine_translation/2")
```
https://www.tensorflow.org/beta/tutorials/text/transformer#evaluate
```
tokenizer_pt = tfds.features.text.SubwordTe... | github_jupyter |
```
import os
import pandas as pd
from bs4 import BeautifulSoup
import sys
import re
from nltk.stem import PorterStemmer
from nltk.tokenize import sent_tokenize, word_tokenize
ps = PorterStemmer()
print os.getcwd();
# if necessary change the directory
#os.chdir('c:\\Users\..')
data = pd.read_csv("nightlife_sanfrancisco... | github_jupyter |
# Tutorial Part 2: Learning MNIST Digit Classifiers
In the previous tutorial, we learned some basics of how to load data into DeepChem and how to use the basic DeepChem objects to load and manipulate this data. In this tutorial, you'll put the parts together and learn how to train a basic image classification model in... | github_jupyter |
# QST CGAN with thermal noise in the channel (convolution)
```
import numpy as np
from qutip import Qobj, fidelity
from qutip.wigner import qfunc
from qutip.states import thermal_dm
from qutip import coherent_dm
from qutip.visualization import plot_wigner_fock_distribution
import tensorflow_addons as tfa
import te... | github_jupyter |
# T008 · Protein data acquisition: Protein Data Bank (PDB)
Authors:
- Anja Georgi, CADD seminar, 2017, Charité/FU Berlin
- Majid Vafadar, CADD seminar, 2018, Charité/FU Berlin
- Jaime Rodríguez-Guerra, Volkamer lab, Charité
- Dominique Sydow, Volkamer lab, Charité
__Talktorial T008__: This talktorial is part of the... | github_jupyter |
# Image classification training on a DEBIAI project with a dataset generator
This tutorial shows how to classify images of flowers after inserting the project contextual into DEBIAI.
Based on the tensorflow tutorial : https://www.tensorflow.org/tutorials/images/classification
```
# Import TensorFlow and other librar... | github_jupyter |
```
import os
import sys
import json
import tempfile
import pandas as pd
import numpy as np
import datetime
from CoolProp.CoolProp import PropsSI
from math import exp, factorial, ceil
import matplotlib.pyplot as plt
%matplotlib inline
cwd = os.getcwd()
sys.path.append(os.path.normpath(os.path.join(cwd, '..', '..', ... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import re
import glob
import lzma
import pickle
import pandas as pd
import numpy as np
import requests as r
import seaborn as sns
import warnings
import matplotlib as mpl
import matplotlib.pyplot as plt
from joblib import hash
from collections import Counter
from sklearn.metrics ... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_excel(r'C:\Users\kundi\Moji_radovi\MVanalysis\datasetup\MV_DataFrame.xlsx')
df['Sat'] = df['Uplaćeno'].astype(str).str.slice(-8,-6)
df['Datum'] = df['Uplaćeno'].astype(str).str.slice(-19,-13)
df.info()
df
df.drop(columns = ['Uplaćeno'], inplace = Tr... | github_jupyter |
# Convolutional Neural Network
## Import Dependencies
```
%matplotlib inline
from imp import reload
import itertools
import numpy as np
import utils; reload(utils)
from utils import *
from __future__ import print_function
from sklearn.metrics import confusion_matrix, classification_report, f1_score
from keras.prepr... | github_jupyter |
# Comparison of the data taken with a long adaptation time
(c) 2019 Manuel Razo. This work is licensed under a [Creative Commons Attribution License CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/). All code contained herein is licensed under an [MIT license](https://opensource.org/licenses/MIT)
---
```
impo... | github_jupyter |
# DCGAN - Create Images from Random Numbers!
### Generative Adversarial Networks
Ever since Ian Goodfellow and colleagues [introduced the concept of Generative Adversarial Networks (GANs)](https://arxiv.org/abs/1406.2661), GANs have been a popular topic in the field of AI. GANs are an application of unsupervised lear... | github_jupyter |
# Download Patent DB & Adding Similarity Data
The similarity data on its own provides data on patent doc2vec vectors, and some pre-calculated similarity scores. However, it is much more useful in conjunction with a dataset containing other patent metadata. To achieve this it is useful to download a patent dataset and ... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Tra... | github_jupyter |
# Reconstructing MNIST images using Autoencoder
Now that we have understood how autoencoders reconstruct the inputs, in this section we will learn how autoencoders reconstruct the images of handwritten digits using the MNIST dataset.
In this chapter, we use keras API from the tensorflow for building the models. So ... | github_jupyter |
```
# default_exp core
```
# Few-shot Learning with GPT-J
> API details.
```
# export
import os
import pandas as pd
#hide
from nbdev.showdoc import *
import toml
s = toml.load("../.streamlit/secrets.toml", _dict=dict)
```
Using `GPT_J` model API from [Nlpcloud](https://nlpcloud.io/home/token)
```
import nlpcloud
c... | github_jupyter |
# Synthetic Images from simulated data
## Authors
Yi-Hao Chen, Sebastian Heinz, Kelle Cruz, Stephanie T. Douglas
## Learning Goals
- Assign WCS astrometry to an image using ```astropy.wcs```
- Construct a PSF using ```astropy.modeling.model```
- Convolve raw data with PSF using ```astropy.convolution```
- Calculate... | github_jupyter |
# Candlestick Upside Gap Two Crows
https://www.investopedia.com/terms/u/upside-gap-two-crows.asp
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import talib
import warnings
warnings.filterwarnings("ignore")
# yahoo finance is used to fetch data
import yfinance as yf
yf.pdr_override()
# ... | github_jupyter |
# Slope Analysis
This project use the change of holding current slope to identify drug responders.
## Analysis Steps
The `getBaselineAndMaxDrugSlope` function smoothes the raw data by the moving window decided by `filterSize`, and analyzes the smoothed holding current in an ABF and returns baseline slope and drug sl... | github_jupyter |
# TensorFlow Regression Example
## Creating Data
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
# 1 Million Points
x_data = np.linspace(0.0,10.0,1000000)
noise = np.random.randn(len(x_data))
# y = mx + b + noise_levels
b = 5
y_true = (0.5 * x_data ) + 5 + noise
my_data... | github_jupyter |
# Image classification training with image format
1. [Introduction](#Introduction)
2. [Prerequisites and Preprocessing](#Prerequisites-and-Preprocessing)
1. [Permissions and environment variables](#Permissions-and-environment-variables)
2. [Prepare the data](#Prepare-the-data)
3. [Fine-tuning The Image Classificat... | github_jupyter |
## PySpark Data Engineering Practice (Sandboxing)
### Olympic Athlete Data
This notebook is for data engineering practicing purposes.
During this notebook I want to explore data by using and learning PySpark.
The data is from: https://www.kaggle.com/mysarahmadbhat/120-years-of-olympic-history
```
## Imports
from pysp... | github_jupyter |
## The Golden Standard
In the previous session, we saw why and how association is different from causation. We also saw what is required to make association be causation.
$
E[Y|T=1] - E[Y|T=0] = \underbrace{E[Y_1 - Y_0|T=1]}_{ATET} + \underbrace{\{ E[Y_0|T=1] - E[Y_0|T=0] \}}_{BIAS}
$
To recap, association becomes ... | github_jupyter |
```
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
from scipy.stats import poisson, norm
def compute_scaling_ratio(mu_drain,mu_demand,drift_sd,init_state):
drain_time = init_state/(mu_drain-mu_demand)
accum_std = drift_sd*np.sqrt(drain_time)
ratio = accum_std/init_state
retur... | github_jupyter |
# Ridge Regressor with StandardScaler
### Required Packages
```
import warnings
import numpy as np
import pandas as pd
import seaborn as se
import matplotlib.pyplot as plt
from sklearn.linear_model import Ridge
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from skl... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
from latency import run_latency, run_latency_changing_topo, run_latency_per_round, run_latency_per_round_changing_topo, nodes_latency
import sys
sys.path.append('..')
from utils import create_mixing_matrix, load_data, run, consensus
```
# Base case
```
# IID ca... | github_jupyter |
```
# import customizing_motif_vec
import extract_motif
import motif_class
import __init__
import json_utility
from importlib import reload
reload(__init__)
reload(extract_motif)
# reload(customizing_motif_vec)
reload(motif_class)
import plot_glycan_utilities
reload(plot_glycan_utilities)
import matplotlib.pyplot as pl... | github_jupyter |
```
import matplotlib.pyplot as plt
from matplotlib import style
import numpy as np
%matplotlib inline
style.use('ggplot')
x = [20,30,50]
y = [ 10,50,13]
x2 = [4,10,47,]
y2= [56,4,30]
plt.plot(x, y, 'r', label='line one', linewidth=5)
plt.plot(x2, y2, 'c', label ='line two', linewidth=5)
plt.title('Interactive plot'... | github_jupyter |
# Svenskt Kvinnobiografiskt lexikon part 5
version part 5 - 0.1
Check SKBL women if Alvin has an authority for the women
* this [Jupyter Notebook](https://github.com/salgo60/open-data-examples/blob/master/Svenskt%20Kvinnobiografiskt%20lexikon%20part%205.ipynb)
* [part 1](https://github.com/salgo60/open-data-exam... | github_jupyter |
[[source]](../api/alibi.explainers.shap_wrappers.rst)
# Tree SHAP
<div class="alert alert-info">
Note
To enable SHAP support, you may need to run:
```bash
pip install alibi[shap]
```
</div>
## Overview
The tree SHAP (**SH**apley **A**dditive ex**P**lanations) algorithm is based on the paper [From local explan... | github_jupyter |
# Using PyTorch with TensorRT through ONNX:
TensorRT is a great way to take a trained PyTorch model and optimize it to run more efficiently during inference on an NVIDIA GPU.
One approach to convert a PyTorch model to TensorRT is to export a PyTorch model to ONNX (an open format exchange for deep learning models) and... | github_jupyter |
```
import pandas as pd
import utils
import matplotlib.pyplot as plt
import random
import plotly.express as px
import numpy as np
random.seed(9000)
plt.style.use("seaborn-ticks")
plt.rcParams["image.cmap"] = "Set1"
plt.rcParams['axes.prop_cycle'] = plt.cycler(color=plt.cm.Set1.colors)
%matplotlib inline
```
In this ... | github_jupyter |
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