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# SLU09 - Classification With Logistic Regression: Exercise notebook
```
import pandas as pd
import numpy as np
import hashlib
```
In this notebook you will practice the following:
- What classification is for
- Logistic regression
- Cost function
- Binary classification
You thought that you ... | github_jupyter |
# Baby boy/girl classifier model preparation
*based on: Francisco Ingham and Jeremy Howard. Inspired by [Adrian Rosebrock](https://www.pyimagesearch.com/2017/12/04/how-to-create-a-deep-learning-dataset-using-google-images/)*
*by: Artyom Vorobyov*
Notebook execution and model training is made in Google Colab
```
fro... | github_jupyter |
```
import torch
import datasets as nlp
from transformers import LongformerTokenizerFast
tokenizer = LongformerTokenizerFast.from_pretrained('allenai/longformer-base-4096')
def get_correct_alignement(context, answer):
""" Some original examples in SQuAD have indices wrong by 1 or 2 character. We test and fix this h... | github_jupyter |
```
from IPython.display import Markdown as md
### change to reflect your notebook
_nb_loc = "09_deploying/09c_changesig.ipynb"
_nb_title = "Changing signatures of exported model"
### no need to change any of this
_nb_safeloc = _nb_loc.replace('/', '%2F')
md("""
<table class="tfo-notebook-buttons" align="left">
<td... | github_jupyter |
<a href="https://colab.research.google.com/github/Miseq/naive_imdb_reviews_model/blob/master/naive_imdb_model.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
from keras.datasets import imdb
from keras import optimizers
from keras import losses
f... | github_jupyter |
# Convolutional Neural Networks: Application
Welcome to Course 4's second assignment! In this notebook, you will:
- Implement helper functions that you will use when implementing a TensorFlow model
- Implement a fully functioning ConvNet using TensorFlow
**After this assignment you will be able to:**
- Build and t... | github_jupyter |
### Deep Kung-Fu with advantage actor-critic
In this notebook you'll build a deep reinforcement learning agent for atari [KungFuMaster](https://gym.openai.com/envs/KungFuMaster-v0/) and train it with advantage actor-critic.
鐵達尼生存預測
https://www.kaggle.com/c/titanic
# [作業目標]
- 試著調整特徵篩選的門檻值, 觀察會有什麼影響效果
# [作業重點]
- 調整相關係數過濾法的篩選門檻, 看看篩選結果的影響 (In[5]~In[8], Out[5]~Out[8])
- 調整L1 嵌入法篩選門檻, 看看篩選結果的影響 (In[9]~In[11], Out[9]~Out[11])
```
# 做完特徵工程前的所有準備 (與前範例相同)
import pandas as pd
import numpy as np
import copy
from sklearn.preprocess... | github_jupyter |
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-59152712-8"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-59152712-8');
</script>
# Convert LaTeX Sentence to SymPy Expression
## Author: Ke... | github_jupyter |
# Analyzing data with Pandas
First a little setup. Importing the pandas library as ```pd```
```
import pandas as pd
```
Set some helpful display options. Uncomment the boilerplate in this cell.
```
%matplotlib inline
pd.set_option("max_columns", 150)
pd.set_option('max_colwidth',40)
pd.options.display.float_format ... | github_jupyter |
```
library(caret, quiet=TRUE);
library(base64enc)
library(httr, quiet=TRUE)
```
# Build a Model
```
set.seed(1960)
create_model = function() {
model <- train(Species ~ ., data = iris, method = "ctree2")
return(model)
}
# dataset
model = create_model()
# pred <- predict(model, as.matrix(iris[, -5]) ... | github_jupyter |
# Predicting NYC Taxi Fares with RAPIDS
Process 380 million rides in NYC from 2015-2017.
RAPIDS is a suite of GPU accelerated data science libraries with APIs that should be familiar to users of Pandas, Dask, and Scikitlearn.
This notebook focuses on showing how to use cuDF with Dask & XGBoost to scale GPU DataFrame ... | github_jupyter |
```
#importing modules
import os
import codecs
import numpy as np
import string
import pandas as pd
```
# **Data Preprocessing**
```
#downloading and extracting the files on colab server
import urllib.request
urllib.request.urlretrieve ("https://archive.ics.uci.edu/ml/machine-learning-databases/20newsgroups-mld/20_ne... | github_jupyter |
```
```
# INTRODUCTION TO UNSUPERVISED LEARNING
Unsupervised learning is the training of a machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance. Here the task of the machine is to group unsorted information according to similarit... | github_jupyter |
```
%load_ext rpy2.ipython
%matplotlib inline
from fbprophet import Prophet
import pandas as pd
from matplotlib import pyplot as plt
import logging
logging.getLogger('fbprophet').setLevel(logging.ERROR)
import warnings
warnings.filterwarnings("ignore")
df = pd.read_csv('../examples/example_wp_log_peyton_manning.csv')
m... | github_jupyter |
### Demonstration of `flopy.utils.get_transmissivities` method
for computing open interval transmissivities (for weighted averages of heads or fluxes)
In practice this method might be used to:
* compute vertically-averaged head target values representative of observation wells of varying open intervals (including va... | github_jupyter |
```
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
# Dependencies for interaction with database:
from sqlalchemy import create_engine
from sqlalchemy.orm import Session
# Machine Learning dependencies:
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardSca... | 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 math
import numpy as np
import pandas as pd
```
### Initial conditions
```
initial_rating = 400
k = 100
things = ['Malted Milk','Rich Tea','Hobnob','Digestive']
```
### Elo Algos
```
def expected_win(r1, r2):
"""
Expected probability of player 1 beating player 2
if player 1 has rating 1 (r1)... | github_jupyter |
# Benchmark FRESA.CAD BSWIMS final Script
This algorithm implementation uses R code and a Python library (rpy2) to connect with it, in order to run the following it is necesary to have installed both on your computer:
- R (you can download in https://www.r-project.org/) <br>
- install rpy2 by <code> pip install rpy2... | github_jupyter |
This notebook contains an implementation of the third place result in the Rossman Kaggle competition as detailed in Guo/Berkhahn's [Entity Embeddings of Categorical Variables](https://arxiv.org/abs/1604.06737).
The motivation behind exploring this architecture is it's relevance to real-world application. Much of our f... | github_jupyter |
<a href="https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/verbose/alphafold_noTemplates_noMD.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#AlphaFold
```
#################
# WARNING
#################
# - This notebook is in... | github_jupyter |
# mlrose Tutorial Examples - Genevieve Hayes
## Overview
mlrose is a Python package for applying some of the most common randomized optimization and search algorithms to a range of different optimization problems, over both discrete- and continuous-valued parameter spaces. This notebook contains the examples used in ... | github_jupyter |
<!--NAVIGATION-->
_______________
Este documento puede ser utilizado de forma interactiva en las siguientes plataformas:
- [Google Colab](https://colab.research.google.com/github/masdeseiscaracteres/ml_course/blob/master/material/05_random_forests.ipynb)
- [MyBinder](https://mybinder.org/v2/gh/masdeseiscaracteres/ml... | github_jupyter |
```
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
% matplotlib inline
```
### Loading Training Transactions Data
```
tr_tr = pd.read_csv('data/train_transaction.csv', index_col='TransactionID')
print('Rows :', tr_tr.shape[0],' Columns : ',tr_tr.shape... | github_jupyter |
```
midifile = 'data/chopin-fantaisie.mid'
import time
import copy
import subprocess
from abc import abstractmethod
import numpy as np
import midi # Midi file parser
from midipattern import MidiPattern
from distorter import *
from align import align_frame_to_frame, read_align, write_align
MidiPattern.MIDI_DEVICE = 2
... | github_jupyter |
# SciPy를 이용한 최적화
- fun: 2.0
hess_inv: array([[ 0.5]])
jac: array([ 0.])
message: 'Optimization terminated successfully.'
nfev: 9 # SciPy는 Sympy가 아니라서, Symbolic을 활용하지 못하기에 수치 미분을 함 - 1위치에서 3번 계산됨 nit가 2 이라는거는 2번 뛰엇나느 것이며, 3곳에서 9번함수를돌림..
nit: 2
njev: 3
status: 0
success: True
x: ... | github_jupyter |
# RadarCOVID-Report
## Data Extraction
```
import datetime
import json
import logging
import os
import shutil
import tempfile
import textwrap
import uuid
import matplotlib.pyplot as plt
import matplotlib.ticker
import numpy as np
import pandas as pd
import pycountry
import retry
import seaborn as sns
%matplotlib in... | github_jupyter |
```
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import math as m
%matplotlib inline
import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import random
from torch.utils.data import... | github_jupyter |
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
# Reinforcement Learning in A... | github_jupyter |
```
import csv
import tensorflow as tf
import numpy as np
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
!wget --no-check-certificate \
https://storage.googleapis.com/laurencemoroney-blog.appspot.com/bbc-text.csv \
-O /tmp/bbc-text.cs... | github_jupyter |
# Learning Tree-augmented Naive Bayes (TAN) Structure from Data
In this notebook, we show an example for learning the structure of a Bayesian Network using the TAN algorithm. We will first build a model to generate some data and then attempt to learn the model's graph structure back from the generated data.
For comp... | 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/introduction).**
---
As a warm-up, you'll review some machine learning fundamentals and submit you... | github_jupyter |
<a href="https://colab.research.google.com/github/magenta/ddsp/blob/master/ddsp/colab/demos/timbre_transfer.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
##### Copyright 2020 Google LLC.
Licensed under the Apache License, Version 2.0 (the "Licens... | github_jupyter |
```
import numpy as np
import copy
from sklearn import preprocessing
import tensorflow as tf
from tensorflow import keras
import os
import pandas as pd
from matplotlib import pyplot as plt
from numpy.random import seed
np.random.seed(2095)
data = pd.read_excel('Dataset/CardiacPrediction.xlsx')
data.drop(['SEQN','Annual... | github_jupyter |
# Importing libraries
```
import nltk
import glob
import os
import numpy as np
import string
import pickle
from gensim.models import Doc2Vec
from gensim.models.doc2vec import LabeledSentence
from tqdm import tqdm
from sklearn import utils
from sklearn.svm import LinearSVC
from sklearn.neural_network import MLPClassifi... | github_jupyter |
# Lab 2: Object-Oriented Python
## Overview
After have covered rules, definitions, and semantics, we'll be playing around with actual classes, writing a fair chunk of code and building several classes to solve a variety of problems.
Recall our starting definitions:
- An *object* has identity
- A *name* is a referen... | github_jupyter |
## KITTI Object Detection finetuning
### This notebook is used to lunch the finetuning of FPN on KITTI object detection benchmark, the code fetches COCO weights for weight initialization
```
data_path = "../datasets/KITTI/data_object_image_2/training"
import detectron2
from detectron2.utils.logger import setup_logger
... | github_jupyter |
# SP LIME
## Regression explainer with boston housing prices dataset
```
from sklearn.datasets import load_boston
import sklearn.ensemble
import sklearn.linear_model
import sklearn.model_selection
import numpy as np
from sklearn.metrics import r2_score
np.random.seed(1)
#load example dataset
boston = load_boston()
... | github_jupyter |
# Practice: Basic Statistics I: Averages
For this practice, let's use the Boston dataset.
```
# Import the numpy package so that we can use the method mean to calculate averages
import numpy as np
# Import the load_boston method
from sklearn.datasets import load_boston
# Import pandas, so that we can work with the d... | github_jupyter |
# 第2章 スカラー移流方程式（数値計算法の基礎）
## 2.2 [3] 空間微分項に対する１次精度風上差分の利用
```
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
```
(1) $\Delta t = 0.05, \Delta x = 0.1$
初期化
```
c = 1
dt = 0.05
dx = 0.1
jmax = 21
nmax = 6
x = np.linspace(0, dx * (jmax - 1), jmax)
q = np.zeros(jmax)
for j in range(jmax):
... | github_jupyter |
```
from functools import wraps
import time
def show_args(function):
@wraps(function)
def wrapper(*args, **kwargs):
print('hi from decorator - args:')
print(args)
result = function(*args, **kwargs)
print('hi again from decorator - kwargs:')
print(kwargs)
retu... | github_jupyter |
<a href="https://colab.research.google.com/github/yukinaga/object_detection/blob/main/section_3/03_exercise.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# 演習
RetinaNetで、物体の領域を出力する`regression_head`も訓練対象に加えてみましょう。
モデルを構築するコードに、追記を行なってください。
## 各設... | github_jupyter |
### Лекция 7. Исключения
https://en.cppreference.com/w/cpp/language/exceptions
https://en.cppreference.com/w/cpp/error
https://apprize.info/c/professional/13.html
<br />
##### Зачем нужны исключения
Для обработки исключительных ситуаций.
Как вариант - обработка ошибок.
<br />
###### Как пользоваться исключения... | github_jupyter |
```
import numpy as np
from sklearn.datasets import load_iris
# Loading the dataset
iris = load_iris()
X_raw = iris['data']
y_raw = iris['target']
# Isolate our examples for our labeled dataset.
n_labeled_examples = X_raw.shape[0]
training_indices = np.random.randint(low=0, high=len(X_raw)+1, size=3)
# Defining the ... | github_jupyter |
```
import pandas as pd
import numpy as np
import os
from sklearn.metrics import mean_squared_error, mean_absolute_error
import matplotlib.pyplot as plt
import pickle
import random
import train
from model import NNModelEx
pd.set_option('display.max_columns', 999)
# For this model, the data preprocessing part is alread... | github_jupyter |
```
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
credit_df = pd.read_csv('German Credit Data.csv')
credit_df
credit_df.info()
X_features = list(credit_df.columns)
X_features.remove('status')
X_features
encoded_df = pd.get_dummies(credit_df[X_features],d... | github_jupyter |
# Comparing Training and Test and Parking and Sensor Datasets
```
import sys
import pandas as pd
import numpy as np
import datetime as dt
import time
import matplotlib.pyplot as plt
sys.path.append('../')
from common import reorder_street_block, process_sensor_dataframe, get_train, \
feat_eng, add_... | github_jupyter |
This notebook is part of the $\omega radlib$ documentation: https://docs.wradlib.org.
Copyright (c) $\omega radlib$ developers.
Distributed under the MIT License. See LICENSE.txt for more info.
# Export a dataset in GIS-compatible format
In this notebook, we demonstrate how to export a gridded dataset in GeoTIFF and... | github_jupyter |
# FAO Economic and Employment Stats
Two widgets for the 'People' tab.
- No of people employed full time (```forempl``` x 1000)
- ...of which are female (```femempl``` x 1000)
- Net USD generate by forest ({```usdrev``` - ```usdexp```} x 1000)
- GDP in USD in 2012 (```gdpusd2012``` x 1000) **NOTE: GDP in year=9999**
... | github_jupyter |
```
import numpy as np
import tensorflow as tf
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score
import json
import pickle
from sklearn.externals import joblib
import sys
sys.path.append('../src/')
from TFExpMachine import TFExpMachine, simple_batcher
```
# Load data (see m... | github_jupyter |
# Python 101
```
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
```
### First code in Python
#### Running (executing) a cell
Jupyter Notebooks allow code to be separated into sections that can be executed independent of one another. These sections are call... | github_jupyter |
# Working with Data in OpenCV
Now that we have whetted our appetite for machine learning, it is time to delve a little
deeper into the different parts that make up a typical machine learning system.
Machine learning is all about building mathematical models in order
to understand data. The learning aspect enters this... | github_jupyter |
<a href="https://colab.research.google.com/github/alijablack/data-science/blob/main/Wikipedia_NLP_Sentiment_Analysis.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Natural Language Processing
## Problem Statement
Use natural language processing... | github_jupyter |
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Libraries-&-settings" data-toc-modified-id="Libraries-&-settings-1"><span class="toc-item-num">1 </span>Libraries & settings</a></span></li><li><span><a href="#Metrics" data-toc-modif... | github_jupyter |
# Introduction to Modeling Libraries
```
import numpy as np
import pandas as pd
np.random.seed(12345)
import matplotlib.pyplot as plt
plt.rc('figure', figsize=(10, 6))
PREVIOUS_MAX_ROWS = pd.options.display.max_rows
pd.options.display.max_rows = 20
np.set_printoptions(precision=4, suppress=True)
```
## Interfacing Be... | github_jupyter |
```
import torch
# Check if pytorch is using GPU:
print('Used device name: {}'.format(torch.cuda.get_device_name(0)))
```
Import your google drive if necessary.
```
from google.colab import drive
drive.mount('/content/drive')
import sys
import os
ROOT_DIR = 'your_dir'
sys.path.insert(0, ROOT_DIR)
import pickle
import... | github_jupyter |
# Notebook version of NSGA-II constrained, without scoop
```
%matplotlib inline
#!/usr/bin/env python
# This file is part of DEAP.
#
# DEAP is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as
# published by the Free Software Foundation, ... | github_jupyter |
# 1. Introduction
This notebook demonstrates how to create two parallel video pipelines using the GStreamer multimedia framework:
* The first pipeline captures video from a V4L2 device and displays the output on a monitor using a DRM/KMS display device.
* The secon... | github_jupyter |
# Running a Federated Cycle with Synergos
In a federated learning system, there are many contributory participants, known as Worker nodes, which receive a global model to train on, with their own local dataset. The dataset does not leave the individual Worker nodes at any point, and remains private to the node.
The j... | github_jupyter |
```
import torch
import torch.nn as nn
import numpy as np
from copy import deepcopy
device = "cuda" if torch.cuda.is_available() else "cpu"
class RBF(nn.Module):
def __init__(self):
super(RBF, self).__init__()
torch.cuda.manual_seed(0)
self.rbf_clt = self.init_clt()
self.rb... | github_jupyter |
```
import os
import numpy as np
import sys
import matplotlib.pyplot as plt
from matplotlib import rc
from matplotlib.pyplot import cm
from library.trajectory import Trajectory
# uzh trajectory toolbox
sys.path.append(os.path.abspath('library/rpg_trajectory_evaluation/src/rpg_trajectory_evaluation'))
import plot_utils ... | 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 |
# Distributed Object Tracker RL training with Amazon SageMaker RL and RoboMaker
---
## Introduction
In this notebook, we show you how you can apply reinforcement learning to train a Robot (named Waffle) track and follow another Robot (named Burger) by using the [Clipped PPO](https://coach.nervanasys.com/algorithms/p... | github_jupyter |
# This jupyter notebook contains examples of
- some basic functions related to Global Distance Test (GDT) analyses
- local accuracy plot
```
%matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
import MDAnalysis as mda
import pyrexMD.misc as misc
import pyrexMD.core as core
import pyrexMD.topology ... | github_jupyter |
# Text Using Markdown
**If you double click on this cell**, you will see the text change so that all of the formatting is removed. This allows you to edit this block of text. This block of text is written using [Markdown](http://daringfireball.net/projects/markdown/syntax), which is a way to format text using headers,... | github_jupyter |
<h3> ABSTRACT </h3>
All CMEMS in situ data products can be found and downloaded after [registration](http://marine.copernicus.eu/services-portfolio/register-now/) via [CMEMS catalogue] (http://marine.copernicus.eu/services-portfolio/access-to-products/).
Such channel is advisable just for sporadic netCDF donwloading ... | github_jupyter |
# TensorFlow script mode training and serving
Script mode is a training script format for TensorFlow that lets you execute any TensorFlow training script in SageMaker with minimal modification. The [SageMaker Python SDK](https://github.com/aws/sagemaker-python-sdk) handles transferring your script to a SageMaker train... | github_jupyter |
# Rekurrente Netze (RNNs)
## Sequentialle Daten
<img src="img/ag/Figure-22-001.png" style="width: 10%; margin-left: auto; margin-right: auto;"/>
## Floating Window
<img src="img/ag/Figure-22-002.png" style="width: 20%; margin-left: auto; margin-right: auto;"/>
## Verarbeitung mit MLP
<img src="img/ag/Figure-22-00... | github_jupyter |
# This notebook is copied from [here](https://github.com/warmspringwinds/tensorflow_notes/blob/master/tfrecords_guide.ipynb) with some small changes
---
### Introduction
In this post we will cover how to convert a dataset into _.tfrecord_ file.
Binary files are sometimes easier to use, because you don't have to spec... | github_jupyter |
# Custom Models in pycalphad: Viscosity
## Viscosity Model Background
We are going to take a CALPHAD-based property model from the literature and use it to predict the viscosity of Al-Cu-Zr liquids.
For a binary alloy liquid under small undercooling, Gąsior suggested an entropy model of the form
$$\eta = (\sum_i x_i... | github_jupyter |
# Section 3.3
```
%run preamble.py
danish = pd.read_csv("../Data/danish.csv").x.values
```
# MLE of composite models
```
parms, BIC, AIC = mle_composite(danish, (1,1,1), "gam-par")
fit_gam_par = pd.DataFrame(np.append(parms, [AIC, BIC])).T
fit_gam_par.columns = ["shape", "tail", "thres", "AIC","BIC"]
print(fit_gam_p... | github_jupyter |
# "[Prob] Basics of the Poisson Distribution"
> "Some useful facts about the Poisson distribution"
- toc:false
- branch: master
- badges: false
- comments: true
- author: Peiyi Hung
- categories: [category, learning, probability]
# Introduction
The Poisson distribution is an important discrete probability distributi... | github_jupyter |
# Introduction
```
#r "BoSSSpad.dll"
using System;
using System.Collections.Generic;
using System.Linq;
using ilPSP;
using ilPSP.Utils;
using BoSSS.Platform;
using BoSSS.Platform.LinAlg;
using BoSSS.Foundation;
using BoSSS.Foundation.XDG;
using BoSSS.Foundation.Grid;
using BoSSS.Foundation.Grid.Classic;
... | github_jupyter |
# <img style="float: left; padding-right: 10px; width: 45px" src="https://raw.githubusercontent.com/Harvard-IACS/2018-CS109A/master/content/styles/iacs.png"> CS109B Data Science 2: Advanced Topics in Data Science
## Lab 2 - Smoothers and Generalized Additive Models
**Harvard University**<br>
**Spring 2019**<br>
**In... | github_jupyter |
### **Install ChEMBL client for getting the dataset**
#### **https://www.ebi.ac.uk/chembl/**
```
!pip install chembl_webresource_client
```
### **Import Libraries**
```
import pandas as pd
from chembl_webresource_client.new_client import new_client
```
### **Find Coronavirus Dataset**
#### **Search Target**
```
... | github_jupyter |
# Facial Keypoint Detection
This project will be all about defining and training a convolutional neural network to perform facial keypoint detection, and using computer vision techniques to transform images of faces. The first step in any challenge like this will be to load and visualize the data you'll be working ... | github_jupyter |
```
import warnings
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
from astropy.io import fits
from astropy.table import Table
import pandas as pd
import numpy as np
np.seterr(divide='ignore')
warnings.filterwarnings("ignore", category=RuntimeWarning)
class HRCevt... | github_jupyter |
# Parameterizing with Continuous Variables
```
from IPython.display import Image
```
## Continuous Factors
1. Base Class for Continuous Factors
2. Joint Gaussian Distributions
3. Canonical Factors
4. Linear Gaussian CPD
In many situations, some variables are best modeled as taking values in some continuous space. E... | 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 |
```
# 使下面的代码支持python2和python3
from __future__ import division, print_function, unicode_literals
# 查看python的版本是否为3.5及以上
import sys
assert sys.version_info >= (3, 5)
# 查看sklearn的版本是否为0.20及以上
import sklearn
assert sklearn.__version__ >= "0.20"
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
... | github_jupyter |
```
import pandas as pd
from datetime import datetime, timedelta
import time
import requests
import numpy as np
import json
import urllib
from pandas.io.json import json_normalize
import re
import os.path
import zipfile
from glob import glob
url ="https://api.usaspending.gov/api/v1/awards/?limit=100"
r = requests.get(u... | github_jupyter |
# Understanding Classification and Logistic Regression with Python
## Introduction
This notebook contains a short introduction to the basic principles of classification and logistic regression. A simple Python simulation is used to illustrate these principles. Specifically, the following steps are performed:
- A dat... | github_jupyter |
# Implementation of Softmax Regression from Scratch
:label:`chapter_softmax_scratch`
Just as we implemented linear regression from scratch,
we believe that multiclass logistic (softmax) regression
is similarly fundamental and you ought to know
the gory details of how to implement it from scratch.
As with linear regr... | github_jupyter |
<table align="left">
<td>
<a target="_blank" href="https://colab.research.google.com/github/ShopRunner/collie/blob/main/tutorials/05_hybrid_model.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" /> Run in Google Colab</a>
</td>
<td>
<a target="_blank" href="https://github.com/ShopRu... | github_jupyter |
# Implicit Georeferencing
This workbook sets explicit georeferences from implicit georeferencing through names of extents given in dataset titles or keywords.
A file `sources.py` needs to contain the CKAN and SOURCE config as follows:
```
CKAN = {
"dpaw-internal":{
"url": "http://internal-data.dpaw.wa.gov.au/"... | github_jupyter |
<a href="https://colab.research.google.com/github/xavoliva6/dpfl_pytorch/blob/main/experiments/exp_FedMNIST.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#Experiments on FedMNIST
**Colab Support**<br/>
Only run the following lines if you want to ... | github_jupyter |
```
# for reading and validating data
import emeval.input.spec_details as eisd
import emeval.input.phone_view as eipv
import emeval.input.eval_view as eiev
# Visualization helpers
import emeval.viz.phone_view as ezpv
import emeval.viz.eval_view as ezev
import emeval.viz.geojson as ezgj
import pandas as pd
# Metrics hel... | github_jupyter |
<img width="100" src="https://carbonplan-assets.s3.amazonaws.com/monogram/dark-small.png" style="margin-left:0px;margin-top:20px"/>
# Forest Emissions Tracking - Validation
_CarbonPlan ClimateTrace Team_
This notebook compares our estimates of country-level forest emissions to prior estimates from other
groups. The ... | github_jupyter |
*Call expressions* invoke [functions](functions), which are named operations.
The name of the function appears first, followed by expressions in
parentheses.
For example, `abs` is a function that returns the absolute value of the input
argument:
```
abs(-12)
```
`round` is a function that returns the input argument ... | github_jupyter |
## Don't worry if you don't understand everything at first! You're not supposed to. We will start using some "black boxes" and then we'll dig into the lower level details later.
## To start, focus on what things DO, not what they ARE.
# What is NLP?
Natural Language Processing is technique where computers tr... | github_jupyter |
To aid autoassociative recall (sparse recall using partial pattern), we need to two components -
1. each pattern remembers a soft mask of the contribution of each
element in activating it. For example, if an element varies a lot at high activation levels, that element should be masked out when determining activation. ... | github_jupyter |
Original samples in https://fslab.org/FSharp.Charting/FurtherSamples.html
```
#load "FSharp.Charting.Paket.fsx"
#load "FSharp.Charting.fsx"
```
## Sample data
```
open FSharp.Charting
open System
open System.Drawing
let data = [ for x in 0 .. 99 -> (x,x*x) ]
let data2 = [ for x in 0 .. 99 -> (x,sin(float x / 10.0))... | github_jupyter |
# Examples for Bounded Innovation Propagation (BIP) MM ARMA parameter estimation
```
import numpy as np
import scipy.signal as sps
import robustsp as rsp
import matplotlib.pyplot as plt
import matplotlib
# Fix random number generator for reproducibility
np.random.seed(1)
```
## Example 1: AR(1) with 30 percent isola... | github_jupyter |
# Perceptron
### TODO
- **[ok]** Ajouter dans le code la fonction d'évaluation du réseau
- **[ok]** Plot de $\sum |E|$ par itération (i.e. num updates par itération)
- Critere d'arrêt + générale
- Lire l'article de rérérence
- Ajouter la preuve de convergence
- Ajouter notations et explications
- Tester l'autre versi... | github_jupyter |
## Load Weight
```
import torch
import numpy as np
path = './output/0210/Zero/checkpoint_400.pth'
import os
assert(os.path.isfile(path))
weight = torch.load(path)
input_dim = weight['input_dim']
branchNum = weight['branchNum']
IOScale = weight['IOScale']
state_dict = weight['state_dict']... | github_jupyter |
# OneHotEncoder
Performs One Hot Encoding.
The encoder can select how many different labels per variable to encode into binaries. When top_categories is set to None, all the categories will be transformed in binary variables.
However, when top_categories is set to an integer, for example 10, then only the 10 most po... | github_jupyter |
```
from eva_cttv_pipeline.clinvar_xml_utils import *
from consequence_prediction.repeat_expansion_variants.clinvar_identifier_parsing import parse_variant_identifier
import os
import sys
import urllib
import requests
import xml.etree.ElementTree as ElementTree
from collections import Counter
import hgvs.parser
from ... | github_jupyter |
# GPU
```
gpu_info = !nvidia-smi
gpu_info = '\n'.join(gpu_info)
print(gpu_info)
```
# CFG
```
CONFIG_NAME = 'config41.yml'
debug = False
from google.colab import drive, auth
# ドライブのマウント
drive.mount('/content/drive')
# Google Cloudの権限設定
auth.authenticate_user()
def get_github_secret():
import json
... | github_jupyter |
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