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# LinearSVR with MinMaxScaler & Power Transformer
This Code template is for the Classification task using Support Vector Regressor (SVR) based on the Support Vector Machine algorithm with Power Transformer as Feature Transformation Technique and MinMaxScaler for Feature Scaling in a pipeline.
### Required Packages
... | github_jupyter |
<img src="../images/26-weeks-of-data-science-banner.jpg"/>
# Getting Started with Python
## About Python
<img src="../images/python-logo.png" alt="Python" style="width: 500px;"/>
Python is a
- general purpose programming language
- interpreted, not compiled
- both **dynamically typed** _and_ **strongly typed**
-... | github_jupyter |
#Instalamos pytorch
```
#pip install torch===1.6.0 torchvision===0.7.0 -f https://download.pytorch.org/whl/torch_stable.html
```
#Clonamos el repositorio para obtener el dataset
```
!git clone https://github.com/joanby/deeplearning-az.git
from google.colab import drive
drive.mount('/content/drive')
```
# Importar l... | github_jupyter |
# NLP Intent Recognition
Hallo und herzlich willkommen zum codecentric.AI bootcamp!
Heute wollen wir uns mit einem fortgeschrittenen Thema aus dem Bereich _natural language processing_, kurz _NLP_, genannt, beschäftigen:
> Wie bringt man Sprachassistenten, Chatbots und ähnlichen Systemen bei, die Absicht eines Nutze... | github_jupyter |
Copyright 2018 Google Inc.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
https://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distribut... | github_jupyter |
# Lab 2: networkX Drawing and Network Properties
```
import matplotlib.pyplot as plt
import pandas as pd
from networkx import nx
```
## TOC
1. [Q1](#Q1)
2. [Q2](#Q2)
3. [Q3](#Q3)
4. [Q4](#Q4)
```
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(11, 8))
ax = axes.flatten()
path = nx.path_graph(5)
nx.draw_networkx... | github_jupyter |
# Lesson 1 Experiments
This section just reproduces lesson 1 logic using my own code and with 30 tennis and 30 basketball player images. I chose all male players for simplicity.
```
# Put these at the top of every notebook, to get automatic reloading and inline plotting
%reload_ext autoreload
%autoreload 2
%matplotlib... | github_jupyter |
##### Copyright 2019 The TensorFlow Authors.
```
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | github_jupyter |
# Tutorial
In this notebook, we will see how to pass your own encoder and decoder's architectures to your VAE model using pythae!
```
# If you run on colab uncomment the following line
#!pip install git+https://github.com/clementchadebec/benchmark_VAE.git
import torch
import torchvision.datasets as datasets
import ma... | github_jupyter |
# Chapter 3 : pandas
```
#load watermark
%load_ext watermark
%watermark -a 'Gopala KR' -u -d -v -p watermark,numpy,pandas,matplotlib,nltk,sklearn,tensorflow,theano,mxnet,chainer,seaborn,keras,tflearn,bokeh,gensim
```
# pandas DataFrames
```
import numpy as np
import scipy as sp
import pandas as pd
```
## Load the d... | github_jupyter |
```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from ttim import *
```
### Theis
```
from scipy.special import exp1
def theis(r, t, T, S, Q):
u = r ** 2 * S / (4 * T * t)
h = -Q / (4 * np.pi * T) * exp1(u)
return h
def theisQr(r, t, T, S, Q):
u = r ** 2 * S / (4 * T * t)
... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import argparse
import sys
from time import sleep
import numpy as np
from rdkit import Chem, DataStructs
from rdkit.Chem import AllChem
from rdkit.Chem.Crippen import MolLogP
from sklearn.metrics import accuracy_score, mean_squared_error
import torch
import torch.nn as nn
impo... | github_jupyter |
# Control
In this notebook we want to control the chaos in the Henon map. The Henon map is defined by
$$
\begin{align}
x_{n+1}&=1-ax_n^2+y_n\\
y_{n+1}&=bx_n
\end{align}.
$$
```
from plotly import offline as py
from plotly import graph_objs as go
py.init_notebook_mode(connected=True)
```
### Fixed points
First we ... | github_jupyter |
# Single model
```
from consav import runtools
runtools.write_numba_config(disable=0,threads=4)
%matplotlib inline
%load_ext autoreload
%autoreload 2
# Local modules
from Model import RetirementClass
import SimulatedMinimumDistance as SMD
import figs
import funs
# Global modules
import numpy as np
import matplotlib... | github_jupyter |
# CNTK 201A Part A: CIFAR-10 Data Loader
This tutorial will show how to prepare image data sets for use with deep learning algorithms in CNTK. The CIFAR-10 dataset (http://www.cs.toronto.edu/~kriz/cifar.html) is a popular dataset for image classification, collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. ... | 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 |
```
#@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 agreed to in writing, software
# distributed u... | github_jupyter |
# MARATONA BEHIND THE CODE 2020
## DESAFIO 2: PARTE 1
### Introdução
Em projetos de ciência de dados visando a construção de modelos de *machine learning*, ou aprendizado estatístico, é muito incomum que os dados iniciais estejam já no formato ideal para a construção de modelos. São necessários vários passos interme... | github_jupyter |
```
import os
import time
import random
import pandas as pd
import numpy as np
import gc
import re
import torch
from torchtext import data
import spacy
from tqdm import tqdm_notebook, tnrange
from tqdm.auto import tqdm
from unidecode import unidecode
import random
tqdm.pandas(desc='Progress')
from collections import C... | github_jupyter |
# Discrete stochastic Erlang SEIR model
Author: Lam Ha @lamhm
Date: 2018-10-03
## Calculate Discrete Erlang Probabilities
The following function is to calculate the discrete truncated Erlang probability, given $k$ and $\gamma$:
\begin{equation*}
p_i =
\frac{1}{C(n^{E})}
\Bigl(\sum_{j=0}^{k-1}
\frac{e^{-(i-1)\g... | github_jupyter |
# <div align="center">Random Forest Classification in Python</div>
---------------------------------------------------------------------
you can Find me on Github:
> ###### [ GitHub](https://github.com/lev1khachatryan)
<img src="asset/main.png" />
<a id="top"></a> <br>
## Notebook Content
1. [The random forests al... | github_jupyter |
<a href="https://colab.research.google.com/github/zjzsu2000/CMPE297_AdvanceDL_Project/blob/main/Data_Preprocessing/Final_result.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import pandas as pd
import numpy as np
from google.colab import drive... | github_jupyter |
```
# hide
# all_tutorial
! [ -e /content ] && pip install -Uqq mrl-pypi # upgrade mrl on colab
```
# Tutorial - Conditional LSTM Language Models
>Training and using conditional LSTM language models
## LSTM Language Models
LSTM language models are a type of autoregressive generative model. This particular type of ... | github_jupyter |
# Decisiton Tree interpretability notebook
```
import os
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.tree import plot_tree
from dtreeviz.trees import *
from pycaret import classification
```
### Exploratory data analysis
Import to specify correctly the data path. Initally we can make an easy exp... | github_jupyter |
# OpenCV example. Show webcam image and detect face.
It uses Lena's face and add random noise to it if the video capture doesn't work for some reason.
https://gist.github.com/astanin/3097851
<table >
<tr>
<th></th>
<th>. All the code examples should work f... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
```
# Numerical Integration
The definite integral $\int_a^b f(x) dx$ can be computed exactly if the primitive $F$ of $f$ is known, e.g.
```
f = lambda x: np.divide(np.dot(x,np.exp(x)),np.power(x+1,2))
F = lambda x: np.divide(np.exp(x),(x+1))
... | github_jupyter |
# 012_importing_datasets
[Source](https://github.com/iArunava/Python-TheNoTheoryGuide/)
```
# Required Imports
import pandas as pd
import sklearn as sk
import sqlite3
from pandas.io import sql
# Importing CSV files from local directory
# NOTE: Make sure the Path you use contains the dataset named 'whereisthatdataset.... | github_jupyter |
```
# install composer, hiding output to keep the notebook clean
! pip install mosaicml > /dev/null 2>&1
```
# Using the Functional API
In this tutorial, we'll see an example of using Composer's algorithms in a standalone fashion with no changes to the surrounding code and no requirement to use the Composer trainer. ... | github_jupyter |
```
# default_exp distributed
#export
from fastai.basics import *
from fastai.callback.progress import ProgressCallback
from torch.nn.parallel import DistributedDataParallel, DataParallel
from fastai.data.load import _FakeLoader
```
# Distributed and parallel training
> Callbacks and helper functions to train in para... | github_jupyter |
# Description for Modules
pandas-> read our csv files
numpy-> convert the data to suitable form to feed into the classification data
seaborn and matplotlib-> For visualizations
sklearn-> To use logistic regression
```
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
% mat... | github_jupyter |
# sports-book-manager
## Example 1:
### Using the BookScraper class
Importing the scraper class and setting the domain and directory paths.
```
import sports_book_manager.book_scrape_class as bs
PointsBet = bs.BookScraper(domain=r'https://nj.pointsbet.com/sports',
directorie... | github_jupyter |
     
     
     
     
     
   
[Home Page](../../START_HERE.ipynb)
[Previous Notebook](Challenge.ipynb)
     
     
 &ems... | github_jupyter |
# UMAP on the PBMC dataset of Zheng
```
%load_ext autoreload
%autoreload 2
%env CUDA_VISIBLE_DEVICES=2
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import umap
from firelight.visualizers.colorization import get_distinct_colors
from matplotlib.colors import ListedColormap
import pic... | github_jupyter |
# Ray Crash Course - Actors
© 2019-2021, Anyscale. All Rights Reserved
Using Ray _tasks_ is great for distributing work around a cluster, but we've said nothing so far about managing distributed _state_, one of the big challenges in distributed computing. Ray ta... | github_jupyter |
Before you turn this problem in, make sure everything runs as expected. First, **restart the kernel** (in the menubar, select Kernel$\rightarrow$Restart) and then **run all cells** (in the menubar, select Cell$\rightarrow$Run All).
Make sure you fill in any place that says `YOUR CODE HERE` or "YOUR ANSWER HERE", as we... | github_jupyter |
<a href="https://githubtocolab.com/giswqs/geemap/blob/master/examples/notebooks/57_cartoee_blend.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open in Colab"/></a>
Uncomment the following line to install [geemap](https://geemap.org) and [cartopy](https://scitools.org.... | github_jupyter |
# Using the MANN Package to convert and prune an existing TensorFlow model
In this notebook, we utilize the MANN package on an existing TensorFlow model to convert existing layers to MANN layers and then prune the model.
```
# Load the MANN package and TensorFlow
import tensorflow as tf
import mann
# Load the data
(x... | github_jupyter |
# Demistifying GANs in TensorFlow 2.0
```
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tensorflow import keras
print(tf.__version__)
```
## Global Parameters
```
BATCH_SIZE = 256
BUFFER_SIZE = 60000
EPOCHES = 300
OUTPUT_DIR = "img" # The output directory where the images of the gen... | github_jupyter |
<a href="https://colab.research.google.com/github/christianhidber/easyagents/blob/master/jupyter_notebooks/intro_cartpole.ipynb"
target="_parent">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
# CartPole Gym environment with TfAgents
## Install packages (gym, tf... | github_jupyter |
Tutorials table of content:
- [Tutorial 1: Run a first scenario](./Tutorial-1_Run_your_first_scenario.ipynb)
- [Tutorial 2: Add contributivity measurements methods](./Tutorial-2_Add_contributivity_measurement.ipynb)
- Tutorial 3: Use a custom dataset
# Tutorial 3 : Use homemade dataset
With this example, we dive d... | github_jupyter |
# Programming and Database Fundamentals for Data Scientists - EAS503
Python classes and objects.
In this notebook we will discuss the notion of classes and objects, which are a fundamental concept. Using the keyword `class`, one can define a class.
Before learning about how to define classes, we will first understa... | github_jupyter |
# BigQuery ML models with feature engineering
In this notebook, we will use BigQuery ML to build more sophisticated models for taxifare prediction.
This is a continuation of our [first models](../../02_bqml/solution/first_model.ipynb) we created earlier with BigQuery ML but now with more feature engineering.
## Lear... | github_jupyter |
# Tutorial 11: Normalizing Flows for image modeling
**Filled notebook:**
[](https://github.com/ph... | github_jupyter |
```
!pip install beautifulsoup4
import urllib.request
import pandas as pd
import numpy as np
from bs4 import BeautifulSoup,Comment
import re
url = "http://www.hanban.org/hanbancn/template/ciotab_cn1.htm?v1"
response = urllib.request.urlopen(url)
#webContent = response.read().decode(response.headers.get_content_charset(... | github_jupyter |
# Distributional DQN
The final improvement to the DQN agent [1] is using distributions instead of simple average values for learning the q value function. This algorithm was presented by Bellemare et al. (2018) [2]. In their math heavy manuscript, the authors introduce the distributional Belman operator and show that i... | github_jupyter |
# Regularization
Welcome to the second assignment of this week. Deep Learning models have so much flexibility and capacity that **overfitting can be a serious problem**, if the training dataset is not big enough. Sure it does well on the training set, but the learned network **doesn't generalize to new examples** that... | github_jupyter |
# Style Transfer
In this notebook we will implement the style transfer technique from ["Image Style Transfer Using Convolutional Neural Networks" (Gatys et al., CVPR 2015)](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf).
The general idea is to take two ... | github_jupyter |
```
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from sklearn.cross_validation import train_test_split, cross_val_score, KFold
from sklearn.prepro... | github_jupyter |
## Polygon Environment Building
Devising scenarios for the polygon-based environments.
```
%load_ext autoreload
%autoreload 2
from mpb import MPB, MultipleMPB
from plot_stats import plot_planner_stats, plot_smoother_stats
from utils import latexify
from table import latex_table
from definitions import *
import matplot... | github_jupyter |
# Classical Logic Gates with Quantum Circuits
```
from qiskit import *
from qiskit.tools.visualization import plot_histogram
import numpy as np
```
Using the NOT gate (expressed as `x` in Qiskit), the CNOT gate (expressed as `cx` in Qiskit) and the Toffoli gate (expressed as `ccx` in Qiskit) create functions to imple... | github_jupyter |
# Train a Simple Audio Recognition model for microcontroller use
This notebook demonstrates how to train a 20kb [Simple Audio Recognition](https://www.tensorflow.org/tutorials/sequences/audio_recognition) model for [TensorFlow Lite for Microcontrollers](https://tensorflow.org/lite/microcontrollers/overview). It will p... | github_jupyter |
En el mundo Qt tenemos una herramienta [RAD (Rapid Application Development)](https://es.wikipedia.org/wiki/Desarrollo_r%C3%A1pido_de_aplicaciones). Esta herramienta se llama Qt DesigneEste nuevo capítulo es el último en los que enumeramos los widgets disponibles dentro de Designer, en este caso le toca el turno a los ... | github_jupyter |
```
import os, sys, glob, scipy
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
```
## Plan
1. Describe the task
2. Make the simplest visualization you can think of that contains:
- the Dependent Variable, i.e. the behavior of the participants that you're trying to mod... | github_jupyter |
<a href="https://colab.research.google.com/github/AnacletoLAB/grape/blob/main/tutorials/High_performance_graph_algorithms.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# High performance graph algorithms
A number of high performance algorithms hav... | github_jupyter |
```
%matplotlib inline
import xarray as xr
import os
import pandas as pd
import numpy as np
import skdownscale
import dask
import dask.array as da
import dask.distributed as dd
import rhg_compute_tools.kubernetes as rhgk
from utils import _convert_lons, _remove_leap_days, _convert_ds_longitude
from regridding import... | github_jupyter |
# Jupyter Example 5 for HERMES: Neutrinos
```
from pyhermes import *
from pyhermes.units import PeV, TeV, GeV, mbarn, kpc, pc, deg, rad
import astropy.units as u
import numpy as np
import healpy
import matplotlib.pyplot as plt
```
HEMRES has available two cross-section modules for $pp \rightarrow \nu$:
* one bui... | 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 |
# 04 Spark essentials
```
# Make it Python2 & Python3 compatible
from __future__ import print_function
import sys
if sys.version[0] == 3:
xrange = range
```
## Spark context
The notebook deployment includes Spark automatically within each Python notebook kernel. This means that, upon kernel instantiation, there ... | github_jupyter |
```
import pickle
import numpy as np
import mplhep
import awkward
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import uproot
import boost_histogram as bh
physics_process = "qcd"
data_baseline = awkward.Array(pickle.load(open("/home/joosep/reco/mlpf/CMSSW_12_1_0_pre3/11843.0/out.pkl", "rb")))
... | github_jupyter |
```
import pandas as pd
import pyspark.sql.functions as F
from datetime import datetime
from pyspark.sql.types import *
from pyspark import StorageLevel
import numpy as np
pd.set_option("display.max_rows", 1000)
pd.set_option("display.max_columns", 1000)
pd.set_option("mode.chained_assignment", None)
from pyspark.ml i... | github_jupyter |
<a href="https://colab.research.google.com/github/cdosrunwild/glide-text2im/blob/main/Copy_of_Disco_Diffusion_v4_1_%5Bw_Video_Inits%2C_Recovery_%26_DDIM_Sharpen%5D.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Disco Diffusion v4.1 - Now with Vid... | github_jupyter |
```
# default_exp exec.parse_data
```
# uberduck_ml_dev.exec.parse_data
Log a speech dataset to the filelist database
Usage:
```
python -m uberduck_ml_dev.exec.parse_data \
--input ~/multispeaker-root \
--format standard-multispeaker \
--ouput list.txt
```
### Supported formats:
### `standard-multispe... | github_jupyter |
# Amazon Fraud Detector - Data Profiler Notebook
### Dataset Guidance
-------
AWS Fraud Detector's Online Fraud Insights(OFI) model supports a flexible schema, enabling you to train an OFI model to your specific data and business need. This notebook was developed to help you profile your data and identify potenital... | github_jupyter |
# V0.1.6 - Simulate a Predefined Model
Example created by Wilson Rocha Lacerda Junior
```
pip install sysidentpy
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sysidentpy.metrics import root_relative_squared_error
from sysidentpy.utils.generate_data import get_miso_data, get_siso_data
fro... | github_jupyter |
# Segmentation
<div class="alert alert-info">
This tutorial is available as an IPython notebook at [Malaya/example/segmentation](https://github.com/huseinzol05/Malaya/tree/master/example/segmentation).
</div>
<div class="alert alert-info">
This module trained on both standard and local (included social media) ... | github_jupyter |
```
import pandas as pd
from sklearn.model_selection import train_test_split
df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/'
'mushroom/agaricus-lepiota.data', header=None, engine='python')
column_name = ['classes','cap-shape', 'cap-surface','cap-color','bruises?','odor',
... | github_jupyter |
# python behaves like a calculator
```
8*8
```
#### Predict the following output
```
print((5+5)/25)
print(5 + 5/25)
```
python does order of operations, etc. just like a calculator
#### Most of the notation is intuitive.
Write out the following in a cell. What value to you get?
$$ (5 \times 5 + \frac{4}{2} - ... | github_jupyter |
```
try:
from openmdao.utils.notebook_utils import notebook_mode
except ImportError:
!python -m pip install openmdao[notebooks]
```
# How to know if a System is under FD or CS
All Systems (Components and Groups) have two flags that indicate whether the System is running under finite difference or complex step... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import descartes
import geopandas as gpd
from shapely.geometry import Point, Polygon
from shapely.ops import nearest_points
import seaborn as sns
from mpl_toolkits.axes_grid1 import make_axes_locatable
import math
import time
from matplotli... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Datasets/Vectors/world_database_on_protected_areas.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td>
<... | github_jupyter |
<table><tr>
<td><img src="logos/JPL-NASA-logo_583x110.png" alt="JPL/NASA logo" style="height: 75px"/></td>
<td><img src="logos/CEOS-LOGO.png" alt="CEOS logo" style="height: 75px"/></td>
<td><img src="logos/CoverageLogoFullClear.png" alt="COVERAGE logo" style="height: 100px"/></td>
</tr></table>
# _Analytic... | github_jupyter |
```
import cv2
import os
import torch,torchvision
import torch.nn as nn
import numpy as np
import os
from tqdm import tqdm
import matplotlib.pyplot as plt
import torch.optim as optim
from torch.nn import *
from torch.utils.tensorboard import SummaryWriter
import matplotlib.pyplot as plt
import wandb
from ray import tu... | github_jupyter |
Definition of **DTLZ2 problem** with 3 objective functions:
$f_1(X) = (1 + g(x_3)) \cdot cos(x_1 \cdot \frac{\pi}{2}) \cdot cos(x_2 \cdot \frac{\pi}{2})$
$f_2(X) = (1 + g(x_3)) \cdot cos(x_1 \cdot \frac{\pi}{2}) \cdot sin(x_2 \cdot \frac{\pi}{2})$
$f_3(x) = (1 + g(x_3)) \cdot sin(x_1 \cdot \frac{\pi}{2})$
with
$-... | github_jupyter |
```
from subprocess import call
from glob import glob
from nltk.corpus import stopwords
import os, struct
from tensorflow.core.example import example_pb2
import pyrouge
import shutil
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from nltk.stem.porter ... | github_jupyter |
Based On the Canadian Marijuana Index these are the primary players in the Canadian Market.
```
from pandas_datareader import data as pdr
import fix_yahoo_finance as fyf
import matplotlib.pyplot as plt
import datetime
import numpy as np
import pandas as pd
import scipy
# import statsmodels.api as sm
from sklearn im... | github_jupyter |
```
from hyppo.ksample import KSample
from hyppo.independence import Dcorr
from combat import combat
import pandas as pd
import glob
import os
import graspy as gp
import numpy as np
from dask.distributed import Client, progress
import dask.dataframe as ddf
from scipy.stats import zscore, rankdata, mannwhitneyu
import c... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import math
from plotnine import *
from datetime import datetime
import pytz
%matplotlib inline
memory_free = pd.read_parquet("path to machine metric dataset/node_memory_MemFree/")
memory_free = memory_free / (1024 * 1024 * 1024)
memory_total =... | github_jupyter |
# Inspecting ModelSelectorResult
When we go down from multiple time-series to single time-series, the best way how to get access to all relevant information to use/access `ModelSelectorResult` objects
```
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('seaborn')
plt.rcParams['figure.figsize'] = [12,... | github_jupyter |
# NumPy
this notebook is based on the SciPy NumPy tutorial
<div class="alert alert-block alert-warning">
Note that the traditional way to import numpy is to rename it np. This saves on typing and makes your code a little more compact.</div>
```
import numpy as np
```
NumPy provides a multidimensional array class a... | github_jupyter |
# 机器学习工程师纳米学位
## 模型评价与验证
## 项目 1: 预测波士顿房价
欢迎来到机器学习工程师纳米学位的第一个项目！在此文件中，有些示例代码已经提供给你，但你还需要实现更多的功能来让项目成功运行。除非有明确要求，你无须修改任何已给出的代码。以**'练习'**开始的标题表示接下来的内容中有需要你必须实现的功能。每一部分都会有详细的指导，需要实现的部分也会在注释中以**'TODO'**标出。请仔细阅读所有的提示！
除了实现代码外，你还**必须**回答一些与项目和实现有关的问题。每一个需要你回答的问题都会以**'问题 X'**为标题。请仔细阅读每个问题，并且在问题后的**'回答'**文字框中写出完整的答案。你的项目将会根... | github_jupyter |
```
import glob
import random
import sys
from itertools import chain
from pathlib import Path
import numpy as np
import pandas as pd
from sklearn import preprocessing
from sklearn.metrics import accuracy_score, confusion_matrix
from thundersvm import SVC
from tqdm import tqdm
np.random.seed(0)
random.seed(0)
```
##... | github_jupyter |
# Matplotlib - Intro
* **matplotlib** is a Python plotting library for producing publication quality figures
* allows for interactive, cross-platform control of plots
* makes it easy to produce static raster or vector graphics
* gives the developer complete control over the appearance of their plots, w... | github_jupyter |
```
# Let printing work the same in Python 2 and 3
from __future__ import print_function
# Turning on inline plots -- just for use in ipython notebooks.
import matplotlib
matplotlib.use('nbagg')
import numpy as np
import matplotlib.pyplot as plt
```
# Artists
Anything that can be displayed in a Figure is an [`Artist`]... | github_jupyter |
```
import pandas as pd
import warnings
warnings.filterwarnings("ignore")
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="white", color_codes=True)
iris = pd.read_csv("iris.csv") # the iris dataset is now a Pandas DataFrame
iris.head()
iris["Species"].value_counts()
# .plot extension from pandas... | github_jupyter |
# Train with Scikit-learn on AzureML
## Prerequisites
* Install the Azure Machine Learning Python SDK and create an Azure ML Workspace
```
import time
#check core SDK version
import azureml.core
print("SDK version:", azureml.core.VERSION)
# data_dir = '../../data_airline_updated'
```
## Initialize workspace
Initi... | github_jupyter |
```
import warnings
from itertools import product
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from graspy.plot import heatmap
from graspy.simulations import er_np, sbm
from graspy.utils import symmetrize
from joblib import Parallel, delayed
from scipy.stats import ttest... | github_jupyter |
# Simple Image Classifier - Bring Your Own Data
## Neuronale Netze auf https://bootcamp.codecentric.ai
Jetzt wird es Zeit, mit einem eigenen Dataset zu experimentieren.
Hinweis: Wenn du auf einem Rechner trainierst, wo keine gut GPU verfügbar ist, kann dies sehr lange dauern. Evtl. möchtest du in dem Fall das Kapite... | github_jupyter |
# Network based predictions
The state of the art in crime predication increasingly appears to be "network based". That is, looking at real street networks, and assigning risk to streets, rather than areal grid cells.
This is currently an introduction, a very brief literature review, and a plan of action.
# Literatu... | github_jupyter |
Experimenting with my hack `star_so.py`
```
#!/usr/bin/env python
# All of the argument parsing is done in the `parallel.py` module.
import numpy as np
import Starfish
from Starfish.model import ThetaParam, PhiParam
#import argparse
#parser = argparse.ArgumentParser(prog="star_so.py", description="Run Starfish fit... | github_jupyter |
# Linear Regression
<img src="https://raw.githubusercontent.com/glazec/practicalAI/master/images/logo.png" width=150>
In this lesson we will learn about linear regression. We will first understand the basic math behind it and then implement it in Python. We will also look at ways of interpreting the linear model.
# ... | github_jupyter |
```
%matplotlib notebook
import control as c
import ipywidgets as w
import numpy as np
from IPython.display import display, HTML
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.animation as animation
display(HTML('<script> $(document).ready(function() { $("div.input").hide(); })... | github_jupyter |
<a href="https://colab.research.google.com/github/kartikgill/The-GAN-Book/blob/main/Skill-07/W-GAN.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Importing useful Libraries
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as p... | github_jupyter |
<i>Copyright (c) Microsoft Corporation. All rights reserved.</i>
<i>Licensed under the MIT License.</i>
# Bayesian Personalized Ranking (BPR)
This notebook serves as an introduction to Bayesian Personalized Ranking (BPR) model for implicit feedback. In this tutorial, we focus on learning the BPR model using matrix ... | github_jupyter |
# Day 13 - Prime number factors
* https://adventofcode.com/2020/day/13
For part 1, we need to find the next multiple of a bus ID that's equal to or greater than our earliest departure time. The bus IDs, which determine their frequency, are all prime numbers, of course.
We can calculate the next bus departure $t$ fo... | github_jupyter |
<a href="https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/tensorflow-install-mac-metal-jul-2021.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# T81-558: Applications of Deep Neural Networks
**Manual Python Setu... | github_jupyter |
```
cat ratings_train.txt | head -n 10
def read_data(filename):
with open(filename, 'r') as f:
data = [line.split('\t') for line in f.read().splitlines()]
# txt 파일의 헤더(id document label)는 제외하기
data = data[1:]
return data
train_data = read_data('ratings_train.txt')
test_data = read_data(... | github_jupyter |
```
import pandas as pd
import numpy as np
import zucaml.zucaml as ml
import matplotlib.pyplot as plt
%matplotlib inline
pd.set_option('display.max_columns', None)
```
#### gold
```
df_gold = ml.get_csv('data/gold/', 'gold', [])
df_gold = df_gold.sort_values(['date', 'x', 'y', 'z'], ascending = [True, True, True,... | github_jupyter |
```
#format the book
%matplotlib inline
from __future__ import division, print_function
import sys
sys.path.insert(0, '..')
import book_format
book_format.set_style()
```
# Converting the Multivariate Equations to the Univariate Case
The multivariate Kalman filter equations do not resemble the equations for the univa... | github_jupyter |
```
import glob, sys
from IPython.display import HTML
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from astropy.io import fits
from pyflowmaps.flow import flowLCT
import warnings
warnings.filterwarnings("ignore")
```
# Load the data
We include in the folder *data/* a c... | github_jupyter |
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