code stringlengths 2.5k 150k | kind stringclasses 1
value |
|---|---|
```
###################### Query 1 #####################
from pyspark.sql import SparkSession
spark = SparkSession\
.builder\
.master('yarn-client')\
.appName("TPCH_Q1")\
.getOrCreate()
df = spark.read.format("parquet").load("/orin_tpchnp_100/lineitem")
df.createOrReplaceTempView("linei... | github_jupyter |
```
%run ../Python_files/util.py
##### read in raw data
import openpyxl
data_folder = '/home/jzh/Dropbox/Research/\
Data-driven_estimation_inverse_optimization/INRIX/Raw_data/'
# load filtered INRIX attribute table raw data
wb_INRIX = openpyxl.load_workbook(data_folder + 'filtered_INRIX_attribute_table.xlsx')
# lo... | github_jupyter |
# pipda
A framework for data piping in python
Inspired by [siuba][1], [dfply][2], [plydata][3] and [dplython][4], but with simple yet powerful APIs to mimic the `dplyr` and `tidyr` packages in python
## Installation
```shell
pip install -U pipda
```
## Usage
Checkout [plyrda][6] for more detailed usages.
### Verb... | github_jupyter |
# Visualize the best RFE conformations using cMDS plots
```
import pandas as pd
import numpy as np
import sys
sys.path.append('../..')
from helper_modules.run_or_load import *
from helper_modules.MDS import *
```
### Load protein related data
```
prot_name = 'fxa'
DIR = '../1_Download_and_prepare_protein_ensembles'
... | github_jupyter |
# Explaining random forest model predictions with Shapley values
Shapley values provide an estimate of how much any particular feature influences the model decision. When Shapley values are averaged they provide a measure of the overall influence of a feature.
Shapley values may be used across model types, and so pro... | github_jupyter |
# FloPy
## MNW2 package example
```
from __future__ import print_function
import sys
import os
import numpy as np
try:
import pandas as pd
except:
pass
# run installed version of flopy or add local path
try:
import flopy
except:
fpth = os.path.abspath(os.path.join('..', '..'))
sys.path.append(fpt... | github_jupyter |
# "You get a decision tree! And YOU get a decision tree!"
> "Oprah was so close to discovering random forests."
- comments: true
- categories: [tabular]
Our first method for training structured tabular data is to use ensembles of decision trees.
---
**Decision trees**: a decision tree asks a series of yes/no quest... | github_jupyter |
<a href="https://colab.research.google.com/github/kuriousk516/HIST4916a-Stolen_Bronzes/blob/main/Stolen_Bronzes.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Stolen Bronzes: Western Museums and Repatriation
## Introduction
>"*Walk into any Eur... | github_jupyter |
##### Copyright 2020 The OpenFermion Developers
```
```
# Introduction to OpenFermion
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://quantumai.google/openfermion/tutorials/intro_to_openfermion"><img src="https://quantumai.google/site-assets/images/buttons/quantumai_logo... | github_jupyter |
# Historical Variance
Let's see how we'd be calculating a covariance matrix of assets without the help of a factor model
```
import sys
!{sys.executable} -m pip install -r requirements.txt
import numpy as np
import pandas as pd
import time
import os
import quiz_helper
import matplotlib.pyplot as plt
%matplotlib inlin... | github_jupyter |
```
pwd
import pandas as pd
import numpy as np
df_csv= pd.read_pickle("../df_noplus/df5.pkl")
all_subjects=df_csv['COURSEID'].value_counts()
##removing any subject enrolled less than 20 times
#all_subjects=all_subjects[all_subjects>=20]
print df_csv.shape
df_csv=df_csv[df_csv["COURSEID"].isin(all_subjects.index)]
print... | github_jupyter |
# First look at our dataset
In this notebook, we will look at the necessary steps required before any
machine learning takes place. It involves:
* loading the data;
* looking at the variables in the dataset, in particular, differentiate
between numerical and categorical variables, which need different
preprocess... | github_jupyter |
### Import necessary libraries, set options
```
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import patsy
import seaborn as sns
import statsmodels.api as sm
import warnings
from statsmodels.formula.api import glm
pd.set_option('display.max_columns', 125)
warnings.filterwarnings("i... | github_jupyter |
# Indexing Dataframes
```
#a função set_index é um processo destrutivo e não mantém o index atual
#se quisermos manter o index atual, precisamos manualmente criar uma nova coluna e copiá-los para ela
#os valores
import pandas as pd
df = pd.read_csv('resources/week-1/datasets/Admission_Predict.csv', index_col=0)
df.hea... | github_jupyter |
## Preprocessing
<!-- Was used to generate: <br>
*preprocessed_data/cloud_cover_all_days_input_train_1.npy <br>
preprocessed_data/cloud_cover_all_days_input_valid_1.npy <br>
preprocessed_data/cloud_cover_all_days_output_train_1.npy <br>
preprocessed_data/cloud_cover_all_days_output_valid_1.npy* -->
```
import sys
imp... | github_jupyter |
<img src="img/python-logo-notext.svg"
style="display:block;margin:auto;width:10%"/>
<h1 style="text-align:center;">Python: Pandas Data Frames 1</h1>
<h2 style="text-align:center;">Coding Akademie München GmbH</h2>
<br/>
<div style="text-align:center;">Dr. Matthias Hölzl</div>
<div style="text-align:center;">Allait... | github_jupyter |
<a href="https://colab.research.google.com/github/AlejandroBeltranA/OCVED-ML/blob/master/OCVED_Applied_v2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Classifying remaining articles
This is the 4th of 4 scripts used in ocved.mx
This script us... | github_jupyter |
# Artificial Intelligence Nanodegree
## Voice User Interfaces
## Project: Speech Recognition with Neural Networks
---
In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
from libwallerlab.opticsalgorithms.motiondeblur import blurkernel
```
# Overview
This notebook explores a SNR vs. acquisition time analysis for strobed illumination, stop and stare, and coded illumination acquisition strategies.... | github_jupyter |
**Pix-2-Pix Model using TensorFlow and Keras**
A port of pix-2-pix model built using TensorFlow's high level `tf.keras` API.
Note: GPU is required to make this model train quickly. Otherwise it could take hours.
Original : https://www.kaggle.com/vikramtiwari/pix-2-pix-model-using-tensorflow-and-keras/notebook
## In... | github_jupyter |
<a href="https://qworld.net" target="_blank" align="left"><img src="../qworld/images/header.jpg" align="left"></a>
$ \newcommand{\bra}[1]{\langle #1|} $
$ \newcommand{\ket}[1]{|#1\rangle} $
$ \newcommand{\braket}[2]{\langle #1|#2\rangle} $
$ \newcommand{\dot}[2]{ #1 \cdot #2} $
$ \newcommand{\biginner}[2]{\left\langle... | github_jupyter |
<a href="https://colab.research.google.com/github/lucianaribeiro/filmood/blob/master/SentimentDetectionRNN.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
# Installing Tensorflow
! pip install --upgrade tensorflow
# Installing Keras
! pip insta... | github_jupyter |
```
!pip install torch torchtext
!git clone https://github.com/neubig/nn4nlp-code.git
from collections import defaultdict
import math
import time
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
N=2 #length of window on each side (so N=2 gives a total window size of 5,... | github_jupyter |
```
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_curve, roc_auc_score, precision_score, recall_score, f1_score, accuracy_score, confusion_matrix
import glob
import cv2
import random
import tensorfl... | github_jupyter |
# Supplemental Information:
> **"Clonal heterogeneity influences the fate of new adaptive mutations"**
> Ignacio Vázquez-García, Francisco Salinas, Jing Li, Andrej Fischer, Benjamin Barré, Johan Hallin, Anders Bergström, Elisa Alonso-Pérez, Jonas Warringer, Ville Mustonen, Gianni Liti
## Figure 3 (+ Supp. Figs.)
Th... | github_jupyter |
# The Constellation Wizard requires a STK Scenario to be open
Simply run the cell below and the constelation wizard will appear
```
from tkinter import Tk
from tkinter.ttk import *
from tkinter import W
from tkinter import E
from tkinter import scrolledtext
from tkinter import INSERT
from tkinter import END
from tkin... | github_jupyter |
```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
print("pandas", pd.__version__)
print("numpy",np.__version__)
```
# Cookbook
This is a repository for *short and sweet* examples and links for useful pandas recipes.
We encourage users to add to this documentation.
Adding interesting links an... | github_jupyter |
## Using low dimensional embeddings to discover subtypes of breast cancer
This notebook is largely based on https://towardsdatascience.com/reduce-dimensions-for-single-cell-4224778a2d67 (credit to Nikolay Oskolkov).
https://www.nature.com/articles/s41467-018-07582-3#data-availability
```
import pandas as pd
import n... | github_jupyter |
```
import hoomd
import hoomd.hpmc
import ex_render
import math
from matplotlib import pyplot
import numpy
%matplotlib inline
```
# Selecting move sizes
HPMC allows you to set the translation and rotation move sizes. Set the move size too small and almost all trial moves are accepted, but it takes many time steps to ... | github_jupyter |
# Modeling: Aspect-Based Sentiment Analysis
## BerTweet
Oversampling as a solution to the imabalance still wasn't enough to raise the model's performance significantly. This was especially the case because the validation and test sets were very small and still imbalanced (plus, we can't resample these!). Thus, my next... | github_jupyter |
## VISUALIZING YOUR FAVOURITE NBA PLAYER 3 POINTERS GRAPH
Tools we are going to use:
- The NBA API to get the data from any NBA player
- CARTOframes to upload the data seamlessly to CARTO
- The CARTO Python SDK to analyze and create a 3-pointers map
- carto-print to generate a high resolution ready-to-print image
##... | github_jupyter |
## Prediction sine wave function using Gaussian Process
An example for Gaussian process algorithm to predict sine wave function.
This example is from ["Gaussian Processes regression: basic introductory example"](http://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gp_regression.html).
```
import numpy a... | github_jupyter |
Clustering Mash distances to obtain clonal groups for all Salmonella
```
library('FactoMineR')
library('factoextra')
library('readxl')
library('dplyr')
mash_dist_file = '../data/interim/mash_distance_matrix.csv'
meta_excel_file = '../data/raw/GenotypicAMR_Master.xlsx'
distances <- read.csv(mash_dist_file, header=TRUE,... | github_jupyter |
# Convolutional Neural Networks
---
In this notebook, we train a **CNN** to classify images from the CIFAR-10 database.
The images in this database are small color images that fall into one of ten classes; some example images are pictured below.
<img src='notebook_ims/cifar_data.png' width=70% height=70% />
### Test... | github_jupyter |
# Deep Convolutional Generative Adversarial Networks
:label:`sec_dcgan`
In :numref:`sec_basic_gan`, we introduced the basic ideas behind how GANs work. We showed that they can draw samples from some simple, easy-to-sample distribution, like a uniform or normal distribution, and transform them into samples that appear ... | github_jupyter |
### Convolutional autoencoder
Since our inputs are images, it makes sense to use convolutional neural networks (convnets) as encoders and decoders. In practical settings, autoencoders applied to images are always convolutional autoencoders --they simply perform much better.
Let's implement one. The encoder will consi... | github_jupyter |
```
import sys
import os
import glob
import subprocess as sp
import multiprocessing as mp
import pandas as pd
import numpy as np
from basic_tools import *
debug=False
def run_ldsc(pheno_code,ld,output,mode='original',samp_prev=np.nan,pop_prev=np.nan):
if os.path.exists(ldsc_path.format(pheno_code)+'.log'):
... | github_jupyter |
# anesthetic plot gallery
This functions as both some examples of plots that can be produced, and a tutorial.
Any difficulties/issues/requests should be posted as a [GitHub issue](https://github.com/williamjameshandley/anesthetic/issues)
## Download example data
Download some example data from github (or alternativ... | github_jupyter |
```
%matplotlib inline
```
Sequence-to-Sequence Modeling with nn.Transformer and TorchText
===============================================================
This is a tutorial on how to train a sequence-to-sequence model
that uses the
`nn.Transformer <https://pytorch.org/docs/master/nn.html?highlight=nn%20transformer#... | github_jupyter |
---
_You are currently looking at **version 1.0** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-data-analysis/resources/0dhYG) course resource._
---
# The Series Data Str... | github_jupyter |
```
%matplotlib inline
```
This notebook is based on:
https://mne.tools/stable/auto_tutorials/stats-sensor-space/75_cluster_ftest_spatiotemporal.html
# Spatiotemporal permutation F-test on full sensor data
Tests for differential evoked responses in at least
one condition using a permutation clustering test.
The Fie... | github_jupyter |
```
import os
os.environ['PYSPARK_SUBMIT_ARGS'] = \
'--conf spark.cassandra.connection.host=cassandra --packages org.apache.spark:spark-streaming-kafka-0-8_2.11:2.0.2,com.datastax.spark:spark-cassandra-connector_2.11:2.0.2 pyspark-shell'
from pyspark import SparkContext
from pyspark.streaming import StreamingContext
... | github_jupyter |
```
import os
import xgboost as xgb
import pandas as pd
import numpy as np
from utils import encode_numeric_zscore_list, encode_numeric_zscore_all, to_xy, encode_text_index_list, encode_numeric_log_all
from xgboost.sklearn import XGBClassifier, XGBRegressor
from sklearn import datasets
from sigopt_sklearn.search import... | github_jupyter |
<a href="https://colab.research.google.com/github/rtindru/CompStats/blob/master/Kensho_Assessment_Model.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
import matplotlib.pyplot as plt
plt.rcParams["figure.f... | github_jupyter |
<h1 id="header-ch">2021 CCF BDCI基于飞桨实现花样滑冰选手骨骼点动作识别-第6名方案</h1>
# 赛题介绍
人体运动分析是近几年许多领域研究的热点问题。在学科的交叉研究上，人体运动分析涉及到计算机科学、运动人体科学、环境行为学和材料科学等。随着研究的深入以及计算机视觉、5G通信的飞速发展，人体运动分析技术已应用于自动驾驶、影视创作、安防异常事件监测和体育竞技分析、康复等实际场景人体运动分析已成为人工智能领域研究的前沿课题。目前的研究数据普遍缺少细粒度语义信息，导致现存的分割或识别任务缺少时空细粒度动作语义模型。此类研究在竞技体育、运动康复、日常健身等方面有非常重大的意义。相比于图片的细粒度研究，时... | github_jupyter |
# Plus proches voisins - évaluation
Comment évaluer la pertinence d'un modèle des plus proches voisins.
```
%matplotlib inline
from papierstat.datasets import load_wines_dataset
df = load_wines_dataset()
X = df.drop(['quality', 'color'], axis=1)
y = df['quality']
from sklearn.neighbors import KNeighborsRegressor
knn... | github_jupyter |
# Introduction to the scikit-learn -- supervised learning and model selection (part 3)
- toc: true
- badges: true
- categories: [EEG, jupyter]
- description: To visualize the workings of machine learning algorithms, it is often helpful to study two-dimensional or one-dimensional data, that is data with only one or tw... | 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 |
<center> <font size=5> <h1>Define working environment</h1> </font> </center>
The following cells are used to:
- Import needed libraries
- Set the environment variables for Python, Anaconda, GRASS GIS and R statistical computing
- Define the ["GRASSDATA" folder](https://grass.osgeo.org/grass73/manuals/helptext.html),... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as scy
inds = np.arange(0, 50, 0.001)
capacity = 20
y = 1/ (1 + np.exp( -0.1 * (inds - capacity / 2)) )
y[inds < 5] = 0
plt.plot(inds, y, label='Juveniles')
y1 = y
y1[inds >= 5] = 0.01
plt.plot(inds, y1, '-.', label='Adults')
plt.text(20, 0.03... | github_jupyter |
```
%load_ext itikz
import itikz
from itikz import nicematrix as nM
import jinja2
import numpy as np
import sympy as sym
import panel as pn
pn.extension()
## Invoke itikz without using cell magic
# itikz.build_commands?
# itikz.fetch_or_compile_svg?
```
# 1. Examples from the Original Itikz Notebook
```
%%... | github_jupyter |
# Introducción a Python: Sintaxis, Funciones y Booleanos
<img style="float: right; margin: 0px 0px 15px 15px;" src="https://www.python.org/static/community_logos/python-logo.png" width="200px" height="200px" />
> Bueno, ya que sabemos qué es Python, y que ya tenemos las herramientas para trabajarlo, veremos cómo usar... | github_jupyter |
```
import numpy as np
import random
import pandas as pd
import sklearn
from matplotlib import pyplot as plt
plt.rcParams['figure.figsize'] = (10.0, 8.0)
from sklearn.datasets import make_biclusters
from sklearn.datasets import samples_generator as sg
from sklearn.datasets import fetch_20newsgroups
from sklearn.featu... | github_jupyter |
## Assigning gender based on first name
A straightforward task in natural language processing is to assign gender based on first name. Social scientists are often interested in gender inequalities and may have a dataset that lists name but not gender, such as a list of journal articles with authors in a study of gende... | github_jupyter |
version 1.0.3
# + 
# **Text Analysis and Entity Resolution**
####Entity resolution is a common, yet difficult problem in data clea... | github_jupyter |
# Representación y visualización de datos
El aprendizaje automático trata de ajustar modelos a los datos; por esta razón, empezaremos discutiendo como los datos pueden ser representados para ser accesibles por el ordenador. Además de esto, nos basaremos en los ejemplos de matplotlib de la sección anterior para usarlos... | github_jupyter |
# Day 1
```
from sklearn.datasets import load_iris
import pandas as pd
import numpy as np
iris = load_iris()
df = pd.DataFrame(np.c_[iris['data'], iris['target']], columns = iris['feature_names'] + ['species'])
df['species'] = df['species'].replace([0,1,2], iris.target_names)
df.head()
import numpy as np
import matpl... | github_jupyter |
```
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
df = pd.read_csv('credit.csv')
df.head()
df.shape
df.info()
df.isnull().sum()
df.duplicated().sum()
df.corr()
df['default'].v... | github_jupyter |
# Table of Contents
<p><div class="lev1 toc-item"><a href="#Simulated-annealing-in-Python" data-toc-modified-id="Simulated-annealing-in-Python-1"><span class="toc-item-num">1 </span>Simulated annealing in Python</a></div><div class="lev2 toc-item"><a href="#References" data-toc-modified-id="References-11"><... | github_jupyter |
```
import numpy as np
import pandas as pd
# stats
from scipy import stats
# Plotting
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import math
%matplotlib inline
_df4 = pd.read_csv('winequality-red.csv',sep=";")
_df4
# _df4.head()
```
# Basics of MatPlotLib
# Pylab interface, whe... | github_jupyter |
## "FAQ-Style QA": Utilizing existing FAQs for Question Answering
While *extractive Question Answering* works on pure texts and is therefore more generalizable, there's also a common alternative that utilizes existing FAQ data.
Pros:
- Very fast at inference time
- Utilize existing FAQ data
- Quite good control over ... | github_jupyter |
## **Variables and Data Types**
**Topics Covered**
> Creating Variable
> DataTypes
> None Keyword
> Multi Line statement and Multi Comment
-----
### Creating a Variable
* Variables are used to store values. In Python you don't have to declare a varaible.
* Variable is created the moment you ... | github_jupyter |
# Writing a Device driver
### Basic structure
Here is a simple (but complete and functional) code block that implements a VISA driver for a power sensor:
```
import labbench as lb
import pandas as pd
# Specific driver definitions are implemented by subclassing classes like lb.VISADevice
class PowerSensor(lb.VISADevic... | github_jupyter |
# Computation on Arrays: Broadcasting
We saw in the previous section how NumPy's universal functions can be used to *vectorize* operations and thereby remove slow Python loops.
Another means of vectorizing operations is to use NumPy's *broadcasting* functionality.
Broadcasting is simply a set of rules for applying bin... | github_jupyter |
*Note: This is not yet ready, but shows the direction I'm leaning in for Fourth Edition Search.*
# State-Space Search
This notebook describes several state-space search algorithms, and how they can be used to solve a variety of problems. We start with a simple algorithm and a simple domain: finding a route from city ... | github_jupyter |
# Hyper parameters
The goal here is to demonstrate how to optimise hyper-parameters of various models
The kernel is a short version of https://www.kaggle.com/mlisovyi/featureengineering-basic-model
```
max_events = None
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.r... | github_jupyter |
```
#hide
#default_exp clean
from nbdev.showdoc import show_doc
#export
import io,sys,json,glob,re
from fastcore.script import call_parse,Param,bool_arg
from fastcore.utils import ifnone
from nbdev.imports import Config
from nbdev.export import nbglob
from pathlib import Path
#hide
#For tests only
from nbdev.imports im... | github_jupyter |
# openCV Configure for Raspberry PI
What is openCV?
* Collection of computer vision tools in one place
* Computational photography to object detection
Where is openCV?
* http://opencv.org/
What resources did I use?
* http://www.pyimagesearch.com/2016/04/18/install-guide-raspberry-pi-3-raspbian-jessie-opencv-3/
* htt... | github_jupyter |
```
%tensorflow_version 1.x
import numpy as np
import pandas as pd
import sklearn
import sklearn.metrics
from sklearn import tree
from matplotlib import pyplot as plt
%load_ext autoreload
%autoreload 2
import torch
from torch.autograd import Variable as V
import torchvision.models as models
from torchvision import tr... | github_jupyter |
### Load SEM image
Import packages
```
from PIL import Image
import numpy as np
import time
import matplotlib.pyplot as plt
import cv2
import copy
# from skimage import io
# from skimage.io import imread, imshow
# from skimage.filters import threshold_otsu
# from skimage import color
# from skimage.color import label... | github_jupyter |
# Materialien zu <i>zufall</i>
Autor: Holger Böttcher - hbomat@posteo.de
## Aufgaben 13 - Simulation (Probleme von Leibniz <br>und de Méré)
<br>
### Problem von Leibniz
Leibniz nahm fälschlicherweise an, dass beim Werfen von 2 Würfeln die Augensumme<br>
11 genau so oft auftritt wie die Augensumme 12<br>
```
%run zu... | github_jupyter |
<img src="data/photutils_banner.svg">
## Photutils
- Code: https://github.com/astropy/photutils
- Documentation: http://photutils.readthedocs.org/en/stable/
- Issue Tracker: https://github.com/astropy/photutils/issues
## Photutils Overview
- Background and background noise estimation
- Source Detection and Extract... | github_jupyter |
# Extracting condtion-specific trials
The aim of this section is to extract the trials according to the trigger channel. We will explain how the events can be generated from the stimulus channels and how to extract condition specific trials (epochs). Once the trials are extracted, bad epochs will be identified and exc... | github_jupyter |
# Parte 3 - Machine Learning Workflow
Datasets: [Diamanti](https://www.kaggle.com/shivam2503/diamonds)
**OBBIETTVO:** In base alle sue caratteristiche provare a predire il prezzo di un diamante <br>
Utilizzeremo la libreria python **scikit-learn** per testare alcuni algoritmi di classificiazione!
```
import pandas a... | github_jupyter |
# cadCAD Tutorials: The Robot and the Marbles, part 3
In parts [1](../robot-marbles-part-1/robot-marbles-part-1.ipynb) and [2](../robot-marbles-part-2/robot-marbles-part-2.ipynb) we introduced the 'language' in which a system must be described in order for it to be interpretable by cadCAD and some of the basic concepts... | github_jupyter |
```
import numpy as np
import pandas as pd
import os
from pathlib import Path
import selfies as sf
from rdkit import Chem
import pandas as pd
```
# Molecule retrieval from Zinc20 smi files
```
Is_data_prepared = True
if not Is_data_prepared:
tranche_dirs = ['FK', 'DC', 'BB', 'JA', 'HE', 'GA', 'KG', 'IC', 'CB... | github_jupyter |
# Python:
## basic features
https://www.python.org/
```
print("Hello, World!")
a = 5
b = 2
a + b
1 + a * b
a ** b
# different in python 3: a//b
# for same behaviour run: from __future__ import division
a / b
a / float(b)
a % b
min(a, b)
a == b
a != b
a += 3
a
# Python Lists
a = [1, "hello", 5.5]
a
len(a)
a[2]
a.appe... | github_jupyter |
# Compare tangential shear profiles from the extragalactic and object catalogs for DC2 Run 2.1i
This notebook can be run at NERSC or CC-IN2P3 where the DESC DC2 products are stored. You need to be a DESC member to be able to access those. The DC2 catalog-related imports below (`FoFCatalogMatching`, `GCR` and `GCRCatal... | github_jupyter |
# User Study
```
import pandas as pd
import numpy as np
import math
import time
eval_dir = "gc_imdb"
# df = pd.read_csv("../data/" + eval_dir + "/test.csv", header=None, sep="\t", names=[0, 1, "mutant", "template", "gender", "label", "country"])
df = pd.read_csv("../data/" + eval_dir + "/test.csv", header=None, sep="\... | github_jupyter |
# SageMaker Debugger Profiling Report
SageMaker Debugger auto generated this report. You can generate similar reports on all supported training jobs. The report provides summary of training job, system resource usage statistics, framework metrics, rules summary, and detailed analysis from each rule. The graphs and tab... | github_jupyter |
```
import pandas as pd
import numpy as np
data = pd.Series(np.random.randn(9),
index=[['a', 'a', 'a', 'b', 'b', 'c', 'c', 'd', 'd'],
[1, 2, 3, 1, 3, 1, 2, 2, 3]])
data
data.index
data['b']
data['b':'c']
data.loc[['b','d']]
data.loc[:,2]
data.unstack()
data.unstack().stack()
fra... | github_jupyter |
```
import numpy as np
import regex as re
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import statistics
import math
import os
import keras.backend as K
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_t... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
```
## Introduction
Because of the relational structure in a graph,
we can begin to think about "importance" of a node
that is induced because of its relationships
to the rest of the nodes in the graph.
Before we... | github_jupyter |
# Tutorial 08: Creating Custom Environments 创建自定义环境
This tutorial walks you through the process of creating custom environments in Flow. Custom environments contain specific methods that define the problem space of a task, such as the state and action spaces of the RL agent and the signal (or reward) that the RL algor... | github_jupyter |
# Description : This is a emotion analysis program that parses the tweets fetched from Twitter using Python
```
# import libraries
import tweepy
from textblob import TextBlob
from wordcloud import WordCloud
import pandas as pd
import numpy as np
import re
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight... | github_jupyter |
```
!git clone https://github.com/parhamzm/Beijing-Pollution-DataSet
!ls Beijing-Pollution-DataSet
import torch
import torchvision
import torch.nn as nn
from torchvision import transforms
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from torch.utils.data import random_split
from math import... | github_jupyter |
# Treasure Hunt Game Notebook
## Read and Review Your Starter Code
The theme of this project is a popular treasure hunt game in which the player needs to find the treasure before the pirate does. While you will not be developing the entire game, you will write the part of the game that represents the intelligent agent... | github_jupyter |
```
from google.colab import drive
drive.mount('/content/drive')
cd drive/My Drive/google_colab_gpu/GSOC 2020/CERN-HSF
ls
#import cv2
import numpy as np
import pandas as pd
#from google.colab.patches import cv2_imshow
import h5py
#import numpy as np
#import matplotlib.pyplot as plt
import pandas as pd
import warnings
w... | github_jupyter |
# Introdcution
This trial describes how to create edge and screw dislocations in iron BCC strating with one unitcell containing two atoms
## Background
The elastic solution for displacement field of dislocations is provided in the paper [Dislocation Displacement Fields in Anisotropic Media](https://doi.org/10.1063/1... | github_jupyter |
# Rolling Update Tests
Check rolling updates function as expected.
```
import json
import time
!kubectl create namespace seldon
!kubectl config set-context $(kubectl config current-context) --namespace=seldon
```
## Change Image
```
!kubectl apply -f resources/fixed_v1.yaml
!kubectl rollout status deploy/$(kubectl ... | github_jupyter |
# Hypothesis and Inference
In this chapter, we test hypotheses. Firstly, let's test the hypothesis that a series of coin flips will be fair. It also build upon previous functions found in earlier chapters.
### Assumptions:
1. each flip is a Bernoulli trial, meaning that `X` a binomial `(n,p)` random variable.
2. `... | github_jupyter |
# Homework 8
## Due Date: Tuesday, October 31st at 11:59 PM
# Problem 1: BST Traversal
This problem builds on Problem 1 of Homework 7 in which you wrote a binary search tree.
### Part 1
As discussed in lecture, three different types to do a depth-first traversal are: preorder, inorder, and postorder. Here is a ref... | github_jupyter |
# Managing pins
```
%load_ext autoreload
%autoreload 2
import qiskit_metal as metal
from qiskit_metal import designs, draw
from qiskit_metal import MetalGUI, Dict, Headings
Headings.h1('Welcome to Qiskit Metal')
design = designs.DesignPlanar()
gui = MetalGUI(design)
```
First we create some transmon pockets to have a... | github_jupyter |
# Deep Convolutional Neural Networks
In this assignment, we will be using the Keras library to build, train, and evaluate some *relatively simple* Convolutional Neural Networks to demonstrate how adding layers to a network can improve accuracy, yet are more computationally expensive.
The purpose of this assignment ... | 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 |
# Various Routines to Harvest CRIM Metadata from Production Server
### Just the basics here, allowing interaction with "request" as a way to retrieve individual Observations and Relationships
```
import requests
import pandas as pd
```
# Variables
Now we can set a variable, in this case the URL of a single Observati... | github_jupyter |
#Sheet Copy
Copy tab from a sheet to a sheet.
#License
Copyright 2020 Google LLC,
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
https://www.apache.org/licenses/LICENSE-2.0
Unless required by... | github_jupyter |
# 数学函数、字符串和对象
## 本章介绍Python函数来执行常见的数学运算
- 函数是完成一个特殊任务的一组语句，可以理解为一个函数相当于一个小功能，但是在开发中，需要注意一个函数的长度最好不要超过一屏
- Python中的内置函数是不需要Import导入的
<img src="../Photo/15.png"></img>
```
#绝对值
print(abs(-10))
#MAX
max(1,2,3)
#max('abc')
#MIN
min(-1,0,1)
#POW（幂）
pow(5,8)
#ROUND（X）(返回与X最接近的整数)
round(3.8)
#round(x,n) 保留浮点小数
round(3.99999... | github_jupyter |
<a href="https://colab.research.google.com/github/spyrosviz/Injury_Prediction_MidLong_Distance_Runners/blob/main/ML%20models/Models_Runners_Injury_Prediction.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
# Import Libraries
import pandas as pd... | github_jupyter |
# Esercitazione 1
## Esercizio
### Analisi del segnale
Apriamo il segnale da analizzare con [Audacity](https://www.audacityteam.org/).
Ascoltandolo possiamo chiaramente riconoscere una sequenza di tasti premuti su un tastierino telefonico, anche conosciuto come [DTMF](https://en.wikipedia.org/wiki/Dual-tone_multi-fr... | github_jupyter |
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