text stringlengths 2.5k 6.39M | kind stringclasses 3
values |
|---|---|
```
import pandas as pd
import matplotlib.pyplot as plt
import xlrd
import os
```
打开excel文件并获取sheet数量及各sheet的名称
```
path = 'C:\\Users\\Z0050908\\Documents\\Jupyter_scipt\\group5\\group5\\Result analysis\\Original.XLS'
# df = pd.read_excel(path)
data = xlrd.open_workbook(path)
count = len(data.sheets())
sheet_name = [... | github_jupyter |
# Fine-Tuning a BERT Model and Create a Text Classifier
We have already performed the Feature Engineering to create BERT embeddings from the `reviews_body` text using the pre-trained BERT model, and split the dataset into train, validation and test files. To optimize for Tensorflow training, we saved the files in TFRe... | github_jupyter |
```
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
df = pd.read_csv("/data/iris.csv")
df.head()
features = ["SepalLengthCm", "PetalLengthCm"]
df.Species.value_counts()
fig, ax = plt.subplots()
colors = ["red", "green", "blue"]
for i, v in enumerate(df.Species.unique()):
... | github_jupyter |
##### Copyright 2018 Google LLC.
Licensed under the Apache License, Version 2.0 (the "License");
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... | github_jupyter |
### Preprocessing
```
# import relevant statistical packages
import numpy as np
import pandas as pd
# import relevant data visualisation packages
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
# load Default dataset
url = "/Users/arpanganguli/Documents/Professional/Finance/ISLR/Datasets/Defau... | github_jupyter |
```
import pandas as pd
import numpy as np
import scipy.stats
import matplotlib.pyplot as plt
import tensorflow as tf
import statistics as stats
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing ... | github_jupyter |
# Laboratorio 3.1
*Elaborado por Oscar Franco-Bedoya*
*`Proyecto Mision TIC 2021*
## Objetivo
Aplicar el concepto de modulos mediante la impementación de programas que utilizan librerias de Python como math y random.
## La calculadora de Trigo
### Contexto
El profesor de matemáticas del colegio de la esquina ha de... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import math
import cv2
from skimage import io, color, exposure, feature, filters, util, measure
from skimage import img_as_ubyte
from skimage import img_as_float
from skimage.filters import threshold_otsu
from skimage.draw import ellipse_perimeter
from skimage.dr... | github_jupyter |
### Problem Statement
Given a linked list with integer data, arrange the elements in such a manner that all nodes with even numbers are placed after odd numbers. **Do not create any new nodes and avoid using any other data structure. The relative order of even and odd elements must not change.**
**Example:**
* `link... | github_jupyter |
# PYNQ tutorial: DMA to streamed interfaces
Overlay consists of two DMAs and an AXI Stream FIFO (input and output AXI stream interfaces). The FIFO represents an accelerator. A single DMA could be used with a read and write channel enabled, but for demonstration purposes, two different DMAs will be used.
* The first ... | github_jupyter |
# Machine Learning Engineer Nanodegree
## Reinforcement Learning
## Project: Train a Smartcab to Drive
Welcome to the fourth project of the Machine Learning Engineer Nanodegree! In this notebook, template code has already been provided for you to aid in your analysis of the *Smartcab* and your implemented learning alg... | github_jupyter |
# Using Automated Machine Learning
There are many kinds of machine learning algorithm that you can use to train a model, and sometimes it's not easy to determine the most effective algorithm for your particular data and prediction requirements. Additionally, you can significantly affect the predictive performance of a... | github_jupyter |
# Tenor functions with Pytorch
### Torch up your tensor game
The following 5 functions might empower you to navigate through your Deep Learning endeavours with Pytorch
- torch.diag()
- torch.inverse()
- torch.randn()
- torch.zeros_like()
- torch.arange()
```
# Import torch and other required modules
import torch
``... | github_jupyter |
# Specific examples of transitions data
# 0. Import dependencies and inputs
```
%run ../../notebook_preamble_Transitions.ipy
# Location to store transitions data
outputs_folder = f'{useful_paths.data_dir}processed/transitions/specific_examples/'
# File name to use for the specific examples
file_name = 'Data_example'... | 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 numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import datetime
from datetime import datetime
from sklearn.metrics import mean_squared_error
%matplotlib inline
plt.style.use('fivethirtyeight') #Used for replicating graph styles from fivethirtyeight.com
from k... | github_jupyter |
<!--NOTEBOOK_HEADER-->
*This notebook contains material from [cbe61622](https://jckantor.github.io/cbe61622);
content is available [on Github](https://github.com/jckantor/cbe61622.git).*
<!--NAVIGATION-->
< [A.2 Downloading Python source files from github](https://jckantor.github.io/cbe61622/A.02-Downloading_Python_so... | github_jupyter |
# Tutorial 05: Creating Custom Networks
This tutorial walks you through the process of generating custom networks. Networks define the network geometry of a task, as well as the constituents of the network, e.g. vehicles, traffic lights, etc... Various networks are available in Flow, depicting a diverse set of open an... | github_jupyter |
TSG033 - Show BDC SQL status
============================
Steps
-----
### Common functions
Define helper functions used in this notebook.
```
# Define `run` function for transient fault handling, hyperlinked suggestions, and scrolling updates on Windows
import sys
import os
import re
import json
import platform
imp... | github_jupyter |
# Predicting Marketing Efforts: SEO Advertising, Brand Advertising, and Retailer Support
Let's look at predicting the average Brand Advertising Efforts and Search Engine Optimization Efforts
This helps us make more accurate decisions in BSG and identify if we'll hit the shareholder expectations for the period.
```
#... | github_jupyter |
### Een parser-generator voor de wordgrammar
In de ETCBC-data wordt een morphologische analyse-annotatie gebruikt, die per project kan worden gedefinieerd in een `word_grammar`-definitiebestand. Per project moet er eerst een annotatieparser worden gegenereerd aan de hand van het `word-grammar`-bestand. Dat gebeurt in ... | github_jupyter |
最初に必要なライブラリを読み込みます。
```
from sympy import *
from sympy.physics.quantum import *
from sympy.physics.quantum.qubit import Qubit, QubitBra, measure_all, measure_all_oneshot
from sympy.physics.quantum.gate import H,X,Y,Z,S,T,CPHASE,CNOT,SWAP,UGate,CGateS,gate_simp
from sympy.physics.quantum.gate import IdentityGate as _I
... | github_jupyter |
```
import numpy as np
import torch
import math
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import GPyOpt
import GPy
import os
import matplotlib as mpl
import matplotlib.tri as tri
import ternary
import pickle
import datetime
from collections import Counter
import matplot... | github_jupyter |
## Introduction to Pandas
Pandas is a newer package built on top of NumPy, and provides an
efficient implementation of a DataFrame . DataFrame s are essentially multidimen‐
sional arrays with attached row and column labels, and often with heterogeneous
types and/or missing data. As well as offering a convenient storag... | github_jupyter |
# Project 2: Continuous Control
### Test 2 - PPO model
<sub>Uirá Caiado. October 15, 2018<sub>
#### Abstract
_In this notebook, I will use the Unity ML-Agents environment to train a PPO model for the second project of the [Deep Reinforcement Learning Nanodegree](https://www.udacity.com/course/deep-reinforcement-lear... | github_jupyter |
# The Laplace Transform
*This Jupyter notebook is part of a [collection of notebooks](../index.ipynb) in the bachelors module Signals and Systems, Communications Engineering, Universität Rostock. Please direct questions and suggestions to [Sascha.Spors@uni-rostock.de](mailto:Sascha.Spors@uni-rostock.de).*
## Inverse ... | github_jupyter |
```
%matplotlib inline
%config InlineBackend.figure_formats = {'png', 'retina'}
data_key = pd.read_csv('key.csv')
data_key = data_key[data_key['station_nbr'] != 5]
data_weather = pd.read_csv('weather.csv')
data_weather = data_weather[data_weather['station_nbr'] != 5] ## Station 5번 제거한 나머지
data_train = pd.read_csv('tra... | github_jupyter |
# ElasticSearch DSL
Librería de alto nivel que ayuda a escribir y ejecutar consultas en Elastic.
Proporciona una API más idiomática y pythonica para manipular y componer consultas.
Proporciona una capa para trabajar con los documentos, definiendo el mapping, rescatar, actualizar, guardar documentos, usando orientació... | github_jupyter |
## ResFPN Classifier Tutorial - Flower Photos
by *Ming Ming Zhang*
```
import tensorflow as tf
print('TF Version:', tf.__version__)
#print('GPUs:', len(tf.config.list_physical_devices('GPU')))
import numpy as np
import matplotlib.pyplot as plt
import os, sys
# python files directory
PY_DIR = #'directory/to/python/fi... | github_jupyter |
# **BentoML Example: Image Segmentation with PaddleHub**
**BentoML makes moving trained ML models to production easy:**
* Package models trained with any ML framework and reproduce them for model serving in production
* **Deploy anywhere** for online API serving or offline batch serving
* High-Performance API mode... | github_jupyter |
# Hyperparameter tuning
## Spark
<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/f/f3/Apache_Spark_logo.svg/1280px-Apache_Spark_logo.svg.png" width="400">
# Load data and feature engineering
```
import numpy as np
import datetime
import findspark
findspark.init()
from pyspark.sql import SparkSession... | github_jupyter |
<a href="https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/manual_setup.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 Setup**
* Instructor: [Jeff H... | github_jupyter |
```
from os import listdir
from keras.models import model_from_json
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from nltk.translate.bleu_score import sentence_bleu
from tqdm import tqdm
import numpy as np
import h5py as h5py
from compiler.classes.Compiler import... | github_jupyter |
<a href="https://colab.research.google.com/github/pachterlab/CBP_2021/blob/main/notebooks/VMHNeurons/kimetal_smartseq_predictions.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
import requests
import os
from tqdm import tnrange, tqdm_notebook... | github_jupyter |
# Ax Service API with RayTune on PyTorch CNN
Ax integrates easily with different scheduling frameworks and distributed training frameworks. In this example, Ax-driven optimization is executed in a distributed fashion using [RayTune](https://ray.readthedocs.io/en/latest/tune.html).
RayTune is a scalable framework for... | github_jupyter |
```
#code is used from these 3 repositories, have a look at them on GitHub or access the files from colab
!git clone https://github.com/PeterWang512/FALdetector
!git clone https://github.com/NVIDIA/flownet2-pytorch.git
!git clone https://github.com/Kwanss/PCLNet
#import necessary modules and append paths
import rando... | github_jupyter |
## Data Centric ML Development using Snowflake and Amazon SageMaker
This notebook guides you through a Data Centric machine learning (ML) development process using Snowflake and Amazon SageMaker. We demonstrate the use case through a credit-risk analysis use case.
**What you will learn:**
* How to use the Snowflake ... | github_jupyter |
**[Pandas Home Page](https://www.kaggle.com/learn/pandas)**
---
# Introduction
Run the following cell to load your data and some utility functions.
```
from learntools.core import binder; binder.bind(globals())
from learntools.pandas.renaming_and_combining import *
print("Setup complete.")
import pandas as pd
revi... | github_jupyter |
# Shifting Previous Week Stats to Predict Current Week Performance
Author: Aidan O'Connor
Date: 15 June 2021
In this notebook, I'll take previous week stats and shift them to current week predictions.
```
# Import pandas for data manipulation and sqlite3 for stored data access
import pandas as pd
import sqlite3... | github_jupyter |
# Obsessed with Boba? Analyzing Bubble Tea Shops in NYC Using the Yelp Fusion API
Exploratory Data Analysis
```
# # imports for Google Colab Sessions
# !apt install gdal-bin python-gdal python3-gdal
# # Install rtree - Geopandas requirment
# !apt install python3-rtree
# # Install Geopandas
# !pip install git+git://g... | github_jupyter |
# Convolutional Neural Networks: Step by Step
Welcome to Course 4's first assignment! In this assignment, you will implement convolutional (CONV) and pooling (POOL) layers in numpy, including both forward propagation and (optionally) backward propagation.
**Notation**:
- Superscript $[l]$ denotes an object of the $l... | github_jupyter |
<a href="https://colab.research.google.com/github/GiselaCS/Mujeres_Digitales/blob/main/KNN_(Ejemplo_1).ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Cargamos todas las librerías necesarias. Utilizaremos la clase KNeighborsClassifier, para poder us... | github_jupyter |
# 1: Palindrome 1
```
palindrome_answer = "abcdefghijklmnopqrstuvwxyzyxwvutsrqponmlkjihgfedcba"
def basic_palindrome():
letter = 'a'
output_string = ""
# chr() converts a numeric value to a character and ord() converts a character to a numeric value
# This allows us to arithmetically change the va... | github_jupyter |
# Content:
1. [Simple example](#1.-Simple-example)
2. [Parametric equations](#2.-Parametric-equations)
3. [Polishing the plot](#3.-Polishing-the-plot)
4. [Contour plot](#4.-Contour-plot)
5. [Beginner-level animation](#5.-Beginner-level-animation)
6. [Intermediate-level animation](#6.-Intermediate-level-animation)
## 1... | github_jupyter |
# Least Squares Regression for Impedance Analysis
## Introduction
This is a tutorial for how to set up the functions and calls for curve fitting
an experimental impedance spectrum with Python using a least squares
regression. Four different models are used as examples for how to set up the
curv... | github_jupyter |
```
import os
from tensorflow.keras import layers
from tensorflow.keras import Model
!wget --no-check-certificate \
https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5 \
-O /tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5
from tensorflow.keras.... | github_jupyter |
```
%matplotlib inline
import ipywidgets as widgets
from ipywidgets import interact
import numpy as np
import matplotlib.pyplot as pl
from scipy.spatial.distance import cdist
from numpy.linalg import inv
import george
```
# Gaussian process regression
## Lecture 1
### Suzanne Aigrain, University of Oxford
#### LSS... | github_jupyter |
# `stedsans`
This is a notebook showing the current and most prominent capabilities of `stedsans`.
It is heavily recommended to run the notebook by using Google Colab:
<br>
<br>
[](https://colab.research.google.com/github/MalteHB/stedsans/blob/... | github_jupyter |
```
import sys
sys.path.append('src/')
import numpy as np
import torch, torch.nn
from library_function import library_1D
from neural_net import LinNetwork
from DeepMod import *
import matplotlib.pyplot as plt
plt.style.use('seaborn-notebook')
import torch.nn as nn
from torch.autograd import grad
from scipy.io import lo... | github_jupyter |
## 1. Import basic libraries
```
import pandas as pd
import pandas_profiling
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import matplotlib.ticker as mtick
```
## 2. Read final.csv
```
data = pd.read_csv('Data/final.csv')
```
## 3. And now let's carefully analyse the d... | github_jupyter |
# Extreme Gradient Boosting Regressor
### Required Packages
```
!pip install xgboost
import warnings
import numpy as np
import pandas as pd
import seaborn as se
import xgboost as xgb
import matplotlib.pyplot as plt
from xgboost import XGBRegressor
from sklearn.model_selection import train_test_split
from s... | github_jupyter |
# Characterization of Systems in the Time Domain
*This Jupyter notebook is part of a [collection of notebooks](../index.ipynb) in the bachelors module Signals and Systems, Communications Engineering, Universität Rostock. Please direct questions and suggestions to [Sascha.Spors@uni-rostock.de](mailto:Sascha.Spors@uni-r... | github_jupyter |
```
# LSTM for international airline passengers problem with window regression framing
import numpy
import numpy as np
import keras
import matplotlib.pyplot as plt
from pandas import read_csv
import math
from keras.models import Sequential
from keras.layers import Dense,Dropout
from keras.layers import LSTM
from sklear... | github_jupyter |
```
import pandas as pd
import zipfile
import numpy as np
import sys
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import display
directory_lic='C:\\repos\\public-procurement\\data\\licitaciones\\'
directory_oc='C:\\repos\\public-procurement\\data\\ordenes\\'
year_range=np.arange(2010,2021,1... | github_jupyter |
# Simple Model of a Car on a Bumpy Road
This notebook allows you to compute and visualize the car model presented in Example 2.4.2 the book.
The road is described as:
$$y(t) = Ysin\omega_b t$$
And $\omega_b$ is a function of the car's speed.
```
import numpy as np
def x_h(t, wn, zeta, x0, xd0):
"""Returns the ... | github_jupyter |
```
import pandas as pd
import numpy as np
from source.make_train_test import make_teams_target
pd.set_option("max_columns", 300)
def _add_stage(total):
total['stage'] = '68'
total.loc[(total.DayNum == 136) | (total.DayNum == 136), 'stage'] = '64'
total.loc[(total.DayNum == 138) | (total.DayNum == 139), '... | github_jupyter |
```
def cosamp(Phi, u, s, tol=1e-10, max_iter=1000):
"""
@Brief: "CoSaMP: Iterative signal recovery from incomplete and inaccurate
samples" by Deanna Needell & Joel Tropp
@Input: Phi - Sampling matrix
u - Noisy sample vector
s - Sparsity vector
@Return: A s... | github_jupyter |
# SLU10 - Metrics for regression: Learning Notebook
In this notebook, you will learn about:
- Loss functions vs. Evaluation Metrics
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- Mean Absolute Error (MAE)
- Coefficient of Determination (R²)
- Adjusted R²
- Scikitlearn metrics... | github_jupyter |
# Model Understanding
Simply examining a model's performance metrics is not enough to select a model and promote it for use in a production setting. While developing an ML algorithm, it is important to understand how the model behaves on the data, to examine the key factors influencing its predictions and to consider ... | github_jupyter |
In this blog we will discuss about the inference for MLR. How to choose significant predictor by Hypothesis Test and Confidence Interval, as well as doing interpretations for the slope.
*Screenshot taken from [Coursera](https://class.coursera.org/statistics-003/lect... | github_jupyter |
# Think Bayes: Chapter 7
This notebook presents code and exercises from Think Bayes, second edition.
Copyright 2016 Allen B. Downey
MIT License: https://opensource.org/licenses/MIT
```
from __future__ import print_function, division
% matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import math... | github_jupyter |
# Determining rigid body transformation using the SVD algorithm
Marcos Duarte
Ideally, three non-colinear markers placed on a moving rigid body is everything we need to describe its movement (translation and rotation) in relation to a fixed coordinate system. However, in pratical situations of human motion analysis, ... | github_jupyter |
```
!pip install keras
from keras.models import Model
from keras.optimizers import SGD,Adam,RMSprop
# from keras.layers import Dense, Input, LSTM, Embedding,Dropout,Bidirectional,Flatten
from keras.layers import *
import os
# from __future__ import print_function
from keras import backend as K
from keras.engine.topolo... | github_jupyter |
```
from PreFRBLE.likelihood import *
from PreFRBLE.plot import *
```
### Identify intervening galaxies
Here we attempto to identify LoS with intervening galaxies.
For this purpose, we compare the likelihood of temporal broadening $L(\tau)$ for scenarios with and without intervening galaxies, as well as consider a sc... | github_jupyter |
```
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
%pylab
%matplotlib inline
import os
import math
import time
import tensorflow as tf
from datasets import dataset_utils,cifar10
from tensorflow.contrib import slim
```
# lrn적용한 버젼!
```
dropout_keep_prob=0.8... | github_jupyter |
# This is the Saildrone and MUR global 1 km sea surface temperature collocation code.
```
import os
import numpy as np
import matplotlib.pyplot as plt
import datetime as dt
import xarray as xr
def get_sat_filename(date):
dir_sat='F:/data/sst/jpl_mur/v4.1/'
syr, smon, sdym, sjdy = str(date.dt.year.data), str(d... | github_jupyter |
```
import numpy as np
import cv2
import matplotlib.pyplot as plt
import glob
from keras.layers import Input, concatenate, Conv2D, MaxPooling2D, UpSampling2D, Dropout, Cropping2D
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
from keras import backend as K
from ker... | github_jupyter |
Introdução ao NumPy
===========
O tipo *ndarray*
--------------------
O tipo *ndarray*, ou apenas *array* é um arranjo de itens homogêneos de dimensionalidade N, indexados por uma tupla de N inteiros. Existem 3 informações essenciais associadas ao *ndarray*: o tipo dos dados, suas dimensões e seus dados em si. A pr... | github_jupyter |
## Introduction to AWS ParallelCluster
For an overview of this workshop, please read [README.md](README.md)
This notebook shows the main steps to create a ParallelCluster. Steps to prepare (pre and post cluster creation) for the ParallelCluster are coded in pcluster-athena.py.
#### Before:
- Create ssh key
- Creat... | github_jupyter |
<img src="../../../../../images/qiskit_header.png" alt="Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook" align="middle">
# _*Qiskit Finance: Pricing Asian Barrier Spreads*_
The latest version of this notebook is available on https://github.com/Qiskit/qiskit-t... | github_jupyter |
# Deep Markov Model
## Introduction
We're going to build a deep probabilistic model for sequential data: the deep markov model. The particular dataset we want to model is composed of snippets of polyphonic music. Each time slice in a sequence spans a quarter note and is represented by an 88-dimensional binary vector... | github_jupyter |
This notebook demonstrates [vaquero](https://github.com/jbn/vaquero), as both a library and data cleaning pattern.
```
from vaquero import Vaquero, callables_from
```
# Task
Say you think you have pairs of numbers serialized as comma separated values in a file. You want to extract the pair from each line, then sum o... | github_jupyter |
```
%matplotlib inline
```
**1**. (25 points)
We have a surgeon who wants to find rich, obese patients for bariatric surgery. The surgeon purchases 3rd party databases that include the following:
- patients - includes height and weight for 100 patients
- finances - income of patients
- orders - patients who have bou... | github_jupyter |
# Customer Segmentation in Python
This notebook explains how to perform Association Analysis from customer purchase history data. We are using [pandas](https://pandas.pydata.org) (for data manipulation) and [mlxtend](https://github.com/rasbt/mlxtend) (for apriori and association rules algorithnms). The data we're usin... | github_jupyter |
# Algo - Aparté sur le voyageur de commerce
Le voyageur de commerce ou Travelling Salesman Problem en anglais est le problème NP-complet emblématique : il n'existe pas d'algorithme capable de trouver la solution optimale en temps polynômial. La seule option est de parcourir toutes les configurations pour trouver la me... | github_jupyter |
```
import os
import json
tmp = dict()
"""
Daily Met Livneh 2013
"""
tmp['dailymet_livneh2013'] = dict()
tmp['dailymet_livneh2013']['spatial_resolution'] = '1/16-degree'
tmp['dailymet_livneh2013']['web_protocol'] = 'ftp'
tmp['dailymet_livneh2013']['domain'] = 'livnehpublicstorage.colorado.edu'
tmp['dailymet_livneh2013'... | github_jupyter |
# Метод ADMM (alternating direction methods of multipliers)
## На прошлом семинаре
- Субградиентный метод: базовый метод решения негладких задач
- Проксимальный метод и его свойства: альтернатива градиентному спуску
- Проксимальный градиентный метод: заглядывание в чёрный ящик
- Ускорение проксимального градиентного ... | github_jupyter |
# Summary Statistics - Exercises
In these exercises we'll use a real life medical dataset to learn how to obtain basic statistics from the data. This dataset comes from [Gluegrant](https://www.gluegrant.org/), an American project that aims to find a which genes are more important for the recovery of severely injured pa... | github_jupyter |
```
# -*- coding: utf-8 -*-
import pymongo
import pymysql
from lxml import etree
import re
import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings("ignore")
# -*- coding: utf-8 -*-
import pymongo
import urllib
def get_mongo_db_client():
username_str = 'breadt'
password_str = 'Breadt@201... | github_jupyter |
<a href="https://colab.research.google.com/github/mikislin/summer20-Intro-python/blob/master/07_Matplotlib_Solutions.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
**(a)** Write a Python program to draw a scatter plot using random distributions to ... | github_jupyter |
<b> One-Layer Atmosphere Model </b><br>
Reference: Walter A. Robinson, Modeling Dynamic Climate Systems
```
import numpy as np
import matplotlib.pyplot as plt
plt.style.use("seaborn-dark")
# Step size
dt = 0.01
# Set up a 10 years simulation
tmin = 0
tmax = 10
t = np.arange(tmin, tmax + dt, dt)
n = len(t)
# Seconds... | github_jupyter |
```
import keras
from keras.models import Sequential, Model, load_model
from keras.layers import Dense, Dropout, Activation, Flatten, Input, Lambda
from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Conv1D, MaxPooling1D, LSTM, ConvLSTM2D, GRU, CuDNNLSTM, CuDNNGRU, BatchNormalization, LocallyConnected2D, ... | github_jupyter |
# Math Operations
```
from __future__ import print_function
import torch
import numpy as np
from datetime import date
date.today()
author = "kyubyong. https://github.com/Kyubyong/pytorch_exercises"
torch.__version__
np.__version__
```
NOTE on notation
_x, _y, _z, ...: NumPy 0-d or 1-d arrays<br/>
_X, _Y, _Z, ...: Nu... | github_jupyter |
```
import os
import tensorflow as tf
import pandas as pd
from sklearn.utils import shuffle
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from PIL import Image
import shutil
import keras
import keras.backend as K
from keras.models import Model
from keras import backend as K
from keras.utils imp... | github_jupyter |
<a href="https://colab.research.google.com/github/AlexTeboul/msds/blob/main/csc594-topics-in-artificial-intelligence/CSC594_Emotional_Contagion_Content_Theory_Implementation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
#Alex Teboul
#CSC 594 - Em... | github_jupyter |
# Introduction
In this tutorial, we will train a regression model with Foreshadow using the [House Pricing](https://www.kaggle.com/c/house-prices-advanced-regression-techniques) dataset from Kaggle.
# Getting Started
To get started with foreshadow, install the package using `pip install foreshadow`. This will also in... | github_jupyter |
```
import re
import requests
from html.parser import HTMLParser
from time import sleep
from tqdm import tqdm
#手工输入会议以及链接
# """GECCO, SSBSE, QRS还没找到"""
journals = []
confs = []
for line in open("conf_list.csv"):
line = line.replace("http://","https://").replace("/index.html","/")
data = line.split("\t")
i... | github_jupyter |
# Skip-gram word2vec
In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in natural language processing. This will come in handy when dealing with things like translations.
## Re... | github_jupyter |
# 日本語手紙文字OCRサンプル
## OpenVINOのインストールディレクトリからオリジナルのサンプルコード関連ファイルをコピー
```
!cp $INTEL_OPENVINO_DIR/inference_engine/demos/python_demos/handwritten_japanese_recognition_demo/requirements.txt .
!cp $INTEL_OPENVINO_DIR/inference_engine/demos/python_demos/handwritten_japanese_recognition_demo/models.lst .
!cp -r $INTEL_OPENV... | github_jupyter |
# Prepare Glove vector
```
import numpy as np
import codecs
import pickle
import operator
def loadGloveModel(gloveFile):
print "Loading Glove Model"
f = codecs.open(gloveFile,'r')
model = {}
for line in f:
splitLine = line.split()
word = splitLine[0]
embedding = [float(val)... | github_jupyter |
# Categorical Features CV Encoding
### Explanation: https://medium.com/@pouryaayria/k-fold-target-encoding-dfe9a594874b
```
from google.colab import drive
drive.mount('/content/gdrive')
!pip install category_encoders
# General imports
import numpy as np
import pandas as pd
import os, sys, gc, warnings, random, dateti... | github_jupyter |
# Simple 10-class classification
```
import keras
from keras.models import Sequential
from keras.layers import Dense, Activation
import numpy as np
import matplotlib.pyplot as plt
import warnings
# Suppress warkings (gets rid of some type-conversion warnings)
warnings.filterwarnings("ignore")
%matplotlib inline
```
... | 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 |
# MODEL ANALYSIS [TEST DATA]
#### Dependecies
```
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from sklearn.metrics import brier_score_loss
LEN = range(70, 260, 10)
def decodePhed(x):
return 10**(-x/10.0)
```
#### Load csv files
```
test_regul... | github_jupyter |
# Imports
```
#!pip install plotly
from os import listdir
from os.path import isfile, join
import pandas as pd
import cbsodata
from datetime import datetime
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import altair as alt
from sklearn import preprocessing
import plotly.express as px
from ... | github_jupyter |
```
%matplotlib inline
import pandas as pd
import numpy as np
import os.path
import matplotlib.pyplot as plt
import seaborn as sns
import math
from scipy.stats import poisson
def ColumnNames():
return ['col1', 'col2', 'col3', 'average']
def PreProcess(filepath, skiprows, usecols):
"""
This function reads ... | github_jupyter |
# Using Threshold - Based Source Detection and Confusion Matrix
This notebook provides an example of how to run the astropy - based source detection on a fits file. This also demonstrates how to generate a confusion matrix based on the results of the source detection.
```
import astropy
from astropy.io import fits
fro... | github_jupyter |
# LiH Molecule: Constructing Potential Energy Surfaces Using VQE
## Step 1: Classical calculations
```
import numpy as np
import matplotlib.pyplot as plt
from utility import *
import tequila as tq
threshold = 1e-6 #Cutoff for UCC MP2 amplitudes and QCC ranking gradients
basis = 'sto-3g'
```
#### Classical Electroni... | github_jupyter |
# Face Recognition for the Happy House
Welcome to the first assignment of week 4! Here you will build a face recognition system. Many of the ideas presented here are from [FaceNet](https://arxiv.org/pdf/1503.03832.pdf). In lecture, we also talked about [DeepFace](https://research.fb.com/wp-content/uploads/2016/11/deep... | github_jupyter |
# CappingTransformer
This notebook shows the functionality in the CappingTransformer class. This transformer caps numeric columns at either a maximum value or minimum value or both. <br>
```
import pandas as pd
import numpy as np
from sklearn.datasets import fetch_california_housing
import tubular
from tubular.capping... | github_jupyter |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.