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<h1 align="center">TensorFlow Neural Network Lab</h1>
<img src="image/notmnist.png">
In this lab, you'll use all the tools you learned from *Introduction to TensorFlow* to label images of English letters! The data you are using, <a href="http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html">notMNIST</a>, consi... | github_jupyter |
```
#export
from fastai2.data.all import *
from fastai2.text.core import *
from nbdev.showdoc import *
#default_exp text.models.awdlstm
#default_cls_lvl 3
```
# AWD-LSTM
> AWD LSTM from [Smerity et al.](https://arxiv.org/pdf/1708.02182.pdf)
## Basic NLP modules
On top of the pytorch or the fastai [`layers`](/layers... | github_jupyter |
```
import sys, os
sys.path.append('..')
from Data.TimeSeries import *
from ETF.AAA import *
from Data import factors
import Quandl
import pandas as pd
import matplotlib
import cvxopt as opt
from cvxopt import blas, solvers
%matplotlib inline
sector_tickers = [
'GOOG/NYSEARCA_XLB',
'GOOG/NYS... | github_jupyter |
```
import numpy as np, pandas as pd, matplotlib.pyplot as plt
from scipy.stats.stats import pearsonr
from scipy.stats.stats import spearmanr
from scipy.optimize import minimize
from BTC_Alpha_func import *
from tqdm import tqdm
import os
%matplotlib inline
class signal_search(object):
def __init__(self, data,... | github_jupyter |
# SBTi-Finance Tool for Temperature Scoring & Portfolio Coverage
***Do you want to understand what drives the temperature score of your portfolio to make better engagement and investment decisions?***
This notebook provides ... | github_jupyter |
```
import json
import matplotlib.pyplot as plt; plt.rcdefaults()
import numpy as np
import os
from pprint import pprint
from scipy.stats import ttest_ind
num_evals = 5
evals = {}
task_dir = '/Users/ethanperez/research/ParlAI/parlai/mturk/core/run_data/live/context_evaluator_'
task_setup = [
# ## Persuading Humans (D... | github_jupyter |
<!--BOOK_INFORMATION-->
<img align="left" style="padding-right:10px;" src="figures/PDSH-cover-small.png">
*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/Pyth... | github_jupyter |
```
"""Optimal prioritizing of your tasks, in the sense of minimizing redundant memory footprint.
Idea is to take the longest graph path with most dependancies so that you don't mentally backtrack so often.
From the optimal paradigm of cpu worker allotment.
Simply make a dictionary where each key is a task name and the... | github_jupyter |
# Cart-pole Balancing Model with Amazon SageMaker on SageMaker Managed Spot Training
The example here is almost the same as [Cart-pole Balancing Model with Amazon SageMaker](https://github.com/awslabs/amazon-sagemaker-examples/blob/master/reinforcement_learning/rl_cartpole_coach/rl_cartpole_coach_gymEnv.ipynb).
This ... | github_jupyter |
# Window Splitters in Sktime
In this notebook we describe the window splitters included in the [`sktime.forecasting.model_selection`](https://github.com/alan-turing-institute/sktime/blob/main/sktime/forecasting/model_selection/_split.py) module. These splitters can be combined with `ForecastingGridSearchCV` for model ... | github_jupyter |
# Classify structured data using Keras Preprocessing Layers
## Learning Objectives
* Load a CSV file using [Pandas](https://pandas.pydata.org/).
* Build an input pipeline to batch and shuffle the rows using [tf.data](https://www.tensorflow.org/guide/datasets).
* Map from columns in the CSV to features used to train ... | github_jupyter |
### Imports
```
import keras
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
import math
```
### Data Examination
```
mnist_data = input_data.read_data_sets('MNIST_data/', one_hot = True)
input_batch, gt_batch = mnist_data.train.next_batch(10)
x, y = inpu... | github_jupyter |
# Missing value imputation: MeanMedianImputer
The MeanMedianImputer() replaces missing data by the mean or median value of the
variable. It works only with numerical variables.
We can pass a list of variables to be imputed. Alternatively, the
MeanMedianImputer() will automatically select all variables of type numeric... | github_jupyter |
```
import sys
sys.path.append("../libs/basic_units/")
import numpy as np
import librosa
import python_speech_features
from basic_units import cm, inch
import matplotlib.pyplot as plt
from scipy.signal.windows import hann, hamming
import tensorflow as tf
import matplotlib.pyplot as plt
k = 2
max_iterations = 100
n_mfcc... | github_jupyter |
<img src='https://certificate.tpq.io/quantsdev_banner_color.png' width="250px" align="right">
# Reinforcement Learning
**Adding Noise to the Time Series Data**
© Dr Yves J Hilpisch | The Python Quants GmbH
[quants@dev Discord Server](https://discord.gg/uJPtp9Awaj) | [@quants_dev](https://twitter.com/quants_dev... | github_jupyter |
<a href="https://colab.research.google.com/github/jan-1995/Trajectory_Ctrl_LQR/blob/main/TRAJECTORY_CONTROL_QC.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
**IMPORTING ALL THE IMPORTANT LIBRARIES**
```
from math import cos, sin
import numpy as n... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import cv2
import sys
from collections import defaultdict
from time import time
from os import makedirs
from os.path import join, isdir
from glob import glob
from keras.callbacks import TensorBoard
sys.path.append('scripts')
from model_helpers import *
from data_... | github_jupyter |
# Ray RLlib - Extra Application Example - FrozenLake-v0
© 2019-2020, Anyscale. All Rights Reserved
This example uses [RLlib](https://ray.readthedocs.io/en/latest/rllib.html) to train a policy with the `FrozenLake-v0` environment ([gym.... | github_jupyter |
## Importing Libraries
```
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from scipy import stats
from plotly import express as px
import matplotlib.pyplot as plt
import seaborn as sns
import datetime as dt
today = dt.datetime.today()
```
## Importing the Tables
```
df... | github_jupyter |
<a href="https://colab.research.google.com/github/FranciscoLuna/curso_ICE_STM32CUBEIDE/blob/master/IMDB_y_RNR_entrenamiento%2C_evaluacion_y_descarga.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Análisis de sentimientos con Redes Neuronales Recu... | github_jupyter |
# Explaining Tree Models with Interventional Feature Perturbation Tree SHAP
<div class="alert alert-info">
To enable SHAP support, you may need to run
```bash
pip install alibi[shap]
```
</div>
## Introduction
This example shows how to apply interventional Tree SHAP to compute shap values exactly for an `xgboo... | github_jupyter |
```
import numpy as np
import tensorflow as tf
%matplotlib inline
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
plt.gray()
plt.imshow(mnist.train.images[0].reshape(28, 28))
plt.show()
print(mnist.train.labels[0])... | github_jupyter |
# Exploratory data analysis for hourly time series
```
import pandas as pd
import numpy as np
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from statsmodels.tsa.seasonal import STL
from matplotlib import pyplot as plt, rc_context, rc
plt.style.use('seaborn-deep')
```
We start by reading in the data.
... | github_jupyter |
# Phase 3. Statistical Information
## Contents
- [Configuration](#Configuration)
- [Imports](#Imports)
- [Variables](#Variables)
- [Support functions](#Support-functions)
- [Botscore distribution](#Botscore-distribution)
- [Daily total traffic](#Daily-total-traffic)
- [Tweet type distributions per botscore autho... | github_jupyter |
# Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network is based off of Andrej Karpathy's [post on RNNs](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) and [i... | github_jupyter |
<a href="https://colab.research.google.com/github/wtsyang/UserIntentPrediction/blob/BERT/BERT/LSTM-multiPrediction_addPenality_32_lrschedule.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
from google.colab import drive # import drive from googl... | github_jupyter |
# Introduction to ImageJ Ops
[ImageJ Ops](https://imagej.net/ImageJ_Ops) is a library for N-dimensional image processing.
The primary design goals of Ops are:
1. __Ease of use.__ Ops provides a wealth of easy-to-use image processing operations ("ops").
2. __Reusability.__ Ops extends Java's mantra of "[write once, r... | github_jupyter |
# Identifying safe loans with decision trees
```
import pandas as pd
import numpy as np
from sklearn import tree
from IPython.display import Image
import pydotplus
%matplotlib inline
loans = pd.read_csv('lending-club-data.csv')
loans.head()
loans.columns
```
## Features for the classification algorithm
```
# safe_lo... | github_jupyter |
Before you begin, execute this cell to import numpy and packages from the D-Wave Ocean suite, and all necessary functions for the gate-model framework you are going to use, whether that is the Forest SDK or Qiskit. In the case of Forest SDK, it also starts the qvm and quilc servers.
```
%run -i "assignment_helper.py"
... | github_jupyter |
Based on: https://arxiv.org/abs/1508.06576
```
!ln -s "/content/drive/MyDrive/meu/imgs" ./data
# from __future__ import print_function
import torch
import torch.nn as nn
import torch.optim as optim
from PIL import Image
import torchvision.transforms as transforms
import torchvision.models as models
from torchvision.u... | github_jupyter |
```
from azureml.train.estimator import Estimator
from azureml.core import Workspace, Experiment
from azureml.core.compute import ComputeTarget, AmlCompute
from azureml.widgets import RunDetails
from azureml.train.hyperdrive import *
from azureml.train.automl import AutoMLConfig
from azureml.train.automl.constants impo... | github_jupyter |
<a href="https://colab.research.google.com/github/napsternxg/ipython-notebooks/blob/master/Keras_Elmo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
! pip install nltk
import tensorflow as tf
import tensorflow_hub as hub
import tensorflow.keras... | github_jupyter |
# SUSA CX Kaggle Capstone Project
## Part 3: Hyperparameter Tuning, Decision Trees, Ensemble Learning
### Table Of Contents
* [Introduction](#section1)
* [Hyperparameters](#section2)
* [GridSearch](#section2i)
* [Decision Trees](#section3)
* [Random Forest](#section4)
* [Ensemble Learning](#section5)
* [Conclusion]... | github_jupyter |
# Single cell data analysis using Scanpy
* __Notebook version__: `v0.0.5.2`
* __Created by:__ `Imperial BRC Genomics Facility`
* __Maintained by:__ `Imperial BRC Genomics Facility`
* __Docker image:__ `imperialgenomicsfacility/scanpy-notebook-image:release-v0.0.4`
* __Github repository:__ [imperial-genomics-facility/s... | github_jupyter |
# Description
Generates the figure for top cell types for a specified LV (in Settings section below).
# Modules loading
```
%load_ext autoreload
%autoreload 2
import re
from pathlib import Path
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from data.recount2 import LVAnalysis
from utils... | github_jupyter |
```
%matplotlib inline
```
3D interpolation
=============
Interpolation of a three-dimensional regular grid.
Trivariate
-----------
The
[trivariate](https://pangeo-pyinterp.readthedocs.io/en/latest/generated/pyinterp.trivariate.html#pyinterp.trivariate)
interpolation allows obtaining values at arbitrary points in a... | github_jupyter |
# Meshed AC-DC example
This example has a 3-node AC network coupled via AC-DC converters to a 3-node DC network. There is also a single point-to-point DC using the Link component.
The data files for this example are in the examples folder of the github repository: <https://github.com/PyPSA/PyPSA>.
```
import pypsa
i... | 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 |
#### Based on https://github.com/bkj/basenet/tree/master/examples
```
import sys
import json
import argparse
import numpy as np
from time import time
from PIL import Image
import torch
from torch import nn
from torch.nn import functional as F
from torch.autograd import Variable
from torchvision import transforms, da... | github_jupyter |
<a href="https://colab.research.google.com/github/JiaminJIAN/20MA573/blob/master/src/Stochastic_approximation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
## **Stochastic Approximation**
Let $D = \{X_{i}: i \in \mathbb{N}\}$ be a data set of iid... | github_jupyter |
# Generating statistics for subset of Wikidata
Example Dataset wikidata subset: https://drive.google.com/drive/u/1/folders/1KjNwV5M2G3JzCrPgqk_TSx8wTE49O2Sx \
Example Dataset statistics: https://drive.google.com/drive/u/0/folders/1_4Mxd0MAo0l9aR3aInv0YMTJrtneh7HW
## Example Invocation command
papermill Knowledg... | github_jupyter |
Before you begin, execute this cell to import numpy and packages from the D-Wave Ocean suite, and all necessary functions the gate-model framework you are going to use, whether that is the Forest SDK or Qiskit. In the case of Forest SDK, it also starts the qvm and quilc servers.
```
%run -i "assignment_helper.py"
```
... | github_jupyter |
```
import os
from pathlib import Path
import numpy as np
import scipy.io
import matplotlib.pyplot as plt
# set Human SAO directory here
SAO_BASE_DIR_NAME = 'F:/Ionosonde/SAO_pick'
SAO_BASE_DIR = Path(SAO_BASE_DIR_NAME)
# set SBF directory here
SBF_BASE_DIR_NAME = 'F:/Ionosonde/SBF_mat'
SBF_BASE_DIR = Path(SBF_BASE_DI... | github_jupyter |
```
import cv2
import numpy as np
import os,sys
from time import time as t
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from firebase import firebase
import firebase_admin
from firebase_admin import credentials
from firebase_admin import db
import argpars... | github_jupyter |
# Singly LInked List
```
class Node:
def __init__(self,data):
self.val = data
self.next = None
class LinkedList:
def __init__(self):
self.head = None
def insertStart(self,data):
newNode = Node(data)
newNode.next = self.head
self.head = newNode
... | github_jupyter |
# Introduction
This notebook was used in order to create the **"Early-fusion + Odom correction" row in TABLE II**.
Note that a lot of code is copy-pasted across notebooks, so you may find some functionality implemented here that is not used, for instance the network is implemented in a way to support late-fusion, whi... | github_jupyter |
# 8章 二値分類
```
# 必要ライブラリの宣言
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
# PDF出力用
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('png', 'pdf')
```
### シグモイド関数のグラフ
図8-4
```
xx = np.linspace(-6, 6, 500)
yy = 1 / (np.exp(-xx) + 1)
plt.figure(figsize=(6,6))
plt.ylim(-... | github_jupyter |
# Word Embeddings
**Learning Objectives**
You will learn:
1. How to use Embedding layer
1. How to create a classification model
1. Compile and train the model
1. How to retrieve the trained word embeddings, save them to disk and visualize it.
## Introduction
This notebook contains an introduction to word embeddin... | github_jupyter |
# <center> Word Tokenization Techniques in NLP
## What is word tokenization?
Tokenization is breaking the raw text into small chunks. Tokenization breaks the raw text into words, sentences called **tokens**. These tokens help in understanding the context or developing the model for the NLP.
The tokenization helps in ... | github_jupyter |
```
import os,sys
sys.path.append('/home/tanmay/JHU/project/deep-mediation/manuscript/code/deep-mediation/src')
import tensorflow as tf
import importlib
import auxiliaryfunctions
import statsmodels.api as sm
import statsmodels.formula.api as smf
import seaborn as sns
import numpy as np
from scipy.stats import zscore,no... | github_jupyter |
# TensorFlow Scan Examples
#### By [Rob DiPietro](http://rdipietro.github.io) – Version 0.32 – April 28, 2016.
## Post or Jupyter Notebook?
This work is available both as a [post](http://rdipietro.github.io/tensorflow-scan-examples/) and as a [Jupyter notebook](https://github.com/rdipietro/jupyter-notebooks/tree/mast... | github_jupyter |
<font color=gray>Oracle Cloud Infrastructure Data Science Sample Notebook
Copyright (c) 2021 Oracle, Inc. All rights reserved. <br>
Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl.
</font>
# Deploying a Simple Sklearn Linear Regression Model
In this tutorial we ... | github_jupyter |
# Project: Investigate a Dataset (TMDb Movies Dataset)
## Table of Contents
<ul>
<li><a href="#intro">Introduction</a></li>
<li><a href="#wrangling">Data Wrangling</a></li>
<li><a href="#eda">Exploratory Data Analysis</a></li>
<li><a href="#conclusions">Conclusions</a></li>
<a id='intro'></a>
## Introduction
This d... | github_jupyter |
# SetUp
```
import os
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import tensorflow as tf
%matplotlib inline
def reset_graph(seed=42):
tf.reset_default_graph()
tf.set_random_seed(seed)
np.random.seed(seed)
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt... | github_jupyter |
# Tabular data handling
This module defines the main class to handle tabular data in the fastai library: [`TabularDataset`](/tabular.data.html#TabularDataset). As always, there is also a helper function to quickly get your data.
To allow you to easily create a [`Learner`](/basic_train.html#Learner) for your data, it ... | github_jupyter |
```
import torch
from torch import nn
from txtfeeder import TXTFeeder
import math
RANDOM_SEED = 42
# Num of bits for the SDR input for character
# The ASCII input has 7 bits. To have sparse representation (< 2%)
# We set 512 as the number of bits and 10 as number of ON bits
NUM_SDR_BIT = 512
NUM_SDR_ON_BIT = 10
INPUT_N... | github_jupyter |
## **Step 1: Please select a GPU runtime**
 
## **Step 2: Install TVM by running the following block.**
We have pre-compiled a tvm build for your convenience.
```
# Let's first install TVM!
# This ... | github_jupyter |
## Summary
- *hidden_size = 162*.
- *num_heads = 9*.
- *dropout = 0*.
- N=16.
- Add node and edge features (node features as 81-dim. embedding in `hidden_size`-dim space).
- Edgeconv: embed x and edge to half their size and keep row x only.
- Embed attention with `model_size == 63` and add `output_dim` attribute to at... | github_jupyter |
# Useful Methods
* [apply() method](#apply_method)
* [apply() with a function](#apply_function)
* [apply() with a lambda expression](#apply_lambda)
* [apply() on multiple columns](#apply_multiple)
* [numpy Vectorize()](#vectorize)
* [describe()](#describe)
* [sort_values()](#sort)
* [corr()](#corr)
* [idxmin and idxma... | github_jupyter |
[](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/8.Generic_Classifier.ipynb)
# Generic Clas... | github_jupyter |
# Распознавание речи
Мы все больше ждем, что компьютеры смогут использовать ИИ для понимания произнесенных и напечатанных команд на естественном языке человека. Например, можно внедрить систему автоматизации дома, которая позволит контролировать устройства в вашем доме с помощью голосовых команд, например «включи св... | github_jupyter |
# Balances
<div class="alert alert-danger">
<strong>Warning!</strong> This notebook contains a fake portfolio <i>(automatically generated)</i> and <b>does not</b> represent my own, it merely serves as an example.
</div>
```
import pytz
import socket
from datetime import datetime, timezone
now = datetime.now().asti... | github_jupyter |
## Module loading
```
import h5py
import matplotlib.pyplot as plt
import numpy as np
import tensorflow.keras as keras
import tensorflow as tf
import os
import nibabel as nib
import random
import re
from sklearn.model_selection import train_test_split
from natsort import natsorted
from collections import Counter
from t... | github_jupyter |
```
%matplotlib inline
import matplotlib.pyplot as plt
import string
import gzip
import json
import re
import numpy as np
import pandas as pd
import scipy
from scipy import interpolate
import glob
import sklearn.cluster
import sklearn.feature_extraction
import sklearn.feature_extraction.text
import sklearn.metrics
im... | github_jupyter |
```
import json
import tensorflow as tf
import csv
import random
import numpy as np
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.utils import to_categorical
from tensorflow.keras import regularizers
embedding_dim = 1... | github_jupyter |
```
import numpy as np
from mlxtend.plotting import plot_decision_regions
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB
from sklearn.base import ClassifierMixin
xx, yy = np.meshgrid(np.linspace(-3, 3, 50),
np.lin... | github_jupyter |
##### Copyright 2020 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 |
# LSTM - Test Model
Si eseguono i test a partire da un modello LSTM.
## Caricamento del dataframe
```
import torch
from torch import nn
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import Dataset, DataLoader, WeightedRandomSampler
import torch.nn as nn
import numpy as np
import pandas as ... | github_jupyter |
```
import pydeck as pdk
import pandas as pd
# 2014 locations of car accidents in the UK
UK_ACCIDENTS_DATA = ('https://raw.githubusercontent.com/uber-common/deck.gl-data/master/examples/3d-heatmap/heatmap-data.csv')
pd.read_csv(UK_ACCIDENTS_DATA).head()
# Define a layer to display on a map
layer = pdk.Layer(
'Hexa... | github_jupyter |
##### Copyright 2021 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 |
<img src="../img/saturn_logo.png" width="300" />
# Parallel Inference
We are ready to scale up our inference task!
<img src="https://media.giphy.com/media/4H5nOUqX7FywOGpCF7/giphy.gif" alt="scaleup" style="width: 200px;"/>
**Dataset:** [Stanford Dogs](http://vision.stanford.edu/aditya86/ImageNetDogs/main.html)
*... | github_jupyter |
# Figure 8. Compare vaccine strains to estimated and observed closest strains to the future
Observed distance to natural H3N2 populations one year into the future for each vaccine strain (green) and the observed (blue) and estimated (orange) closest strains to the future at the corresponding timepoints. Vaccine strain... | github_jupyter |
# 初始化环境
```
from IPython.display import display, Math
from sympy import *
init_printing()
from helper import comparator_factory, comparator_eval_factory, comparator_method_factory
x,y,z = symbols('x y z')
comparator = comparator_factory('使用{}前:','使用后:')
method_comparator = comparator_method_factory('调用{}前:','调用后:')... | github_jupyter |
***
# 数据清洗：
> # 对占中新闻进行数据清洗
***
***
王成军
wangchengjun@nju.edu.cn
计算传播网 http://computational-communication.com
```
# 使用with open读取每一行数据
with open("/Users/chengjun/github/cjc2016/data/occupycentral/zz-hk-2014-10.rtf") as f:
news = f.readlines()
# 查看总共有多少行
len(news)
# 注意：标题和版面之间存在一个空行！所以title是block的第4个元素。
for i in r... | github_jupyter |
<img src="../figures/HeaDS_logo_large_withTitle.png" width="300">
<img src="../figures/tsunami_logo.PNG" width="600">
[](https://colab.research.google.com/github/Center-for-Health-Data-Science/PythonTsunami/blob/intro/Data_structures/Arrays_num... | github_jupyter |
```
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
training_set = pd.read_csv('train.csv')
test_set = pd.read_csv('test.csv')
training_set.head()
test_set.head()
x_train = training_set.iloc[:, 1:].values
y_train = training_set.iloc[:, 0:1].values
x_test = test_set.iloc[:, :].values
x_train = x_t... | github_jupyter |
# MNIST classification example with TensorFlow
## Install packages on Google Cloud Datalab (locally use conda env)
### Select in the Python3 Kernel:
In the menu bar the of 'Kernel', select
**python3**
### Install needed packages
copy the command below in a Google Cloud Datalab cell
**!pip install tensorflow==2.... | github_jupyter |
```
# import the project1-prepareData notebook:
!pip install ipynb
from ipynb.fs.full.data_analysis import *
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
berlinDf_final_linear = berlinDf_select.copy()
# berlinDf_final_linear.drop(['newlyConst', 'balcony','... | github_jupyter |
```
from six.moves import cPickle as pickle
import numpy as np
import tensorflow as tf
from IPython.display import display, Image
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from scipy import stats
from utils import show_graph
%matplotlib inline
```
## Load Data
```
with open('SVHN_data.pickle', 'rb') ... | github_jupyter |
```
import numpy as np
import cntk_unet
import simulation
%matplotlib inline
import helper
import cntk as C
from cntk.learners import learning_rate_schedule, UnitType
# Generate some random images
input_images, target_masks = simulation.generate_random_data(192, 192, count=3)
print(input_images.shape, target_masks.sh... | github_jupyter |
# Sequence prediction analysis
The Sequence prediction analysis tests are done to analyze several different elements of different algorithms.
### Networks to Evaluate
- Fully Connected Networks
- LSTM Networks
- Other RNNs (bidirectionals, GRUs, ...)
- TCN (Temporal Convolutional Networks)
- MANNs (different on... | 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 |
# 🔄 Online learning for time series prediction 🔄
In [1], the authors develop an online learning method to predict time-series
generated by and ARMA (autoregressive moving average) model.
They develop an effective online learning algorithm based on an **improper learning** approach which consists to use an AR model... | github_jupyter |
# LSTM implementation for the centralized model
```
# Dataset - 2019
# Imputation tech - KNN for both air pollutants and meteorological data
# Evaluation metric - MAE while training and SMAPE metric for validating test data
# Negative values where not replaced
import os
import datetime
import matplotlib.pyplot as plt... | github_jupyter |
# 第2章 感知机
二分类模型
$f(x) = sign(w*x + b)$
损失函数 $L(w, b) = -\Sigma{y_{i}(w*x_{i} + b)}$
---
#### 算法
随即梯度下降法 Stochastic Gradient Descent
随机抽取一个误分类点使其梯度下降。
$w = w + \eta y_{i}x_{i}$
$b = b + \eta y_{i}$
当实例点被误分类，即位于分离超平面的错误侧，则调整w, b的值，使分离超平面向该无分类点的一侧移动，直至误分类点被正确分类
拿出iris数据集中两个分类的数据和[sepal length，sepal width]作为特征
`... | github_jupyter |
# Single Qubit Gates
In the previous section we looked at all the possible states a qubit could be in. We saw that qubits could be represented by 2D vectors, and that their states are limited to the form:
$$ |q\rangle = \cos{(\tfrac{\theta}{2})}|0\rangle + e^{i\phi}\sin{\tfrac{\theta}{2}}|1\rangle $$
Where $\theta$ ... | github_jupyter |
# Machine Learning Workflow Automation using the Step Functions Data Science SDK
>__NOTE:__ This Notebook uses the _Python 3 (Data Science)_ Kernel.
---
## 1. Pre-Requisites
### Load the Step Functions Data Science Python Library
```
%%capture
!pip install stepfunctions==2.2.0 sagemaker==2.49.1
```
### Simulate B... | github_jupyter |
## Coronary Heart Disease Prediction
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
# Sklearn
from sklearn.preprocessing import normalize
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection imp... | 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 |
# Getting Started
This section gives an overview over parsing, simulating and filtering models using **pydsge**. It also explains how to load and process data from an estimation.
```
# only necessary if you run this in a jupyter notebook
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
```
## P... | 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 |
```
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
mse_d = pd.read_csv('C:/Users/peter/Desktop/volatility-forecasting/results/test_res/mse_values_daily.csv')
mse_d.set_index(mse_d.iloc[:, 0], inplace = True)
mse_d = mse_d.iloc[:, 1:]
mse_td = pd.read_csv('C:/Users/peter/De... | github_jupyter |
```
%matplotlib inline
```
`파이토치(PyTorch) 기본 익히기 <intro.html>`_ ||
`빠른 시작 <quickstart_tutorial.html>`_ ||
`텐서(Tensor) <tensorqs_tutorial.html>`_ ||
`Dataset과 Dataloader <data_tutorial.html>`_ ||
`변형(Transform) <transforms_tutorial.html>`_ ||
`신경망 모델 구성하기 <buildmodel_tutorial.html>`_ ||
**Autograd** ||
`최적화(Optimizati... | github_jupyter |
```
## Import numpy and visualization packages
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import datasets
# import data
boston = datasets.load_boston()
X_boston = boston['data']
X_boston = (X_boston - X_boston.mean(0))/(X_boston.std(0))
y_boston = boston['target']
y_boston =... | github_jupyter |
# Archiving Discourse
This Jupyter notebook contains the Python code I use to auto-archive my Discourse instances using the API. You can read more about this in [my question](https://meta.discourse.org/t/a-basic-discourse-archival-tool/62614) on [DiscourseMeta](https://meta.discourse.org/).
If you're reading the HTML... | github_jupyter |
# CUSTOMER CHURN ANALYSIS
## Business Project Analysis - Final
_By **Grégory PINCHINAT**_
---
The leading telecom company has a massive market share but one big problem: several rivals that are constantly trying to steal customers. Because this company has been the market leader for so many years, there are not si... | github_jupyter |
```
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
import sys
SOURCE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__name__)))
sys.path.insert(0, SOURCE_DIR)
import malaya_speech
import malaya_speech.config
from malaya_speech.train.model import fastspeech2
import tensorflow as tf
import numpy as np
config... | github_jupyter |
# Borderline-SMOTE
- Creates new samples by interpolation between samples of the minority class and their closest neighbours.
- It does not use all observations from the minority class as templates, unllike SMOTE.
- It selects those observations (from the minority) for which, most of their neighbours belong to a diff... | github_jupyter |
# Temporal-Difference Methods
In this notebook, you will write your own implementations of many Temporal-Difference (TD) methods.
While we have provided some starter code, you are welcome to erase these hints and write your code from scratch.
---
### Part 0: Explore CliffWalkingEnv
We begin by importing the necess... | github_jupyter |
```
! pip install -Uq pandas fastparquet
import pandas as pd
DATA_PATH = '../data'
! ls {DATA_PATH}
df = pd.read_parquet(f'{DATA_PATH}/data.parquet', engine='fastparquet')
df
```
---
Now we want to cleanup timestamp columns to `datetime`:
```
df[df['timestamp'].isna()].count()
df[df['first_order_ts'].isna()].count()
... | github_jupyter |
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