smindler

lukovkin / multi-ts-lstm.py

Last active November 25, 2022 16:23

Time series prediction with multiple sequences input - LSTM - 1

	# Time Series Testing
	import keras.callbacks
	from keras.models import Sequential
	from keras.layers.core import Dense, Activation, Dense, Dropout
	from keras.layers.recurrent import LSTM

	# Call back to capture losses
	class LossHistory(keras.callbacks.Callback):
	def on_train_begin(self, logs={}):
	self.losses = []

Nemitek / keras_prediction.py

Created October 22, 2015 04:11

Predicting sequences of vectors (regression) in Keras using RNN - LSTM (original by danielhnyk.cz) - fixed for Keras 0.2.0

	import pandas as pd
	from random import random

	flow = (list(range(1,10,1)) + list(range(10,1,-1)))*1000
	pdata = pd.DataFrame({"a":flow, "b":flow})
	pdata.b = pdata.b.shift(9)
	data = pdata.iloc[10:] * random() # some noise

	import numpy as np

hnykda / keras.py

Last active June 15, 2023 04:11

Tada's usage (see discussion)

	""" From: http://danielhnyk.cz/predicting-sequences-vectors-keras-using-rnn-lstm/ """
	from keras.models import Sequential
	from keras.layers.core import TimeDistributedDense, Activation, Dropout
	from keras.layers.recurrent import GRU
	import numpy as np

	def _load_data(data, steps = 40):
	docX, docY = [], []
	for i in range(0, data.shape[0]/steps-1):
	docX.append(data[isteps:(i+1)steps,:])

andreiz / classifier.php

Last active August 27, 2024 09:42

A simple example of logistic regression via gradient descent in PHP.

	<?php

	error_reporting(E_ALL);

	define('NUM_FEATURES', 3);

	// My dataset describes cities around the world where I might consider living.
	// Each sample (city) consists of 3 features:
	// * Feature 1: average low winter temperature in the city
	// * Feature 2: city population, in millions