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# from https://cloud.google.com/solutions/machine-learning-with-financial-time-series-data | |
def tf_confusion_metrics(model, actual_classes, session, feed_dict): | |
predictions = tf.argmax(model, 1) | |
actuals = tf.argmax(actual_classes, 1) | |
ones_like_actuals = tf.ones_like(actuals) | |
zeros_like_actuals = tf.zeros_like(actuals) | |
ones_like_predictions = tf.ones_like(predictions) | |
zeros_like_predictions = tf.zeros_like(predictions) |
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#!/usr/bin/env python | |
# An exponential backoff around Boto3 DynamoDB, whose own backoff eventually | |
# fails on long multipage scans. We'd like to use this as a wrapper somehow, | |
# see: https://gist.github.com/numberoverzero/cec21b8ca715401c5662 | |
from time import sleep | |
import boto3 | |
from boto3.dynamodb.conditions import Attr |
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# vim: set fileencoding=utf-8 : | |
# | |
# How to store and retrieve gzip-compressed objects in AWS S3 | |
########################################################################### | |
# | |
# Copyright 2015 Vince Veselosky and contributors | |
# | |
# 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 |
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ks.default <- function(rows) seq(2, max(3, rows %/% 4)) | |
many_kmeans <- function(x, ks = ks.default(nrow(x)), ...) { | |
ldply(seq_along(ks), function(i) { | |
cl <- kmeans(x, centers = ks[i], ...) | |
data.frame(obs = seq_len(nrow(x)), i = i, k = ks[i], cluster = cl$cluster) | |
}) | |
} | |
all_hclust <- function(x, ks = ks.default(nrow(x)), point.dist = "euclidean", cluster.dist = "ward") { |