ktrnka

from __future__ import unicode_literals
import logging
import numpy
import keras.models
import keras.layers.core
import keras.regularizers
import sklearn.metrics
import sklearn.base
import keras.constraints
import keras.layers.noise

ktrnka

class StackedEnsembleClassifier(sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin):
    """
    Ensemble of classifiers. Each classifier must have predict_proba and the head classifier is trained
    to predict the output based on the individual classifier outputs. Stratified k-fold cross-validation
    is used to get probabilities on held-out data.
    """
    def __init__(self, classifiers, head_classifier, num_folds=3):
        self.classifiers = classifiers
        self.head_classifier = head_classifier
        self.num_folds = num_folds

ktrnka

def prepare_time_matrix(X, time_steps=5, fill_value=None):
    if time_steps < 1:
        raise ValueError("time_steps must be 1 or more")

    assert isinstance(X, numpy.ndarray)
    time_shifts = [X]
    time_shifts.extend(numpy.roll(X, t, axis=0) for t in range(1, time_steps))
    time_shifts = reversed(time_shifts)

    X_time = numpy.dstack(time_shifts)

ktrnka

EVTF

• hourly count of total events
• percent of hour in MRB range, in MSL range, in Mars umbra
• time since the last MSL AOS_10 and MRB AOS_00 and AOS_10
• altitude integrated from the ascend/descend events

FTL

• percent of hour in FTL_EARTH, NADIR, flagcomms, etc. Not all of the fields but most

ktrnka