Class Hierarchy

object: The most base type
- dimer.data.AnchorAnnotation
  - dimer.data.AnchorDataset
    - dimer.data.TrainAnchorDataset
- exceptions.BaseException: Common base class for all exceptions
  - exceptions.Exception: Common base class for all non-exit exceptions.
    - dimer.filelock.FileLockException
    - exceptions.StandardError: Base class for all standard Python exceptions that do not represent interpreter exiting.
      - exceptions.AssertionError: Assertion failed.
- dimer.genome.bed.BedReader
- dimer.genome.peak.BroadPeakReader
- dimer.nnet.config_spec.CfgFactory: abstract class with factory method from a config file
  - dimer.nnet.config_spec.AESpec
  - dimer.nnet.config_spec.ModelSpec
  - dimer.nnet.config_spec.MtrainSpec
- dimer.config.Configurable: abstract class with factory method from a config file
- dimer.data.Dataset
  - dimer.data.AnchorDataset
    - dimer.data.TrainAnchorDataset
- dimer.filelock.FileLock: A file locking mechanism that has context-manager support so you can use it in a with statement.
- dimer.nnet.Layer: abstract layer class.
  - dimer.nnet.SpeedLayer: This layer provides an extra set of weights as a support the momentum algorithm for SGD.
    - dimer.nnet.autoencoder.AELayer: An autoencoder layer
    - dimer.nnet.nccn.ConvPoolLayer: LeNet conv-pool layer
    - dimer.nnet.nccn.HiddenLayer: Hidden layer of a feed-forward net
    - dimer.nnet.nccn.LogisticReg: A logistic regression layer
- dimer.nnet.Model: generic model class with basic functionality
  - dimer.nnet.autoencoder.AEStack: a stack of denoising autoencoders.
  - dimer.nnet.autoencoder.AutoEncoder: Denoising autoencoder
  - dimer.nnet.nccn.CnnModel
- dimer.nnet.monitor.Monitor: abstract class keep track of a set of parameters in the model or the learning process
  - dimer.nnet.monitor.DaeLearnMonitor
  - dimer.nnet.monitor.LearnMonitor: learning stats
  - dimer.nnet.monitor.RegrLearnMonitor: learning stats for a linear regression
  - dimer.nnet.monitor.WeightMonitor: weight and activity information on the network
- dimer.genome.peak.NarrowPeakReader
- dimer.nnet.base_test_classes.NpyTester
  - dimer.nnet.base_test_classes.ModelTester
  - dimer.nnet.autoencoder_tests.TestAEModel
  - dimer.nnet.autoencoder_tests.TestAEStackModel
  - dimer.nnet.autoencoder_tests.TestAEl
  - dimer.nnet.nccn_tests.TestCnnModel
- unittest.case.TestCase: A class whose instances are single test cases.
  - dimer.nnet.base_test_classes.CNNLayerTester: test class for CPLayer, HiddenLayer and LogisticRegression Layer
  - dimer.nnet.base_test_classes.ModelTester
  - dimer.nnet.cspec_tests.Test
  - dimer.data_tests.TestAnchorDataset
  - dimer.archive_tests.TestArchive
  - dimer.genome.bedops_tests.TestBEDMAP
  - dimer.data_tests.TestDataset
  - dimer.archive_tests.TestExp
  - dimer.nnet.nnet_tests.TestLr
  - dimer.nnet.monitor_tests.TestMonitor
- dimer.data.TheanoShare: a dataset that can return its data as theano shared variables
  - dimer.data.AnchorDataset
    - dimer.data.TrainAnchorDataset
- dimer.data.TrainDataset: a mixin for batch functionality, valid and train sub-dataset
  - dimer.data.TrainAnchorDataset
- dimer.data.aAnchorDataset: this dataset contains various tracks of epigenetic signal in the for a set of genome sites (e.g., TSS-cenetered regions) all of the same width.
- dimer.data.aaHDFArchive
- tuple: tuple() -> empty tuple tuple(iterable) -> tuple initialized from iterable's items
  - unreachable.AutoEncoderSpec: AutoEncoderSpec(rec_error, minepochs, maxepochs, batch_size, noise)
    - dimer.nnet.config_spec.AESpec
  - unreachable.DataSpec: DataSpec(dataname, tracks, width, train_s, valid_s, labels, track_names, label_names, batch_size, train_batches, valid_batches)
    - dimer.nnet.config_spec.DataSpec
  - unreachable.MetaParams: MetaParams(nkerns, rfield, pool, lreg_size)
    - dimer.nnet.config_spec.ModelSpec
  - unreachable.MtrainSpec: MtrainSpec(batch_size, l1_rate, l2_rate, lr, tau, momentum_mult, nepochs, minepochs, patience)
    - dimer.nnet.config_spec.MtrainSpec
  - unreachable.learnmonitor: learnmonitor(epoch, lrate, traincost, validcost)
    - dimer.nnet.monitor.DaeLearnMonitor
  - unreachable.learnmonitor: learnmonitor(epoch, lrate, traincost, trainCE, trainMC, validcost, validCE, validMC)
    - dimer.nnet.monitor.LearnMonitor: learning stats
  - unreachable.learnmonitor: learnmonitor(epoch, lrate, traincost, validcost)
    - dimer.nnet.monitor.RegrLearnMonitor: learning stats for a linear regression
  - unreachable.w_monitor: w_monitor(epoch, layer, wshp, wmin, wmean, wmedian, wsd, wmax, bshp, bmin, bmean, bmedian, bsd, bmax)
    - dimer.nnet.monitor.WeightMonitor: weight and activity information on the network
- type: type(object) -> the object's type type(name, bases, dict) -> a new type
  - abc.ABCMeta: Metaclass for defining Abstract Base Classes (ABCs).