Class Hierarchy

• numpy._import_tools.PackageLoader
• numpy.core.ma._MaskedPrintOption: One instance of this class, masked_print_option, is created.
• numpy.core.ma._maximum_operation: Object to calculate maxima
• numpy.core.ma._minimum_operation: Object to calculate minima
• numpy.core.ma.domain_check_interval: domain_check_interval(a,b)(x) = true where x < a or y > b
• numpy.core.ma.domain_greater: domain_greater(v)(x) = true where x <= v
• numpy.core.ma.domain_greater_equal: domain_greater_equal(v)(x) = true where x < v
• numpy.core.ma.domain_safe_divide
• numpy.core.ma.domain_tan: domain_tan(eps) = true where abs(cos(x)) < eps)
• numpy.core.ma.domained_binary_operation: Binary operations that have a domain, like divide.
• numpy.core.ma.masked_binary_operation
• numpy.core.ma.masked_unary_operation
• numpy.core.records.format_parser
• numpy.lib.getlimits.iinfo: Limits for integer types.
• numpy.testing.numpytest.NumpyTest: Numpy tests site manager.
  • numpy.testing.numpytest.ScipyTest
• object: The most base type
  • unreachable._CData: XXX to be provided
    • _ctypes._SimpleCData: XXX to be provided
      • ctypes.c_long.__ctype_be__
      • ctypes.c_long
  • basestring: Type basestring cannot be instantiated; it is the base for str and unicode.
    • unicode: unicode(string [, encoding[, errors]]) -> object
      • numpy.unicode_
    • str: str(object) -> string
      • numpy.string_
  • complex: complex(real[, imag]) -> complex number
    • numpy.complex128: Composed of two 64 bit floats
  • numpy.lib.getlimits.finfo: Machine limits for floating point types.
  • unreachable.CHasTraits
    • enthought.traits.has_traits.HasTraits: Enables any Python class derived from it to have trait atttributes.
      • src.stats306b.lecture1.pca_application.PCAAnalysis
        • src.stats306b.lecture1.pca_labels.LabelPCAAnalysis
      • src.stats306b.lecture4.kpca_application.KernelPCA: An example class to show how Kernel PCA works
      • src.stats306b.lecture1.traits.MyObject
        • src.stats306b.lecture1.traits.MyNewObject
  • numpy.broadcast
  • exceptions.BaseException: Common base class for all exceptions
    • exceptions.Exception: Common base class for all non-exit exceptions.
      • exceptions.Warning: Base class for warning categories.
        • exceptions.UserWarning: Base class for warnings generated by user code.
          • numpy.lib.polynomial.RankWarning: Issued by polyfit when Vandermonde matrix is rank deficient.
      • numpy.core.ma.MAError
  • numpy.lib.index_tricks.ndindex: Pass in a sequence of integers corresponding to the number of dimensions in the counter.
  • numpy.ctypeslib._ndptr
  • numpy.core.ma.MaskedArray: Arrays with possibly masked values.
  • numpy.generic
    • numpy.bool_
    • numpy.object_
    • numpy.number
      • numpy.integer
        • numpy.unsignedinteger
          • numpy.uint32
          • numpy.uint8
          • numpy.uint16
          • numpy.core.uintc
          • numpy.uint64
        • numpy.signedinteger
          • numpy.core.intc
          • numpy.int32
          • numpy.int8
          • numpy.int64
          • numpy.int16
      • numpy.inexact
        • numpy.complexfloating
          • numpy.complex192: Composed of two 96 bit floats
          • numpy.complex64: Composed of two 32 bit floats
          • numpy.complex128: Composed of two 64 bit floats
        • numpy.floating
          • numpy.float32
          • numpy.float96
          • numpy.float64
    • numpy.flexible
      • numpy.void
        • numpy.core.records.record
      • numpy.character
        • numpy.unicode_
        • numpy.string_
  • float: float(x) -> floating point number
    • numpy.float64
  • numpy.ufunc: Optimized functions make it possible to implement arithmetic with arrays efficiently
  • numpy.lib.polynomial.poly1d: A one-dimensional polynomial class.
  • int: int(x[, base]) -> integer
    • numpy.core.intc
    • numpy.int32
  • numpy.lib.function_base.vectorize: vectorize(somefunction, otypes=None, doc=None) Generalized Function class.
  • numpy.flatiter
  • numpy.dtype
  • numpy.core.numeric.errstate: with errstate(**state): --> operations in following block use given state.
  • numpy.lib.machar.MachAr: Diagnosing machine parameters.
  • numpy.ndarray: An array object represents a multidimensional, homogeneous array of fixed-size items.
    • numpy.core.records.recarray
    • numpy.core.defmatrix.matrix
    • numpy.core.defchararray.chararray
    • numpy.core.memmap
  • src.stats306b.lecture9.nnmf.NNMF: Use multiplicative updates to find rank k NNMF of X, written as X=WH.
    • src.stats306b.lecture9.nnmf.KL: Uses KL divergence for NNMF.
    • src.stats306b.lecture9.nnmf.Frobenius: Uses Frobenius (Euclidean) distance for NNMF.
    • src.stats306b.lecture9.nnmf.EM: An EM algorithm for non-negative matrix factorization (NNMF).
  • numpy.lib.index_tricks.ndenumerate: A simple nd index iterator over an array.
  • type: type(object) -> the object's type type(name, bases, dict) -> a new type
    • enthought.traits.has_traits.MetaHasTraits
• src.stats306b.lecture10.lda.lfunc: A linear function.
• src.stats306b.lecture10.lda.qfunc: A quadratic function based on the log-likelihood of two Gaussian densities