microprobe.utils.distrib

microprobe.utils.distrib module

Functions

average(array[, weights])	param array:
compute_weighted_profile_average(profile, ...)	param profile:
discrete_average(average_val)	param average_val:
generate_plain_profile(elements)	param elements:
generate_weighted_profile(elements, ...[, ...])	param elements:
locality(values, locdef)
probability(value)
pstdev(data)	Calculates the population standard deviation
regular_probability(value)	Returns callable that returns True every value calls
regular_seq(items)	param items:
shuffle(slist, threshold)	param slist:
sort_by_distance(regs, distdict, useddict, ...)	param regs:
sort_by_usage(regs, lastdict, dummy_defdict)	param regs:
weighted_choice(items)	Returns a function that makes a weighted random choice from items.

Classes

Choice(items)

Classes diagram

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Functions

average(array, weights=None)

Parameters:

• array –

• weights –

compute_weighted_profile_average(profile, attribute)

Parameters:

• profile –

• attribute –

discrete_average(average_val)

Parameters:

average_val –

generate_plain_profile(elements)

Parameters:

elements –

generate_weighted_profile(elements, attribute, targetvalue, maxvalue=None, minvalue=None)

Parameters:

• elements –

• attribute –

• targetvalue –

• maxvalue – (Default value = None)

• minvalue – (Default value = None)

locality(values, locdef)

probability(value)

pstdev(data)

Calculates the population standard deviation

regular_probability(value)

Returns callable that returns True every value calls

regular_seq(items)

Parameters:

items –

shuffle(slist, threshold)

Parameters:

• slist –

• threshold –

sort_by_distance(regs, distdict, useddict, distance, dummy_instr, dummy_idx)

Parameters:

• regs –

• distdict –

• useddict –

• distance –

• dummy_instr –

• dummy_idx –

sort_by_usage(regs, lastdict, dummy_defdict)

Parameters:

• regs –

• lastdict –

• dummy_defdict –

weighted_choice(items)

Returns a function that makes a weighted random choice from items.

Parameters:

items –