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| Clone Mass | Clones in CloneSet | Parameter Count | Clone Similarity | Syntax Category [Sequence Length] |
|---|---|---|---|---|
| 4 | 2 | 0 | 1.000 | stmt_list[2] |
| Clone Abstraction | Parameter Bindings |
| Clone Instance (Click to see clone) | Line Count | Source Line | Source File |
|---|---|---|---|
| 1 | 4 | 225 | Bio/MaxEntropy.py |
| 2 | 4 | 128 | Bio/NaiveBayes.py |
| ||||
if not len(training_set):
raise ValueError("No data in the training set.")
if len(training_set)!=len(results):
raise ValueError("training_set and results should be parallel lists.")
# Rename variables for convenience.
|
| ||||
if not len(training_set):
raise ValueError("No data in the training set.")
if len(training_set)!=len(results):
raise ValueError("training_set and results should be parallel lists.")
# If no typecode is specified, try to pick a reasonable one. If
# training_set is a Numeric array, then use that typecode.
# Otherwise, choose a reasonable default.
# XXX NOT IMPLEMENTED
# Check to make sure each vector in the training set has the same
# dimensionality.
|
| |||
if not len(training_set):
raise ValueError("No data in the training set.")
if len(training_set)!=len(results):
raise ValueError("training_set and results should be parallel lists.")
# If no typecode is specified, try to pick a reasonable one. If
# training_set is a Numeric array, then use that typecode.
# Otherwise, choose a reasonable default.
# XXX NOT IMPLEMENTED
# Check to make sure each vector in the training set has the same
# dimensionality.
# Rename variables for convenience.
|
| CloneAbstraction |
| Parameter Index | Clone Instance | Parameter Name | Value |
|---|---|---|---|
| None | |||