|
| 1 | + |
| 2 | +# Example of using build_feature_vec for gate_feature with all available features |
| 3 | + |
| 4 | +from hal_plugins import machine_learning |
| 5 | + |
| 6 | +# Create the feature context with the netlist |
| 7 | +fc = machine_learning.Context(netlist) |
| 8 | + |
| 9 | + |
| 10 | +features = [ |
| 11 | + #machine_learning.gate_feature.ConnectedGlobalIOs(), |
| 12 | + |
| 13 | + machine_learning.gate_feature.DistanceGlobalIO(hal_py.PinDirection.output, directed=True, forbidden_pin_types=[hal_py.PinType.clock, hal_py.PinType.reset, hal_py.PinType.enable]), |
| 14 | + machine_learning.gate_feature.DistanceGlobalIO(hal_py.PinDirection.output, directed=False, forbidden_pin_types=[hal_py.PinType.clock, hal_py.PinType.reset, hal_py.PinType.enable]), |
| 15 | + machine_learning.gate_feature.DistanceGlobalIO(hal_py.PinDirection.input, directed=True, forbidden_pin_types=[hal_py.PinType.clock, hal_py.PinType.reset, hal_py.PinType.enable]), |
| 16 | + machine_learning.gate_feature.DistanceGlobalIO(hal_py.PinDirection.input, directed=False, forbidden_pin_types=[hal_py.PinType.clock, hal_py.PinType.reset, hal_py.PinType.enable]), |
| 17 | + |
| 18 | + # machine_learning.gate_feature.SequentialDistanceGlobalIO(hal_py.PinDirection.output, directed=True, forbidden_pin_types=[hal_py.PinType.clock, hal_py.PinType.reset, hal_py.PinType.enable]), |
| 19 | + # machine_learning.gate_feature.SequentialDistanceGlobalIO(hal_py.PinDirection.output, directed=False, forbidden_pin_types=[hal_py.PinType.clock, hal_py.PinType.reset, hal_py.PinType.enable]), |
| 20 | + # machine_learning.gate_feature.SequentialDistanceGlobalIO(hal_py.PinDirection.input, directed=True, forbidden_pin_types=[hal_py.PinType.clock, hal_py.PinType.reset, hal_py.PinType.enable]), |
| 21 | + # machine_learning.gate_feature.SequentialDistanceGlobalIO(hal_py.PinDirection.input, directed=False, forbidden_pin_types=[hal_py.PinType.clock, hal_py.PinType.reset, hal_py.PinType.enable]), |
| 22 | + |
| 23 | + # machine_learning.gate_feature.IODegrees(), |
| 24 | + |
| 25 | + # machine_learning.gate_feature.GateTypeOneHot(), |
| 26 | + |
| 27 | + # machine_learning.gate_feature.NeighboringGateTypes(1, hal_py.PinDirection.output, directed=True), |
| 28 | + # machine_learning.gate_feature.NeighboringGateTypes(2, hal_py.PinDirection.output, directed=True), |
| 29 | + # machine_learning.gate_feature.NeighboringGateTypes(3, hal_py.PinDirection.output, directed=True), |
| 30 | + |
| 31 | + # machine_learning.gate_feature.NeighboringGateTypes(1, hal_py.PinDirection.input, directed=True), |
| 32 | + # machine_learning.gate_feature.NeighboringGateTypes(2, hal_py.PinDirection.input, directed=True), |
| 33 | + # machine_learning.gate_feature.NeighboringGateTypes(3, hal_py.PinDirection.input, directed=True), |
| 34 | + |
| 35 | + # machine_learning.gate_feature.BetweennessCentrality(directed = True, cutoff=-1), |
| 36 | + # machine_learning.gate_feature.BetweennessCentrality(directed = True, cutoff=16), |
| 37 | + # machine_learning.gate_feature.BetweennessCentrality(directed = False, cutoff=-1), |
| 38 | + # machine_learning.gate_feature.BetweennessCentrality(directed = False, cutoff=16), |
| 39 | + # machine_learning.gate_feature.SequentialBetweennessCentrality(directed = True, cutoff=-1), |
| 40 | + # machine_learning.gate_feature.SequentialBetweennessCentrality(directed = True, cutoff=16), |
| 41 | + # machine_learning.gate_feature.SequentialBetweennessCentrality(directed = False, cutoff=-1), |
| 42 | + # machine_learning.gate_feature.SequentialBetweennessCentrality(directed = False, cutoff=16), |
| 43 | + |
| 44 | + # machine_learning.gate_feature.HarmonicCentrality(direction=hal_py.PinDirection.output, cutoff=-1), |
| 45 | + # machine_learning.gate_feature.HarmonicCentrality(direction=hal_py.PinDirection.output, cutoff=16), |
| 46 | + # machine_learning.gate_feature.HarmonicCentrality(direction=hal_py.PinDirection.inout, cutoff=-1), |
| 47 | + # machine_learning.gate_feature.HarmonicCentrality(direction=hal_py.PinDirection.inout, cutoff=16), |
| 48 | + # machine_learning.gate_feature.SequentialHarmonicCentrality(direction=hal_py.PinDirection.output, cutoff=-1), |
| 49 | + # machine_learning.gate_feature.SequentialHarmonicCentrality(direction=hal_py.PinDirection.output, cutoff=16), |
| 50 | + # machine_learning.gate_feature.SequentialHarmonicCentrality(direction=hal_py.PinDirection.inout, cutoff=-1), |
| 51 | + # machine_learning.gate_feature.SequentialHarmonicCentrality(direction=hal_py.PinDirection.inout, cutoff=16), |
| 52 | +] |
| 53 | + |
| 54 | +gates = [netlist.get_gate_by_id(3)] |
| 55 | + |
| 56 | +# Build the feature vector for the pair of gates |
| 57 | +feature_vector = machine_learning.gate_feature.build_feature_vecs(fc, features, gates) |
| 58 | + |
| 59 | +print("Feature vector:", feature_vector) |
0 commit comments