def crop_status_hmm_model(nn_pobability_matrix, timeseries_steps,
n_observed_classes):
# 0 1 2 3 4 5
['emergence', 'growth', 'flowers', 'senescing', 'senesced', 'no_crop']
d0 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=0,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d1 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=1,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d2 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=2,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d3 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=3,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d4 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=4,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d5 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=5,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
s0_emerge = State(d0, name='emergence')
s1_growth = State(d1, name='growth')
s2_fls = State(d2, name='flowers')
s3_sencing = State(d3, name='senescing')
s4_senced = State(d4, name='senesced')
s5_none = State(d5, name='no_crop')
model = HiddenMarkovModel()
# Initialize each hidden state.
# All states have an equal chance of being the starting state.
for s in [s0_emerge, s1_growth, s2_fls, s3_sencing, s4_senced, s5_none]:
model.add_state(s)
model.add_transition(model.start, s, 1)
model.add_transitions(
s0_emerge,
[s0_emerge, s1_growth, s2_fls, s3_sencing, s4_senced, s5_none],
[90., 5., 0., 0., 0., 5.])
model.add_transitions(
s1_growth,
[s0_emerge, s1_growth, s2_fls, s3_sencing, s4_senced, s5_none],
[0., 90., 2.5, 2.5, 0., 5.])
model.add_transitions(
s2_fls, [s0_emerge, s1_growth, s2_fls, s3_sencing, s4_senced, s5_none],
[0., 0., 90., 5., 0., 5.])
model.add_transitions(
s3_sencing,
[s0_emerge, s1_growth, s2_fls, s3_sencing, s4_senced, s5_none],
[0., 0., 0., 90., 5., 5.])
model.add_transitions(
s4_senced,
[s0_emerge, s1_growth, s2_fls, s3_sencing, s4_senced, s5_none],
[0., 0., 0., 0., 90., 10.])
model.add_transitions(
s5_none,
[s0_emerge, s1_growth, s2_fls, s3_sencing, s4_senced, s5_none],
[10., 0, 0., 0., 0., 90.])
model.bake(verbose=False)
return model
def dominant_cover_hmm_model(nn_pobability_matrix, timeseries_steps,
n_observed_classes):
d0 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=0,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d1 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=1,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d2 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=2,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d3 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=3,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d4 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=4,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
s0_veg = State(d0, name='vegetation')
s1_residue = State(d1, name='residue')
s2_soil = State(d2, name='soil')
s3_snow = State(d3, name='snow')
s4_water = State(d4, name='water')
model = HiddenMarkovModel()
# Initialize each hidden state.
# All states have an equal chance of being the starting state.
for s in [s0_veg, s1_residue, s2_soil, s3_snow, s4_water]:
model.add_state(s)
model.add_transition(model.start, s, 1)
model.add_transitions(s0_veg,
[s0_veg, s1_residue, s2_soil, s3_snow, s4_water],
[95., 1.0, 1.0, 1.0, 1.0])
model.add_transitions(s1_residue,
[s0_veg, s1_residue, s2_soil, s3_snow, s4_water],
[1.0, 95., 1.0, 1.0, 1.0])
model.add_transitions(s2_soil,
[s0_veg, s1_residue, s2_soil, s3_snow, s4_water],
[1.0, 1.0, 95., 1.0, 1.0])
model.add_transitions(s3_snow,
[s0_veg, s1_residue, s2_soil, s3_snow, s4_water],
[1.0, 1.0, 1.0, 95., 1.0])
model.add_transitions(s4_water,
[s0_veg, s1_residue, s2_soil, s3_snow, s4_water],
[1.0, 1.0, 1.0, 1.0, 95.])
model.bake(verbose=False)
return model
def crop_type_hmm_model(nn_pobability_matrix, timeseries_steps,
n_observed_classes):
# 0 1 2 3 4 5
[
'unknown_plant', 'large_grass', 'small_grass', 'other', 'fallow',
'no_crop'
]
d0 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=0,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d1 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=1,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d2 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=2,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d3 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=3,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d4 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=4,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
d5 = NeuralNetworkWrapperCustom(
predicted_probabilities=nn_pobability_matrix,
i=5,
n_samples=timeseries_steps,
n_classes=n_observed_classes)
s0_unk = State(d0, name='unknown_plant')
s1_large = State(d1, name='large_grass')
s2_small = State(d2, name='small_grass')
s3_other = State(d3, name='other')
s4_fallow = State(d4, name='fallow')
s5_none = State(d5, name='no_crop')
model = HiddenMarkovModel()
# Initialize each hidden state.
# All states have an equal chance of being the starting state.
for s in [s0_unk, s1_large, s2_small, s3_other, s4_fallow, s5_none]:
model.add_state(s)
model.add_transition(model.start, s, 1)
model.add_transitions(
s0_unk, [s0_unk, s1_large, s2_small, s3_other, s4_fallow, s5_none],
[95., 0., 0., 0., 0., 5.])
model.add_transitions(
s1_large, [s0_unk, s1_large, s2_small, s3_other, s4_fallow, s5_none],
[0., 95., 0., 0., 0., 5.])
model.add_transitions(
s2_small, [s0_unk, s1_large, s2_small, s3_other, s4_fallow, s5_none],
[0., 0., 95., 0., 0., 5.])
model.add_transitions(
s3_other, [s0_unk, s1_large, s2_small, s3_other, s4_fallow, s5_none],
[0., 0., 0., 95., 0., 5.])
model.add_transitions(
s4_fallow, [s0_unk, s1_large, s2_small, s3_other, s4_fallow, s5_none],
[0., 0., 0., 0., 95., 5.])
model.add_transitions(
s5_none, [s0_unk, s1_large, s2_small, s3_other, s4_fallow, s5_none],
[2., 2., 2., 2., 2., 90.])
model.bake(verbose=False)
return model
