class MyAdaptiveFilter(BasicFilter):
def __init__(self, name, acc, trans, noits, meats, u0, v0_prob, wk_prob,
vk_prob, total_frame, param):
super().__init__(name, acc, trans, noits, meats, u0, v0_prob, wk_prob,
vk_prob, total_frame)
self.model = NNModel(param)
def fit(self,
X_train,
y_train,
is_valid=True,
save_model=False,
path=None):
self.model.fit(X_train, y_train, is_valid=is_valid)
if save_model:
self.model.save_model(path)
def predict(self, measure, TPM, *args, **kwargs):
load_model = kwargs['load_model']
path = kwargs['path']
if load_model:
self.model.load_model(path)
sample = measure.shape[0]
LH = np.empty([sample, 3], np.float32)
uk = np.empty([sample, 3], np.float32)
X = np.empty([sample, 3, 2], np.float32)
P = np.empty([sample, 3, 2, 2], np.float32)
mode = np.empty([sample, self.frame], np.float32)
state = np.empty([sample, self.frame, 2], np.float32)
his_TPM = np.empty([sample, self.frame, 3, 3], np.float32)
for i in range(sample):
mode[i], state[i], X[i], P[i], LH[i], uk[i] = self.initial_state(
measure[i, 0])
for k in range(1, self.frame):
for i in range(sample):
mode[i, k], state[i, k], X[i], P[i], LH[i], uk[i] = \
self.IMMFilter(measure[i, k], TPM[i], X[i], P[i], LH[i], uk[i])
X_test = np.concatenate([
np.zeros([sample, self.frame - 1 - k, 3], dtype=np.float32),
np.array([
mode[:, :k + 1] == 0, mode[:, :k + 1] == 1, mode[:, :k + 1]
== 2
],
dtype=np.float32).transpose([1, 2, 0])
],
axis=1).reshape([sample, -1])
TPM = self.model.predict(X_test)
his_TPM[:, k] = TPM
return state, mode, his_TPM
def load_model():
model = NNModel()
model.load_model("./model_51_file_training.h5")
print(model.model.summary())
return model
from nn_model import NNModel, ModelStateLogDTO
from experiment_datasets_creator import ExperimentCreator
from phonology_tool import PhonologyTool
MODEL_FILENAME = "../model_size_2_activation_sigmoid.pkl"
test_data_fn = "data/tur_swadesh.txt"
phonology_features_filename = "data/tur_phon_features.tsv"
model = NNModel.load_model(MODEL_FILENAME)
test_dataset = []
with open(test_data_fn, 'r', encoding="utf-8") as test_data_f:
for line in test_data_f:
if all(c in model.alphabet for c in line.strip()):
test_dataset.append(line.strip())
phonologyTool = PhonologyTool(phonology_features_filename)
experimentCreator = ExperimentCreator(model, test_dataset, phonologyTool)
# front_harmony_dataset
front_harmony_dataset_fn = "front_harmony_dataset.tsv"
front_harmony_dataset = experimentCreator.make_dataset_pretty(
experimentCreator.front_harmony_dataset())
experimentCreator.save_dataset_to_tsv(front_harmony_dataset,
front_harmony_dataset_fn)
# vov_vs_cons_dataset
vov_vs_cons_dataset_fn = "vov_vs_cons_dataset.tsv"
vov_vs_cons_dataset = experimentCreator.make_dataset_pretty(
experimentCreator.vov_vs_cons_dataset())
experimentCreator.save_dataset_to_tsv(vov_vs_cons_dataset,