import sys
sys.path.append("../")
from utils.utils import get_early_diff_path, read_data, pd
import matplotlib.pyplot as plt
from scipy.stats.mstats import zscore
sensors = ['tangential_strain']
for sensor in sensors:
print sensor
test_reader = pd.read_csv(get_early_diff_path("test_60_explosion"))
test_data, test_labels = read_data(test_reader, sensor, pre=True)
label = [
0, 1, 1, 2, 1, 0, 0, 0, 2, 1, 1, 2, 0, 4, 0, 2, 2, 1, 1, 1, 3, 2, 0, 3,
2, 0, 4, 1
]
high_possible_index = [
623, 637, 1335, 3892, 4475, 6916, 9754, 13150, 13548, 13801, 18367,
19010, 20799, 22174, 22564, 22927, 24272, 28318, 28360, 30189, 32744,
32861, 33364, 34895, 39462, 41735, 44862, 47834
]
deflation = [
0.0, -29.03, -32.7, -34.23, -21.6, 0.0, 0.0, 0.0, -12.19, -24.12,
-36.21, -18.16, 0.0, -24.12, 0.0, -33.67, -15.56, -27.93, -32.96,
-14.45, -28.35, -60.14, 0.0, -50.28, -29.79, 0.0, -50.28, -29.22
]
new_label = []
# Optimizer
# optimizer = tf.train.AdamOptimizer().minimize(loss)
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss)
# Prediction
prediction = tf.nn.sigmoid(logits)
"""
Data loading
"""
training_set = "training_60_explosion"
validation_set = "valid_60_explosion"
sensor = "radial_strain"
print("Data loading")
print(sensor)
training_reader = pd.read_csv(utils.get_early_diff_path(training_set))
# print len(training_reader)
# training_reader = training_reader[training_reader.current_labels == 0]
# print len(training_reader)
train_data, train_labels = utils.read_data(training_reader,
sensor,
num_steps=config.num_steps,
dim=config.input_size,
pre=True)
validation_reader = pd.read_csv(utils.get_early_diff_path(validation_set))
# print len(validation_reader)
# validation_reader = validation_reader[validation_reader.current_labels == 0]
# print len(validation_reader)
validation_data, validation_labels = utils.read_data(
validation_reader,