def pkg_dtw(file_name, line_num, df):
file_name = 'data/' + file_name
y_list = load_data(file_name, line_num)
query, reference = cal_warped_signals(y_list)
# plot warped signal
# downsample times
xvals, yinterp = get_warped_signals(query, cf.ds_time)
# calculate the corresponding point pair
query.drop(['shift', 't'], axis=1)
query2 = pd.DataFrame({'t': xvals, 'q': yinterp})
query2['close_index'] = 0
true_align_dict = get_true_aligned(cf.ds_time, query, query2)
group_num_dict = get_group_number(true_align_dict, query)
d, cost_matrix, acc_cost_matrix, path = dtw(reference[['t', 'q']].values,
query2[['t', 'q']].values,
dist=norm)
fact_align_dict = get_fact_align(path)
reverse_dict = get_reverse_dict(path)
error_rate = get_k_accuracy(true_align_dict, fact_align_dict,
group_num_dict)
SS1 = get_SS1(fact_align_dict, cf.ds_time)
SS2 = get_SS2(fact_align_dict, reverse_dict, cf.ds_time)
df.loc[line_num] = [error_rate, SS1, SS2]
return df
def event_dtw(file_name, line_num, df):
file_name = 'data/' + file_name
y_list = load_data(file_name, line_num)
query, reference = cal_warped_signals(y_list)
reference['upslope'] = 0
reference['downslope'] = 0
# plot warped signal
# downsample times
xvals, yinterp = get_warped_signals(query, cf.ds_time)
# calculate the corresponding point pair
query.drop('shift', axis=1)
query.drop('t', axis=1)
query2 = pd.DataFrame({'t': xvals, 'q': yinterp})
query2['close_index'] = 0
query2['upslope'] = 0
query2['downslope'] = 0
true_align_dict = get_true_aligned(cf.ds_time, query, query2)
group_num_dict = get_group_number(true_align_dict, query)
raw_reference_uslope, reference_upslope = get_upslope_endings(
reference['q'], cf.refer_percent)
raw_query_uslope, query_upslope = get_upslope_endings(
query2['q'], cf.query_percent)
raw_reference_downlope, reference_downslope = get_downslope_endings(
reference['q'], cf.refer_percent)
raw_query_downlope, query_downslope = get_downslope_endings(
query2['q'], cf.query_percent)
rising_edge_grps = edge_matching(reference, query2, reference_upslope,
query_upslope)
down_edge_grps = edge_matching(reference, query2, reference_downslope,
query_downslope)
calculate_event(rising_edge_grps, reference, query2, True)
calculate_event(down_edge_grps, reference, query2, False)
d, cost_matrix, acc_cost_matrix, path = dtw(
reference[['t', 'q', 'upslope', 'downslope']].values,
query2[['t', 'q', 'upslope', 'downslope']].values,
dist=norm)
fact_align_dict = get_fact_align(path)
reverse_dict = get_reverse_dict(path)
error_rate = get_k_accuracy(true_align_dict, fact_align_dict,
group_num_dict)
SS1 = get_SS1(fact_align_dict, cf.ds_time)
SS2 = get_SS2(fact_align_dict, reverse_dict, cf.ds_time)
df.loc[line_num] = [error_rate, SS1, SS2]
return df
def pkg_shapedtw(file_name, line_num, df):
file_name = 'data/' + file_name
y_list = load_data(file_name, line_num)
query, reference = cal_warped_signals(y_list)
# plot warped signal
xvals, yinterp = get_warped_signals(query, cf.ds_time)
# normalize the signal
reference_norm = stats.zscore(reference['q'])
yinterp_norm = stats.zscore(yinterp)
# store the corresponding point pair
query.drop(['shift', 't'], axis=1)
query2 = pd.DataFrame({'t': xvals, 'q': yinterp})
query2['close_index'] = 0
true_align_dict = get_true_aligned(cf.ds_time, query, query2)
group_num_dict = get_group_number(true_align_dict, query)
refer_subsequences = samplingSequences(reference_norm, sub_len)
query_subsequences = samplingSequences(yinterp_norm, int(sub_len / cf.ds_time))
refer_descriptors = np.zeros((len(refer_subsequences), nBlocks * 8))
query_descriptors = np.zeros((len(query_subsequences), nBlocks * 8))
refer_nsubsequences = len(refer_subsequences)
query_nsubsequences = len(query_subsequences)
for i in range(refer_nsubsequences):
sub_seq = refer_subsequences[i]
refer_descriptors[i] = cal_descriptor(sub_seq, sub_len)
for i in range(query_nsubsequences):
sub_seq = query_subsequences[i]
query_descriptors[i] = cal_descriptor(sub_seq, int(sub_len / cf.ds_time))
d, cost_matrix, acc_cost_matrix, path = dtw(refer_descriptors, query_descriptors, dist=norm)
fact_align_dict = get_fact_align(path)
reverse_dict = get_reverse_dict(path)
error_rate = get_k_accuracy(true_align_dict, fact_align_dict, group_num_dict)
SS1 = get_SS1(fact_align_dict, cf.ds_time)
SS2 = get_SS2(fact_align_dict, reverse_dict, cf.ds_time)
df.loc[line_num] = [error_rate, SS1, SS2]
return df
import pandas as pd
import math
import numpy as np
from scipy import stats
from parameter_cal import cf
from dtw import dtw
from scipy.misc import *
from sdtw.config import sub_len, nBlocks
from sdtw.utils import norm, cal_refer_query_descriptor
from parameter_cal.utils import get_fact_align, get_reverse_dict, get_k_accuracy_same, get_W, get_SS2, get_true_align, get_link_graph
from parameter_cal.utils import load_data, plot_warped_signals, cal_warped_signals
import matplotlib.pyplot as plt
y_list = load_data(True)
query, reference = cal_warped_signals(y_list)
# plot warped signal
xvals, yinterp = plot_warped_signals(reference, query, 1)
# normalize the signal
reference_norm = stats.zscore(reference['q'])
yinterp_norm = stats.zscore(yinterp)
# store the corresponding point pair
query.drop('shift', axis=1)
query.drop('t', axis=1)
query2 = pd.DataFrame(yinterp)
query2['aligned_index'] = 0
query2['t'] = query['t']
query2.columns = ['q', 'aligned_index', 't']
from dtw import dtw
from downsample.utils import get_true_aligned, get_group_number
from parameter_cal.utils import get_SS2, get_group_devi, get_SS1, get_fact_align, get_reverse_dict, get_link_graph
from parameter_cal.utils import load_data, plot_warped_signals, cal_warped_signals
from downsample.utils import slope_col, reference_slope_col, get_k_accuracy
from debug.dbd_cf import debug_file, debug_line
def norm(x, y):
return math.fabs(x[1] - y[1])
# generate warped signal
y_list = load_data(debug_file, debug_line)
y_list = y_list[:-(len(y_list) % cf.ds_time)]
query, reference = cal_warped_signals(y_list, 'right')
# plot warped signal
# downsample times
xvals, yinterp = plot_warped_signals(reference, query, cf.ds_time)
query2 = pd.DataFrame({'t': xvals, 'q': yinterp})
# calculate the corresponding point pair
query.drop(['shift', 't'], axis=1)
query2['close_index'] = 0
true_align_dict = get_true_aligned(cf.ds_time, query, query2)
group_num_dict = get_group_number(true_align_dict, query)
reference_slope_col(reference, cf.ds_time)
slope_col(query2)
d, cost_matrix, acc_cost_matrix, path = dtw(reference[['t',