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DriftAnalyses.py
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#!/usr/bin/env python
import math
import numpy
from numpy.linalg import linalg
#----------------------------------
# Compute the distribution of Mean
#----------------------------------
def ComputeMean(x_axis, y_axis):
xn_array = numpy.array(x_axis)
yn_array = numpy.array(y_axis)
xn_mean = []
yn_mean = []
mean_x = numpy.mean(xn_array)
mean_y = numpy.mean(yn_array)
xn_mean.append(mean_x)
yn_mean.append(mean_y)
return mean_x, mean_y
#---------------------------------
# Compute the Standard Deviation
#---------------------------------
def ComputeSD(x_axis, y_axis):
xn_array = numpy.array(x_axis)
yn_array = numpy.array(y_axis)
xn_std_dev = []
yn_std_dev = []
std_dev_x = numpy.std(xn_array)
std_dev_y = numpy.std(yn_array)
xn_std_dev.append(std_dev_x)
yn_std_dev.append(std_dev_y)
return std_dev_x, std_dev_y
#----------------------------------------------------------------
# Compute Eigen values and Eigen vector of the Covariance Matrix
#----------------------------------------------------------------
def ComputeEigenValues(x_axis, y_axis):
eigen_value = []
dominant_angle = []
X = numpy.vstack((x_axis, y_axis))
# Compute the Covariance Matrix
#print X
cov_matrix = numpy.cov(X)
# Compute eigen value
eigval, eigvec = linalg.eig(cov_matrix)
eigval_list = eigval.tolist()
eigval = sorted(eigval, key=abs, reverse=True)
eigen_value.extend(eigval)
# Compute angle of dominant eigen vector with x-axis
dominant_pos = eigval_list.index(max(eigval_list, key=abs))
if eigvec[dominant_pos][0] == 0:
if eigvec[dominant_pos][1] < 0:
angle = -90
else:
angle = 90
else:
angle = math.degrees(math.atan(eigvec[dominant_pos][1] / eigvec[dominant_pos][0]))
dominant_angle.append(angle)
return eigval, angle
#----------------------------------------------------------------
# Different analyses
#----------------------------------------------------------------
def DriftAnalyses(x_axis, y_axis):
#Max & min
x_min = min(x_axis)
x_max = max(x_axis)
y_min = min(y_axis)
y_max = max(y_axis)
drift_min = [x_min, y_min]
drift_max = [x_max, y_max]
# Centroid
xn_mean, yn_mean = ComputeMean(x_axis, y_axis)
drift_mean = [xn_mean, yn_mean]
# Standard Deviation
xn_std_dev, yn_std_dev = ComputeSD(x_axis, y_axis)
drift_std_dev = [xn_std_dev, yn_std_dev]
# Compute Eigen values and angle of dominant eigen vector of the Covariance Matrix
eigenvalues, angle_eigenvector = ComputeEigenValues(x_axis, y_axis)
# Start point and end point
if x_max == x_min:
mu_S_x = 'inf'
else:
mu_S_x = (x_axis[0]-x_min)*1.0/(x_max-x_min)
if y_max == y_min:
mu_S_y = 'inf'
else:
mu_S_y = (y_axis[0]-y_min)*1.0/(y_max-y_min)
mu_S = [mu_S_x, mu_S_y]
if x_max == x_min:
mu_E_x = 'inf'
else:
mu_E_x = (x_axis[-1]-x_min)*1.0/(x_max-x_min)
if y_max == y_min:
mu_E_y = 'inf'
else:
mu_E_y = (y_axis[-1]-y_min)*1.0/(y_max-y_min)
mu_E = [mu_E_x, mu_E_y]
# Horizontal and vertical motion
x_temp = sum([abs(x_axis[i]-x_axis[i+1]) for i in range(len(x_axis)-1)])
if x_temp == 0:
mu_HMN = 'inf'
else:
mu_HMN = (x_max - x_min)*1.0/x_temp
y_temp = sum([abs(y_axis[i]-y_axis[i+1]) for i in range(len(y_axis)-1)])
if y_temp == 0:
mu_VMN = 'inf'
else:
mu_VMN = (y_max - y_min)*1.0/y_temp
# Aspect ratio
if y_max == y_min and x_max == x_min:
mu_AR = 'inf'
else:
mu_AR = (y_max - y_min)*1.0/(math.sqrt((y_max-y_min)**2 + (x_max-x_min)**2))
# Arcedness and straightness
if y_max == y_min and x_max == x_min:
arc = 'inf'
else:
arc = len(x_axis) - math.sqrt((y_max-y_min)**2 + (x_max-x_min)**2)
return xn_mean, yn_mean, eigenvalues, angle_eigenvector
#----------------------------------------------------------------------------------------------------
def Analysis(drift, NO_OF_FRAGMENTS):
# Drift
drift_feature_vector = []
drift_xn_mean = []
drift_yn_mean = []
drift_eigenvalues = []
drift_angle_eigenvector = []
seq_len = len(drift)
frag_size = round(2*math.log(seq_len, 4)+.5)
start_end = ComputeStartEnd(frag_size, NO_OF_FRAGMENTS, seq_len)
# print(start_end)
dft = list(zip(*drift))
drift_x, drift_y = list(dft[0]), list(dft[1])
for start, end in start_end:
# collect x axis data of Drift
frag_x_axis = drift_x[start:end]
# collect y axis data of Drift
frag_y_axis = drift_y[start:end]
xn_mean, yn_mean, eigenvalues, angle_eigenvector = DriftAnalyses(frag_x_axis, frag_y_axis)
drift_feature_vector.append([xn_mean, yn_mean, eigenvalues[0], eigenvalues[1], angle_eigenvector])
return drift_feature_vector
#---------------------------------------------------------------------------------------------------
# Compute the start and end point of fragments
#---------------------------------------------------------------------------------------------------
def ComputeStartEnd(frag_size, no_of_frag, seq_len):
start = 0
end = frag_size
extra = frag_size * no_of_frag - seq_len
overlap = round(extra*1.0/(no_of_frag-1))
start_end = []
start_end.append([start, end])
for i in range(1,no_of_frag-1):
start = end-overlap
end = min(start + frag_size, seq_len-1)
extra = frag_size*(no_of_frag-i) - (seq_len-start)
overlap = round(extra*1.0/(no_of_frag-i-1)+0.5)
start_end.append([start, end])
start = end-overlap
end = min(start + frag_size, seq_len-1)
start_end.append([start, end])
return start_end