def directional_variance_gradient_i(r_i, w):
""" computes the gradient of the directioanl variance for row_i in
data """
return [2 * Ch4.dot_product(r_i, direction(w)) * r_ij for r_ij in r_i]
def directional_variance_i(r_i, w):
"""" computes for row r_i the projection of r_i onto w """
# we square this because we later sum to get a resultant vector
return Ch4.dot_product(r_i, direction(w))**2
def project(v, w):
""" projects vector v onto w """
projection_length = Ch4.dot_product(v, w)
return Ch4.scalar_multiply(projection_length, w)
def directional_variance_gradient_i(r_i, w):
""" computes the gradient of the directioanl variance for row_i in
data """
return [2 * Ch4.dot_product(r_i, direction(w)) * r_ij for r_ij in r_i]
def directional_variance_i(r_i, w):
"""" computes for row r_i the projection of r_i onto w """
# we square this because we later sum to get a resultant vector
return Ch4.dot_product(r_i, direction(w))**2
def project(v,w):
""" projects vector v onto w """
projection_length = Ch4.dot_product(v,w)
return Ch4.scalar_multiply(projection_length,w)
