def first_principal_component_SGD(data): """ Uses SGD method to determine the direction that maximizes the directional_variance gradient """ guess = data[0] unscaled_maximizer, _ = Ch8.maximize_stochastic(lambda x,_,w: directional_variance_i(x,w), lambda x,_,w: directional_variance_gradient_i(x,w), data, [None for _ in data], guess) return direction(unscaled_maximizer)
def first_principal_component_SGD(data): """ Uses SGD method to determine the direction that maximizes the directional_variance gradient """ guess = data[0] unscaled_maximizer, _ = Ch8.maximize_stochastic( lambda x, _, w: directional_variance_i(x, w), lambda x, _, w: directional_variance_gradient_i(x, w), data, [None for _ in data], guess) return direction(unscaled_maximizer)
def first_principal_component(data): guess = [1 for _ in data[0]] # use partial to make the target and grade fncs a variable of w only unscaled_maximizer, _, _ = Ch8.maximize_batch( partial(directional_variance, data), partial(directional_variance_gradient, data), guess,tolerance = 0.00001) return direction(unscaled_maximizer)
def first_principal_component(data): guess = [1 for _ in data[0]] # use partial to make the target and grade fncs a variable of w only unscaled_maximizer, _, _ = Ch8.maximize_batch( partial(directional_variance, data), partial(directional_variance_gradient, data), guess, tolerance=0.00001) return direction(unscaled_maximizer)