def sim_mat(x, y):
    """ mtype: cdd, ks, dm3, dm4 """
    """ cdd: combined CDF dice """
    """ ks: combined CDF K-S """
    """ dm3: dice with 3 moments, 1 moment and 2 central moments """
    """ dm4: dice with 4 moments, 1 moment and 3 central moments """

    global mtype
    sim_idx = float("NaN")
    try:
        if mtype == None or mtype == "ks":
            sim_idx = simple_ks(x, y)
        elif mtype == "cdd":
            sim_idx = cdf_dice_metric(x, y)
        elif mtype == "dm3":
            sim_idx = moment_dice_metric(x, y)
        elif mtype == "dm4":
            sim_idx = moment_dice_metric(x, y, fourthmoment=True)
        elif mtype == "nm":
            sim_idx = newmetric(x, y)
        elif mtype == "invcdf":
            sim_idx = new_similarity_invcdf(x, y)

        return sim_idx
    except BaseException, be:
        print "Exception -> ", be
        return float("NaN")
Example #2
0
def simexp(nvars):
    nvar1 = nvars[0]
    nvar2 = nvars[1]

    r = SystemRandom()
    npr.seed(r.randint(0, 1e15))
    x = npr.exponential(30, nvar1)
    npr.seed(r.randint(0, 1e15))
    y = npr.exponential(35, nvar2)

    if False:
        x.sort()
        y.sort()

        darray = np.zeros(100)
        for i in xrange(0, 100):
            yprime = r.sample(y, len(x))
            yprime.sort()
            darray[i] = util.dice(x, yprime)
            return max_conf_est(darray, 0.99)

    return new_similarity_invcdf(x, y)
Example #3
0
def simpareto(nvars):
    nvar1 = nvars[0]
    nvar2 = nvars[1]

    p1 = Pareto(2000.0, 2.0)
    p2 = Pareto(2000.0, 1.0)

    x = p1.rnd(nvar1)
    y = p2.rnd(nvar2)

    x.sort()
    y.sort()

    if False:
        r = SystemRandom()

        darray = np.zeros(100)
        for i in xrange(0, 100):
            yprime = r.sample(y, len(x))
            darray[i] = util.dice(x, yprime)
            return max_conf_est(darray, 0.99)

    return new_similarity_invcdf(x, y)
Example #4
0
def simpareto(nvars):
	nvar1 = nvars[0]
	nvar2 = nvars[1]

	p1 = Pareto(2000.0, 2.0)
	p2 = Pareto(2000.0, 1.0)

	x = p1.rnd(nvar1)
	y = p2.rnd(nvar2)

	x.sort()
	y.sort()
	
	if False:
		r = SystemRandom()

		darray = np.zeros(100)
		for i in xrange(0,100):
			yprime = r.sample(y, len(x))
			darray[i] = util.dice(x,yprime)
			return max_conf_est(darray, 0.99)

	return new_similarity_invcdf(x, y)
Example #5
0
def simexp(nvars):
	nvar1 = nvars[0]
	nvar2 = nvars[1]

	r = SystemRandom()
	npr.seed(r.randint(0,1e15))
	x = npr.exponential(30, nvar1)
	npr.seed(r.randint(0,1e15))
	y = npr.exponential(35,nvar2)
	
	if False:
		x.sort()
		y.sort()
	
		darray = np.zeros(100)
		for i in xrange(0,100):
			yprime = r.sample(y, len(x))
			yprime.sort()
			darray[i] = util.dice(x,yprime)
			return max_conf_est(darray, 0.99)


	return new_similarity_invcdf(x,y)
Example #6
0
def sim_mat(x, y, mtype):
    """ mtype: cdd, ks, dm3, dm4 """
    """ cdd: combined CDF dice """
    """ ks: combined CDF K-S """
    """ dm3: dice with 3 moments, 1 moment and 2 central moments """
    """ dm4: dice with 4 moments, 1 moment and 3 central moments """

    sim_idx = 0.0
    if mtype == None or mtype == "ks":
        sim_idx = simple_ks(x, y)
    elif mtype == "cdd":
        sim_idx = cdf_dice_metric(x, y)
    elif mtype == "dm3":
        sim_idx = moment_dice_metric(x, y)
    elif mtype == "dm4":
        sim_idx = moment_dice_metric(x, y, fourthmoment=True)
    elif mtype == "nm":
        sim_idx = newmetric(x, y)
    elif mtype == "sdice":
        sim_idx = simple_dice(x, y)
    elif mtype == "invcdf":
        sim_idx = new_similarity_invcdf(x, y)

    return sim_idx
Example #7
0
def sim_mat(x,y,mtype):
	""" mtype: cdd, ks, dm3, dm4 """
	""" cdd: combined CDF dice """
	""" ks: combined CDF K-S """
	""" dm3: dice with 3 moments, 1 moment and 2 central moments """
	""" dm4: dice with 4 moments, 1 moment and 3 central moments """
	
	sim_idx = 0.0
	if mtype == None or mtype == "ks":
		sim_idx = simple_ks(x,y)
	elif mtype == "cdd":
		sim_idx = cdf_dice_metric(x,y)
	elif mtype == "dm3":
		sim_idx = moment_dice_metric(x,y)
	elif mtype == "dm4":
		sim_idx = moment_dice_metric(x,y,fourthmoment=True)
	elif mtype == "nm":
		sim_idx = newmetric(x,y)
	elif mtype == "sdice":
		sim_idx = simple_dice(x,y)
	elif mtype == "invcdf":
		sim_idx = new_similarity_invcdf(x,y)

	return sim_idx