예제 #1
0
def run_test(argsin):
    n_rows = args["num_rows"]
    n_iters = args["num_iters"]
    n_chains = args["num_chains"]
    ct_kernel = args["ct_kernel"]

    fig = pylab.figure(num=None, facecolor='w', edgecolor='k',frameon=False, tight_layout=True)


    plt = 0
    data = {'x':[], 'sin':[], 'ring':[], 'dots':[]}
    xlims = dict()
    ylims = dict()
    for shape in shapes:
        plt += 1
        data[shape] = gen_function[shape](n_rows)

        ax = pylab.subplot(n_chains+1,4,plt)
        pylab.scatter( data[shape][0], data[shape][1], s=10, color='blue', edgecolor='none', alpha=.2 )
        # pylab.ylabel("X")
        # pylab.ylabel("Y")
        # pylab.title("%s original" % shape)

        ax.set_xticks([])
        ax.set_yticks([])
        pylab.suptitle( "Kernel %i" % ct_kernel)

        xlims[shape] = ax.get_xlim()
        ylims[shape] = ax.get_ylim()

    States = []
    for chain in range(n_chains):
        print("chain %i of %i." % (chain+1, n_chains))
        plt = 0
        for shape in shapes:
            print("\tWorking on %s." % shape)
            plt += 1
            T = data[shape]
            S = cc_state.cc_state(T, cctypes, ct_kernel=ct_kernel, distargs=distargs)
            S.transition(N=n_iters)
            T_chain = numpy.array(su.simple_predictive_sample(S, n_rows, [0,1], N=n_rows))

            ax = pylab.subplot(n_chains+1,4,chain*4+4+plt)
            ax.set_xticks([])
            ax.set_yticks([])
            pylab.scatter( T_chain[:,0], T_chain[:,1], s=10, color='red', edgecolor='none', alpha=.2 )
            pylab.xlim(xlims[shape])
            pylab.ylim(ylims[shape])
            # pylab.title("%s simulated (%i)" % (shape, chain))

    print("Done.")
    pylab.show()
def run_test(argsin):
    n_rows = args["num_rows"]
    n_iters = args["num_iters"]
    n_chains = args["num_chains"]

    n_per_chain = int(float(n_rows) / n_chains)

    plt = 0
    for shape in shapes:
        print "Shape: %s" % shape
        plt += 1
        T_o = gen_function[shape](n_rows)
        T_i = []
        for chain in range(n_chains):
            print "chain %i of %i" % (chain + 1, n_chains)
            S = cc_state.cc_state(T_o, cctypes, ct_kernel=1, distargs=distargs)
            S.transition(N=n_iters)

            T_i.extend(
                su.simple_predictive_sample(S, n_rows, [0, 1], N=n_per_chain))

        T_i = numpy.array(T_i)

        ax = pylab.subplot(2, 4, plt)
        pylab.scatter(T_o[0], T_o[1], color='blue', edgecolor='none')
        pylab.ylabel("X")
        pylab.ylabel("Y")
        pylab.title("%s original" % shape)

        pylab.subplot(2, 4, plt + 4)
        pylab.scatter(T_i[:, 0], T_i[:, 1], color='red', edgecolor='none')
        pylab.ylabel("X")
        pylab.ylabel("Y")
        pylab.xlim(ax.get_xlim())
        pylab.ylim(ax.get_ylim())
        pylab.title("%s simulated" % shape)

    print "Done."
    pylab.show()
def run_test(argsin):
    n_rows = args["num_rows"]
    n_iters = args["num_iters"]
    n_chains = args["num_chains"]

    n_per_chain = int(float(n_rows) / n_chains)

    plt = 0
    for shape in shapes:
        print "Shape: %s" % shape
        plt += 1
        T_o = gen_function[shape](n_rows)
        T_i = []
        for chain in range(n_chains):
            print "chain %i of %i" % (chain + 1, n_chains)
            S = cc_state.cc_state(T_o, cctypes, ct_kernel=1, distargs=distargs)
            S.transition(N=n_iters)

            T_i.extend(su.simple_predictive_sample(S, n_rows, [0, 1], N=n_per_chain))

        T_i = numpy.array(T_i)

        ax = pylab.subplot(2, 4, plt)
        pylab.scatter(T_o[0], T_o[1], color="blue", edgecolor="none")
        pylab.ylabel("X")
        pylab.ylabel("Y")
        pylab.title("%s original" % shape)

        pylab.subplot(2, 4, plt + 4)
        pylab.scatter(T_i[:, 0], T_i[:, 1], color="red", edgecolor="none")
        pylab.ylabel("X")
        pylab.ylabel("Y")
        pylab.xlim(ax.get_xlim())
        pylab.ylim(ax.get_ylim())
        pylab.title("%s simulated" % shape)

    print "Done."
    pylab.show()
예제 #4
0
def run_test(argsin):
    n_rows = args["num_rows"]
    n_iters = args["num_iters"]
    n_chains = args["num_chains"]
    ct_kernel = args["ct_kernel"]

    fig = pylab.figure(num=None,
                       facecolor='w',
                       edgecolor='k',
                       frameon=False,
                       tight_layout=True)

    plt = 0
    data = {'x': [], 'sin': [], 'ring': [], 'dots': []}
    xlims = dict()
    ylims = dict()
    for shape in shapes:
        plt += 1
        data[shape] = gen_function[shape](n_rows)

        ax = pylab.subplot(n_chains + 1, 4, plt)
        pylab.scatter(data[shape][0],
                      data[shape][1],
                      s=10,
                      color='blue',
                      edgecolor='none',
                      alpha=.2)
        # pylab.ylabel("X")
        # pylab.ylabel("Y")
        # pylab.title("%s original" % shape)

        ax.set_xticks([])
        ax.set_yticks([])
        pylab.suptitle("Kernel %i" % ct_kernel)

        xlims[shape] = ax.get_xlim()
        ylims[shape] = ax.get_ylim()

    States = []
    for chain in range(n_chains):
        print("chain %i of %i." % (chain + 1, n_chains))
        plt = 0
        for shape in shapes:
            print("\tWorking on %s." % shape)
            plt += 1
            T = data[shape]
            S = cc_state.cc_state(T,
                                  cctypes,
                                  ct_kernel=ct_kernel,
                                  distargs=distargs)
            S.transition(N=n_iters)
            T_chain = numpy.array(
                su.simple_predictive_sample(S, n_rows, [0, 1], N=n_rows))

            ax = pylab.subplot(n_chains + 1, 4, chain * 4 + 4 + plt)
            ax.set_xticks([])
            ax.set_yticks([])
            pylab.scatter(T_chain[:, 0],
                          T_chain[:, 1],
                          s=10,
                          color='red',
                          edgecolor='none',
                          alpha=.2)
            pylab.xlim(xlims[shape])
            pylab.ylim(ylims[shape])
            # pylab.title("%s simulated (%i)" % (shape, chain))

    print("Done.")
    pylab.show()