def compute_one( p, n, n_alt_plus, n_alt_minus, ): B = simple.Simple(n=n, p=p).flatten() Bplus = simple.Simple(n=n_alt_plus, p=p).flatten() Bminus = simple.Simple(n=n_alt_minus, p=p).flatten() print "Computing Plus Alternative" SPlus = compute_alt(B, Bplus) print "Computing Minus Alternative" SMinus = compute_alt(B, Bminus) return SPlus, SMinus
def SfNplot(): ntrue = 100 nalt0 = 99 nalt1 = 90 S100_1 = simple.Simple(n=ntrue, p=.1).flatten() S99_1 = simple.Simple(n=nalt0, p=.1).flatten() S90_1 = simple.Simple(n=nalt1, p=.1).flatten() S100_5 = simple.Simple(n=ntrue, p=.5).flatten() S99_5 = simple.Simple(n=nalt0, p=.5).flatten() S90_5 = simple.Simple(n=nalt1, p=.5).flatten() S100_9 = simple.Simple(n=ntrue, p=.9).flatten() S99_9 = simple.Simple(n=nalt0, p=.9).flatten() S90_9 = simple.Simple(n=nalt1, p=.9).flatten() fig, ax = my_subplots_for_sfn() S100_1.compare_3bars(S99_1, S90_1, ntrue, (fig, ax[2][0]), xlab='Number of open channels (k)') S100_5.compare_3bars(S99_5, S90_5, ntrue, (fig, ax[1][0])) S100_9.compare_3bars(S99_9, S90_9, ntrue, (fig, ax[0][0])) se_regions(2, False, (fig, ax[0][1])) se_regions(1, False, (fig, ax[1][1])) se_regions(0, False, (fig, ax[2][1])) se_regions(2, True, (fig, ax[0][1])) se_regions(1, True, (fig, ax[1][1])) se_regions(0, True, (fig, ax[2][1])) venn((30, .1), .1, .05, (fig, ax[0][0])) ax[0][0].text(18, .085, 'n-10%=90') ax[0][0].text(41, .105, 'n=100') ax[0][0].text(4.5, .105, 'n-1=99') ax[0][0].text(5, .12, 'Number of Channels:') ax[0][0].text(5, .13, 'Probability of Opening: p=0.9') ax[1][0].text(5, .13, 'Probability of Opening: p=0.5') ax[2][0].text(20, .13, 'Probability of Opening: p=0.1') ax[0][1].text(5, 130, 'Probability of Opening: p=0.9') ax[0][1].text(5, 120, 'Number of Channels in Alternative:') ax[0][1].text(10, 110, '(Falsified with 95% Confidence)') ax[1][1].text(5, 1300, 'Probability of Opening: p=0.5') ax[2][1].text(5, 13000, 'Probability of Opening: p=0.1') ax[0][1].set_ylabel('Needed Sample Size') ax[1][1].set_ylabel('Needed Sample Size') ax[2][1].set_ylabel('Needed Sample Size') ax[2][1].set_xlabel('Number of Channels in True Model (n)') ax[0][1].text(5, 90, 'n-1') ax[0][1].text(5, 72.5, 'n-10%') ax[0][1].text(43, 90, 'n+1') ax[0][1].text(43, 72.5, 'n+10%') venn2((29, 90), .1, .06, (fig, ax[0][1])) fig.show() return fig, ax
def test_hello_world(): import simple os.chdir('/tmp') assert simple.Simple().hello_world() == 'Hello world!'
class Simple2: def __init__(self): self.info = "SimpleClass2" class Simple3(simple.Simple): def __init__(self): simple.Simple.__init__(self) text = "text in simple" assert simple.text == text _s = simple.Simple() _s3 = Simple3() assert _s.info == _s3.info import recursive_import _s = recursive_import.myClass() assert str(_s) == "success!" import from_import_test.b assert from_import_test.b.v == 1 import from_import_test.c assert from_import_test.c.v == 1 # test of keyword "global" in functions of an imported module
# issue 44 funcs = [] for i in [1, 2]: def f(x=i): return x funcs.append(f) assert funcs[0]() == 1 assert funcs[1]() == 2 # issue 45 import simple assert simple.Simple().info == "SimpleClass" # issue 46 class A: COUNTER = 0 def __init__(self): self.COUNTER += 1 a = A() class A:
def test_hello_world(self): import simple self.assertEqual(simple.Simple().hello_world('!'), 'Hello world!')
def test_patch(self, patched_hw): import simple simple.Simple().hello_world('!') patched_hw.assert_called_once_with('!')
def tearDown(self): simple.Simple().finishUp()
def setUp(self): simple.Simple().getReady()
sy_loss = - tf.reduce_mean(sy_states[-1], name="loss") sy_trainable = [neg] sy_opt = tf.train.GradientDescentOptimizer(sy_lr, name="GDOpt") sy_train_op = sy_opt.minimize(sy_loss, var_list=sy_trainable) return {"cl_in": x, "cl_label": y, "cl_logit": states[-1], "cl_prob": cl_prob, "cl_out": cl_out, "cl_op": cl_train_op, "cl_loss": cl_loss, "cl_acc": accuracy, "sy_sample": neg, "sy_op": sy_train_op, "sy_logit": sy_states[-1], "sy_loss": sy_loss} if __name__ == "__main__": m = build_arch() d = simple.Simple() sess = tf.Session() sess.run(tf.global_variables_initializer()) neg_data_seq = [sess.run(m["sy_sample"])] for epoch in range(151): d.set_neg(neg_data_seq[-1]) plt.plot(neg_data_seq[-1][:,0], neg_data_seq[-1][:,1], ".") plt.xlim([-10, 10]) plt.ylim([-10, 10]) plt.savefig("figure/"+str(epoch)+".jpg") plt.show() for i in range(2000):