def test_straightforward(self): x, y, z = floats("xyz") e = mul(add(x, y), true_div(x, y)) C = Composite([x, y], [e]) c = C.make_node(x, y) # print c.c_code(['x', 'y'], ['z'], dict(id = 0)) g = FunctionGraph([x, y], [c.out]) fn = DualLinker().accept(g).make_function() assert fn(1.0, 2.0) == 1.5
def test_with_constants(self): x, y, z = floats("xyz") e = mul(add(70.0, y), true_div(x, y)) C = Composite([x, y], [e]) c = C.make_node(x, y) assert "70.0" in c.op.c_code(c, "dummy", ["x", "y"], ["z"], dict(id=0)) # print c.c_code(['x', 'y'], ['z'], dict(id = 0)) g = FunctionGraph([x, y], [c.out]) fn = DualLinker().accept(g).make_function() assert fn(1.0, 2.0) == 36.0
def test_true_div(self): # true_div's upcast policy is not exactly "upgrade_to_float", # so the test is a little bit different x_range = list(range(-127, 128)) y_range = list(range(-127, 0)) + list(range(1, 127)) xi = int8('xi') yi = int8('yi') xf = Scalar(theano.config.floatX)('xf') yf = Scalar(theano.config.floatX)('yf') ei = true_div(xi, yi) fi = theano.function([xi, yi], ei) ef = true_div(xf, yf) ff = theano.function([xf, yf], ef) for x_val in x_range: for y_val in y_range: outi = fi(x_val, y_val) outf = ff(x_val, y_val) assert outi.dtype == outf.dtype, 'incorrect dtype' assert np.allclose(outi, outf), 'insufficient precision'
def test_mul_add_true(): x, y, z = floats("xyz") e = mul(add(x, y), true_div(x, y)) g = FunctionGraph([x, y], [e]) fn = DualLinker().accept(g).make_function() assert fn(1.0, 2.0) == 1.5