TDD is one of the most important milestones in the history of software development. TDD primarily focused on automated unit testing, its goal is to maximize automated test code. If the code is changed, we can still run the test and catch possible problems. In other words, the test is still valid for the function modules already exist.
Predicate function
Comparing two floating point numbers Size:
assert_approx_equal if the degree of approximation of the two numbers do not reach the specified valid number, throwing an exception
If the degree of approximation assert_array_almost_equal two arrays of elements does not reach the specified accuracy, throwing an exception
assert_array_equal if the two are not the same array object, throwing an exception
assert_array_less two arrays have the same shape, and the first array element is strictly less than the second element of the array, or to throw an exception
assert_equal If two objects are not the same, throwing an exception
assert_raises if called with the fill parameter is not specified exception is thrown, the test does not pass
assert_warns if not thrown specified warning, do not pass the test
two identical string variables asserted assert_string_equal
If the degree of approximation assert_allclose two objects exceeds the specified tolerance limits, will throw an exception
# Assert_allclose degree of approximation if two objects exceeds the specified tolerance limits, it is thrown from numpy.testing Import assert_allclose assert_allclose (np.sum (Q_I), . 1, ERR_MSG = " {} " .format (np.sum (q_i)))