d = float(np.random.randint(low, high))
ideal[row][col] = d
actual[row][col] = d + (np.random.normal() * distort)
return result
# Generate data sets.
smallErrors = generate(SEED, ROWS, COLS, LOW, HIGH, 0.1)
mediumErrors = generate(SEED, ROWS, COLS, LOW, HIGH, 0.5)
largeErrors = generate(SEED, ROWS, COLS, LOW, HIGH, 1.0)
hugeErrors = generate(SEED, ROWS, COLS, LOW, HIGH, 10.0)
small_sse = ErrorCalculation.sse(smallErrors['actual'], smallErrors['ideal'])
small_mse = ErrorCalculation.mse(smallErrors['actual'], smallErrors['ideal'])
small_rms = ErrorCalculation.rms(smallErrors['actual'], smallErrors['ideal'])
medium_sse = ErrorCalculation.sse(mediumErrors['actual'],
mediumErrors['ideal'])
medium_mse = ErrorCalculation.mse(mediumErrors['actual'],
mediumErrors['ideal'])
medium_rms = ErrorCalculation.rms(mediumErrors['actual'],
mediumErrors['ideal'])
large_sse = ErrorCalculation.sse(largeErrors['actual'], largeErrors['ideal'])
large_mse = ErrorCalculation.mse(largeErrors['actual'], largeErrors['ideal'])
large_rms = ErrorCalculation.rms(largeErrors['actual'], largeErrors['ideal'])
huge_sse = ErrorCalculation.sse(hugeErrors['actual'], hugeErrors['ideal'])
huge_mse = ErrorCalculation.mse(hugeErrors['actual'], hugeErrors['ideal'])
huge_rms = ErrorCalculation.rms(hugeErrors['actual'], hugeErrors['ideal'])
def test_rms(self):
actual = np.array(TestError.ACTUAL)
ideal = np.array(TestError.IDEAL)
self.assertAlmostEqual(ErrorCalculation.rms(actual, ideal), 12.3134, 3)
for col in xrange(0, cols):
d = float(np.random.randint(low, high))
ideal[row][col] = d
actual[row][col] = d + (np.random.normal() * distort)
return result
# Generate data sets.
smallErrors = generate(SEED, ROWS, COLS, LOW, HIGH, 0.1)
mediumErrors = generate(SEED, ROWS, COLS, LOW, HIGH, 0.5)
largeErrors = generate(SEED, ROWS, COLS, LOW, HIGH, 1.0)
hugeErrors = generate(SEED, ROWS, COLS, LOW, HIGH, 10.0)
small_sse = ErrorCalculation.sse(smallErrors['actual'], smallErrors['ideal'])
small_mse = ErrorCalculation.mse(smallErrors['actual'], smallErrors['ideal'])
small_rms = ErrorCalculation.rms(smallErrors['actual'], smallErrors['ideal'])
medium_sse = ErrorCalculation.sse(mediumErrors['actual'], mediumErrors['ideal'])
medium_mse = ErrorCalculation.mse(mediumErrors['actual'], mediumErrors['ideal'])
medium_rms = ErrorCalculation.rms(mediumErrors['actual'], mediumErrors['ideal'])
large_sse = ErrorCalculation.sse(largeErrors['actual'], largeErrors['ideal'])
large_mse = ErrorCalculation.mse(largeErrors['actual'], largeErrors['ideal'])
large_rms = ErrorCalculation.rms(largeErrors['actual'], largeErrors['ideal'])
huge_sse = ErrorCalculation.sse(hugeErrors['actual'], hugeErrors['ideal'])
huge_mse = ErrorCalculation.mse(hugeErrors['actual'], hugeErrors['ideal'])
huge_rms = ErrorCalculation.rms(hugeErrors['actual'], hugeErrors['ideal'])
print("Type\tSSE\t\t\tMSE\t\tRMS")
print("Small\t" + str(int(small_sse)) + "\t\t" + "{0:.2f}".format(small_mse) + "\t" + "{0:.2f}".format(small_rms))
def test_rms(self):
actual = np.array(TestError.ACTUAL)
ideal = np.array(TestError.IDEAL)
self.assertAlmostEqual(ErrorCalculation.rms(actual, ideal), 12.3134, 3)
