def score_funct(coeff):
"""
Calculate the score with the specified coefficients. Use MSE error calculation.
@param coeff: The coefficients.
@return: The score. We are trying to minimize this score.
"""
global input_data
global output_data
# Calculate the actual output of the polynomial with the specified coefficients.
actual_output = []
for input_data in training_input:
x = input_data[0]
output_data = poly(coeff, x)
actual_output.append(output_data)
return ErrorCalculation.sse(np.array(actual_output), training_ideal)
def score_funct(coeff):
"""
Calculate the score with the specified coefficients. Use MSE error calculation.
@param coeff: The coefficients.
@return: The score. We are trying to minimize this score.
"""
global input_data
global output_data
# Calculate the actual output of the polynomial with the specified coefficients.
actual_output = []
for input_data in training_input:
x = input_data[0]
output_data = poly(coeff, x)
actual_output.append(output_data)
return ErrorCalculation.sse(np.array(actual_output), training_ideal)
for row in xrange(0, rows):
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'])
for row in xrange(0, rows):
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'])
