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FUNCTION CalculateCurveSimilarity(f, g, x_min, x_max, N, k):

    // Step 1: Define Evaluation Domain and Discretize

    step_size = (x_max - x_min) / (N - 1)

    

    CREATE x_values of size N

    CREATE y1_raw of size N

    CREATE y2_raw of size N

    

    FOR i FROM 0 TO N - 1:

        x_values[i] = x_min + (i * step_size)

        y1_raw[i] = f(x_values[i])

        y2_raw[i] = g(x_values[i])

    END FOR

    // Step 2: Evaluate and Normalize Outputs

    global_min = MIN(MIN(y1_raw), MIN(y2_raw))

    global_max = MAX(MAX(y1_raw), MAX(y2_raw))

    value_range = global_max - global_min

    

    IF value_range == 0 THEN value_range = 1  // Prevent division by zero

    CREATE y1_norm of size N

    CREATE y2_norm of size N

    

    FOR i FROM 0 TO N - 1:

        y1_norm[i] = (y1_raw[i] - global_min) / value_range

        y2_norm[i] = (y2_raw[i] - global_min) / value_range

    END FOR

    // Step 3: Calculate Error Metric (RMSE)

    sum_squared_diff = 0

    FOR i FROM 0 TO N - 1:

        diff = y1_norm[i] - y2_norm[i]

        sum_squared_diff = sum_squared_diff + (diff ^ 2)

    END FOR

    

    rmse = SQRT(sum_squared_diff / N)

    // Step 4: Convert Error to Similarity Percentage

    similarity_percentage = 100 * EXP(-k * rmse)

    

    RETURN similarity_percentage

END FUNCTION