Python generate_naive示例

示例#1

文件： binam_data_occupancy.py 项目： apdavison/pynam

    results["title"].append(title)
    print

def cm2inch(value):
    return value/2.54

def plot():
    fig = plt.figure(figsize=(cm2inch(13), cm2inch(6)))
    ax = fig.add_subplot(111)
    bp = ax.boxplot(results["data"])
    plt.setp(bp['boxes'], color='black')
    plt.setp(bp['whiskers'], color='black')
    plt.setp(bp['fliers'], color='black', marker='+')
    plt.setp(bp['medians'], color='black')
    ax.set_xticklabels(results["title"])
    ax.set_ylim(0, np.max(results["data"]))
    ax.set_ylabel("Occupancy")
    plt.savefig('out/binam_occupancy_data.pdf', format='pdf',
        bbox_inches='tight')

calculate(lambda: binam_data.generate_random(n_bits, n_ones, n_samples),
    "Random\n(with duplicates)")
calculate(lambda: binam_data.generate(n_bits, n_ones, n_samples, balance=False),
    "Random\n(no duplicates)")
calculate(lambda: binam_data.generate_naive(n_bits, n_ones, n_samples),
    "Balanced\n(with duplicates)")
calculate(lambda: binam_data.generate(n_bits, n_ones, n_samples),
    "Balanced\n(no duplicates)")
plot()

示例#2

def cm2inch(value):
    return value / 2.54


def plot():
    fig = plt.figure(figsize=(cm2inch(13), cm2inch(6)))
    ax = fig.add_subplot(111)
    bp = ax.boxplot(results["data"])
    plt.setp(bp['boxes'], color='black')
    plt.setp(bp['whiskers'], color='black')
    plt.setp(bp['fliers'], color='black', marker='+')
    plt.setp(bp['medians'], color='black')
    ax.set_xticklabels(results["title"])
    ax.set_ylim(0, np.max(results["data"]))
    ax.set_ylabel("Occupancy")
    plt.savefig('out/binam_occupancy_data.pdf',
                format='pdf',
                bbox_inches='tight')


calculate(lambda: binam_data.generate_random(n_bits, n_ones, n_samples),
          "Random\n(with duplicates)")
calculate(
    lambda: binam_data.generate(n_bits, n_ones, n_samples, balance=False),
    "Random\n(no duplicates)")
calculate(lambda: binam_data.generate_naive(n_bits, n_ones, n_samples),
          "Balanced\n(with duplicates)")
calculate(lambda: binam_data.generate(n_bits, n_ones, n_samples),
          "Balanced\n(no duplicates)")
plot()