<jupyter_start><jupyter_text>计算指标！<jupyter_code>import numpy as np 
arr = [0.45, 1.138]
 
# 求均值
arr_mean = np.mean(arr)
 
# 求方差
arr_var = np.var(arr)
 
# 求总体标准差
arr_std_1 = np.std(arr)
 
# 求样本标准差
arr_std_2 = np.std(arr, ddof=1)
 
print("平均值为：%f" % arr_mean)
print("方差为：%f" % arr_var)
f1 = [0.792,0.572]
ma = [0.761,0.459]
mle = [0.496,1.113]
for i in range(len(f1)//2):
    arr = [f1[i*2],f1[i*2+1]]
    # 求均值
    arr_mean = np.mean(arr)

    # 求方差
    arr_var = np.var(arr)
    print("f1: mean",arr_mean, "std: ", arr_var)
for i in range(len(ma)//2):
    arr = [ma[i*2],ma[i*2+1]]
    # 求均值
    arr_mean = np.mean(arr)

    # 求方差
    arr_var = np.var(arr)
    print("ma: mean",arr_mean, "std: ", arr_var)
for i in range(len(mle)//2):
    arr = [mle[i*2],mle[i*2+1]]
    # 求均值
    arr_mean = np.mean(arr)

    # 求方差
    arr_var = np.var(arr)
    print("mle: mean",arr_mean, "std: ", arr_var)
pred = np.array([[0,1],
               [0,2],
               [0,3]])
gt = np.array([[1,1],
              [1,2]])
diff = np.expand_dims(pred, axis=1) - np.expand_dims(gt, axis=0)      # (N1,1,2) - (1,N2,2)   -> (N1,N2,2) 两两点之间的差
# 欧氏距离差
dist = np.linalg.norm(diff, axis=-1)
matches = np.less_equal(dist, 2)
matches
for i in matches:
    print(i)
(0.20+0.016)*40
0.2/5000
np.expand_dims()
list1 = []
list2 = []
for i in range(200):
    fz = (65**(0.55*i)) * (35**(0.45*i))
    fm = 65**(0.55*i) * 35**(0.45*i) + 45**(0.55*i) * 55**(0.45*i)
    p = fz/fm
    list1.append(p)
    
    fz1 = (9/13)**(0.1*i)
    fm1 = (99/91)**(0.45*i)
    p1 = fz1*fm1
    list2.append(p1)
list2
13/9*7/11
(13**0.55)*(7**0.45)/((9**0.55)*(11**0.45))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.23454545454545456, 'fraction_numerical': 0.10242424242424242, 'mean_word_length': 2.8306264501160094, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 6, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '32777', 'license': 'MIT', 'n_tokens_mistral': 949, 'n_tokens_neox': 898, 'n_words': 137, 'path': 'example/test.ipynb', 'repo_name': 'fish258/MonoRec', 'size': '1650'}