calc.setNum(num === '' || num === '-' || num === '-0.' ? '0' : num)

calc.setNum(num === '' || num === '-' || num === '-0.' ? '0' : num)的作用是将用户输入的数字字符串转化为可以进行计算的数字类型。具体来说，它会首先判断用户输入的数字是否为空、是否为负号或者是"-0."，如果是，那么就将其转化为0。如果不是，则将其转化为数字类型。

举例来说，如果用户输入了"-0.123"，那么经过这个函数处理后就会变成-0.123。而如果用户输入了""或者"-"，那么就会被转化为0。

相关问题

def create_C1(dataset): C1 = [] for transaction in dataset: for item in transaction: if not [item] in C1: C1.append([item]) C1.sort() return list(map(frozenset, C1)) def scan_D(D, Ck, min_support): ssCnt = {} for tid in D: for can in Ck: if can.issubset(tid): if not can in ssCnt: ssCnt[can] = 1 else: ssCnt[can] += 1 num_items = float(len(D)) retList = [] support_data = {} for key in ssCnt: support = ssCnt[key] / num_items if support >= min_support: retList.insert(0, key) support_data[key] = support return retList, support_data def apriori_gen(Lk, k): retList = [] len_Lk = len(Lk) for i in range(len_Lk): for j in range(i+1, len_Lk): L1 = list(Lk[i])[:k-2] L2 = list(Lk[j])[:k-2] L1.sort() L2.sort() if L1 == L2: retList.append(Lk[i] | Lk[j]) return retList def apriori(dataset, min_support=0.5): C1 = create_C1(dataset) D = list(map(set, dataset)) L1, support_data = scan_D(D, C1, min_support) L = [L1] k = 2 while len(L[k-2]) > 0: Ck = apriori_gen(L[k-2], k) Lk, support_k = scan_D(D, Ck, min_support) support_data.update(support_k) L.append(Lk) k += 1 return L, support_data再此代码基础上增加置信度

在Apriori算法中，可以通过计算关联规则的置信度来评估规则的可靠性。置信度表示当一个规则的前提项出现时，结论项也会出现的概率。可以使用以下代码来计算关联规则的置信度：

def generate_rules(L, support_data, min_confidence=0.7):
    """生成关联规则"""
    big_rules_list = []
    for i in range(1, len(L)):
        for freq_set in L[i]:
            H1 = [frozenset([item]) for item in freq_set]
            if i > 1:
                rules_from_conseq(freq_set, H1, support_data, big_rules_list, min_confidence)
            else:
                calc_confidence(freq_set, H1, support_data, big_rules_list, min_confidence)
    return big_rules_list

def calc_confidence(freq_set, H, support_data, brl, min_confidence=0.7):
    """计算规则的置信度"""
    pruned_H = []
    for conseq in H:
        conf = support_data[freq_set] / support_data[freq_set - conseq]
        if conf >= min_confidence:
            print(freq_set - conseq, "-->", conseq, "conf:", conf)
            brl.append((freq_set - conseq, conseq, conf))
            pruned_H.append(conseq)
    return pruned_H

def rules_from_conseq(freq_set, H, support_data, brl, min_confidence=0.7):
    """从H中生成更多的关联规则"""
    m = len(H[0])
    if len(freq_set) > (m + 1):
        Hmp1 = apriori_gen(H, m + 1)
        Hmp1 = calc_confidence(freq_set, Hmp1, support_data, brl, min_confidence)
        if len(Hmp1) > 1:
            rules_from_conseq(freq_set, Hmp1, support_data, brl, min_confidence)

其中，`generate_rules`函数用于生成关联规则，`calc_confidence`函数用于计算规则的置信度，`rules_from_conseq`函数用于从H中生成更多的关联规则。在计算置信度时，需要计算规则的支持度以及规则的前提项和结论项的支持度，然后使用支持度计算置信度，并与最小置信度进行比较。如果置信度大于或等于最小置信度，则输出关联规则。

def create_C1(dataset): C1 = [] for transaction in dataset: for item in transaction: if not [item] in C1: C1.append([item]) C1.sort() return list(map(frozenset, C1)) # print(len(create_C1(dataset))) def scan_D(D, Ck, min_support): ssCnt = {} for tid in D: for can in Ck: if can.issubset(tid): if not can in ssCnt: ssCnt[can] = 1 else: ssCnt[can] += 1 num_items = float(len(D)) retList = [] support_data = {} for key in ssCnt: support = ssCnt[key] / num_items if support >= min_support: retList.insert(0, key) support_data[key] = support return retList, support_data def apriori_gen(Lk, k): retList = [] len_Lk = len(Lk) for i in range(len_Lk): for j in range(i + 1, len_Lk): L1 = list(Lk[i])[:k - 2] L2 = list(Lk[j])[:k - 2] L1.sort() L2.sort() if L1 == L2: retList.append(Lk[i] | Lk[j]) return retList def apriori(dataset, min_support): C1 = create_C1(dataset) D = list(map(set, dataset)) L1, support_data = scan_D(D, C1, min_support) L = [L1] k = 2 while len(L[k - 2]) > 0: Ck = apriori_gen(L[k - 2], k) Lk, support_k = scan_D(D, Ck, min_support) support_data.update(support_k) L.append(Lk) k += 1 return L, support_data如果我想在这段代码中加入置信度，怎么加

在这段代码中加入置信度，实际上是要实现关联规则算法。在Apriori算法的基础上，可以通过以下步骤来实现关联规则算法：

1. 从频繁项集L中生成候选规则集合，即对于每个频繁项集，生成包含该项集中所有元素的所有子集。

2. 对于每个候选规则，计算其置信度，并筛选出满足最小置信度要求的规则。

下面是修改后的代码实现：

def generate_rules(L, support_data, min_conf):
    rules_list = []
    for i in range(1, len(L)):
        for freq_set in L[i]:
            H1 = [frozenset([item]) for item in freq_set]
            if i > 1:
                rules_from_conseq(freq_set, H1, support_data, rules_list, min_conf)
            else:
                calc_conf(freq_set, H1, support_data, rules_list, min_conf)
    return rules_list

def calc_conf(freq_set, H, support_data, brl, min_conf):
    pruned_h = []
    for conseq in H:
        conf = support_data[freq_set] / support_data[freq_set - conseq]
        if conf >= min_conf:
            print(freq_set - conseq, '-->', conseq, 'conf:', conf)
            brl.append((freq_set - conseq, conseq, conf))
            pruned_h.append(conseq)
    return pruned_h

def rules_from_conseq(freq_set, H, support_data, brl, min_conf):
    m = len(H[0])
    if len(freq_set) > (m + 1):
        Hmp1 = apriori_gen(H, m + 1)
        Hmp1 = calc_conf(freq_set, Hmp1, support_data, brl, min_conf)
        if len(Hmp1) > 1:
            rules_from_conseq(freq_set, Hmp1, support_data, brl, min_conf)

def apriori(dataset, min_support, min_conf):
    C1 = create_C1(dataset)
    D = list(map(set, dataset))
    L1, support_data = scan_D(D, C1, min_support)
    L = [L1]
    k = 2
    while len(L[k - 2]) > 0:
        Ck = apriori_gen(L[k - 2], k)
        Lk, support_k = scan_D(D, Ck, min_support)
        support_data.update(support_k)
        L.append(Lk)
        k += 1
    rules_list = generate_rules(L, support_data, min_conf)
    return L, support_data, rules_list

其中，generate_rules函数用于生成关联规则，calc_conf函数用于计算规则的置信度，rules_from_conseq函数用于从频繁项集中生成候选规则，apriori函数用于调用Apriori算法和关联规则算法，并返回频繁项集、支持度数据和关联规则列表。在调用apriori函数时，需要传入最小支持度和最小置信度阈值。