PHP：为什么变量在提交后没有重新加载？

Question

这是我做过的代码：

<form method="post" action="test.php">
<input type="text" name="name" placeholder="name"/><br />
<input type="submit" value="Validate" />
</form>
<?php
$sum=0;
if(isset($_POST['name'])){
   $sum+=1;
}
echo "sum = $sum";
?>

当我在表单中输入一些文字并单击验证时，页面显示sum=1，但在此之后，当我在表单中没有输入任何内容并单击验证时，页面仍显示STILL sum=1。

为什么变量$ sum不会在两个Validate之间重新加载？有没有办法逃脱它？

由于

Answer 1

这将解决问题

<?php
$sum=0;
if(isset($_POST['name']) && $_POST['name'] != ''){
    $sum+=1;
}
echo "sum = $sum";
?>

Answer 2

这是因为isset()检查$ _POST变量是否存在。在您的情况下，$ _POST变量存在并且具有空字符串值。

如果您将isset()更改为!empty()，那么您的代码就会有效;

<form method="post" action="test.php">

<input type="text" name="name" placeholder="name"/><br />


<input type="submit" value="Validate" />
</form>


<?php

$sum=0;

if(!empty($_POST['name'])){
    $sum+=1;
}

echo "sum = $sum";

?>

有关empty()函数here的更多信息。

Answer 3

试试这个

<form method="post" action="test.php">
<input type="text" name="name" placeholder="name"/><br />
<input type="submit" name="submit" value="Validate" />
</form>
<?php
$sum=0;
if(isset($_POST['submit'])){
   $sum+=1;
}
echo "sum = $sum";
?>

Answer 4

您可以尝试以下内容：

if(isset($_POST['name']) && strlen($_POST['name'])>0){
$sum+=1;

Answer 5

您将代码附加1到变量$ sum 但是你的if语句是基于传递的名称字段。 如果名称字段中包含任何数据，则不会。 所以......只要名称字段被传递，你就已经使你的代码加1了， 无论是否有文字输入。

此外，您应该重新分配varible以重置它。 + =应该只是=

#imports
import math
import random
from datetime import datetime

#sorting
def merge(a,b):
    """ Function to merge two arrays """
    c = []
    while len(a) != 0 and len(b) != 0:
        if a[0] < b[0]:
            c.append(a[0])
            a.remove(a[0])
        else:
            c.append(b[0])
            b.remove(b[0])
    if len(a) == 0:
        c += b
    else:
        c += a
    return c

# Code for merge sort
def mergesort(x):
    """ Function to sort an array using merge sort algorithm """
    if len(x) == 0 or len(x) == 1:
        return x
    else:
        middle = len(x)/2
        a = mergesort(x[:int(middle)])
        b = mergesort(x[int(middle):])
        return merge(a,b)

def evolve(nets, progressReport=True):
  popCount = len(nets)

  if progressReport: print("Neural nets thinking...")
  for net in nets:
      for data in training_data:
          net.think([data[0],data[1]], data[2])
  if progressReport: print("Neural nets thought")

  if progressReport: print("Sorting neural nets...")
  nets = mergesort(nets)
  if progressReport: print("Sorted neural nets")

  print("Winner DNA:", nets[0].DNA)
  print("Winner Cost:", nets[0].getCost())

  if progressReport: print("Killing the weak...")
  nets = nets[int(popCount / 2):]
  if progressReport: print("Killed the weak")

  if progressReport: print("Breeding survivors...")

  for i in range(int(popCount/2)):
    dna = mixDNA(nets[i].DNA, nets[random.randint(1,int(popCount/2))].DNA)
    nets.append(NeuralNetwork(dna))

  if progressReport: print("Bred survivors")

  if progressReport: print("\n")


#sigmoid
def sigmoid(x):
    return 1 / (1 + math.e ** -x)

#Length of DNA
DnaLen = 20

#creates random DNA
def createRandomDNA():
    random.seed(datetime.now())
    return ''.join([str(random.randint(1, 9)) for i in range(DnaLen)])

#mutation chance (0 - 1)
mutationChance = 0.01

#mixes DNA (random chance of mutation)
def mixDNA(dna1, dna2):
    midpoint = random.randint(1,20)
    dna = dna1[:midpoint] + dna2[midpoint:] 
    for i in range(len(dna)):
      if random.random() <= mutationChance: dna = dna[:i] + str(random.randint(1,9)) + dna[i+1:]
    return dna

#XOR
training_data = [
    [0, 0, 0],
    [0, 1, 1],
    [1, 0, 1],
    [1, 1, 0]
]

#connects synapses
class Node(object):
    def __init__(self):
        self.inputs = []
        self.outputs = []
        self.value = 0

    def connect(self, other, syn):
        self.outputs.append(syn)
        other.inputs.append(syn)

    def update(self):
        total = sum([i.value for i in self.inputs])
        self.value = sigmoid(total)
        for out in self.outputs: out.trigger(self.value)

#input
class Receptor(Node):
    def __init__(self):
        Node.__init__(self)

    def update(self, inp):
        self.value = inp
        for out in self.outputs: out.trigger(self.value)

#output
class Effector(Node):
    def __init__(self):
        Node.__init__(self)

    def update(self):
        total = sum([i.value for i in self.inputs])
        self.value = total
        return self.value

#connection
class Neurone(Node): pass

#connects nodes
class Synapse(object):
    def __init__(self, weight):
        self.weight = weight
        self.value = 0

    def trigger(self, value):
        self.value = value * self.weight

#the whole network
class NeuralNetwork(object):
    def __init__(self, DNA):
        #DNA
        self.DNA = DNA
        #sets up nodes
        self.receptors = [Receptor() for i in range(2)]
        self.neurones = [Neurone() for i in range(3)]
        self.effectors = [Effector() for i in range(1)]
        #where to extract code from DNA
        counter = 0
        #connects receptors to neurones
        for receptor in self.receptors:
            for neurone in self.neurones:
                """change synapse weight to be based on DNA"""
                Node.connect(receptor, neurone, Synapse(self.getWeightFromDNA(counter)))
                counter += 2
        #connects neurones to effectors
        for neurone in self.neurones:
            for effector in self.effectors:
                """change synapse weight to be based on DNA"""
                Node.connect(neurone, effector, Synapse(self.getWeightFromDNA(counter)))
                counter += 2
        #how wrong
        self.costs = []

    def __lt__(self, other): return self.getCost() < other.getCost()
    def __le__(self, other): return self.getCost() <= other.getCost()
    def __eq__(self, other): return self.getCost() == other.getCost()
    def __ne__(self, other): return self.getCost() != other.getCost()
    def __ge__(self, other): return self.getCost() >= other.getCost()
    def __gt__(self, other): return self.getCost() > other.getCost()

    def getWeightFromDNA(self, location):
      return int(self.DNA[location] + self.DNA[location+1]) / 100 + 0.5

    def think(self, inputs, correct):
        for i in range(len(self.receptors)): self.receptors[i].update(inputs[i])
        for neurone in self.neurones: neurone.update()
        answer = [effector.update() for effector in self.effectors][0]
        self.costs.append(abs(answer - correct))
        return answer 

    def getCost(self):
        return sum(self.costs) / len(self.costs)


def main():
  print("Creating neural nets...")
  nets = [NeuralNetwork(createRandomDNA()) for i in range(1000)]
  print("Created neural nets")

  print("Evolving nets...")
  for i in range(1000):
    for j in nets: j.costs = []
    evolve(nets, False)
    print("Evolved Generation", i+1)

  nets = mergesort(nets)
  print("Evolved nets")

if __name__ == "__main__":
  main()

Answer 6

另一种方法是检查您的客户在您的网页上发出的请求。因此，如果它是一个简单的刷新（不是表单刷新），它是一个GET请求，因此，变量不应该递增，如果表单已经发送，那么你可以做任何你想做的事情，如递增数据

因此，如果客户端正在发送填充了输入文本的表单，则可以增加该值。在所有其他情况下，该值应保持为零。

<form method="post" action="test.php">

    <input type="text" name="name" placeholder="name"/><br />
    <input type="submit" value="Validate" />

</form>

<?php

    $sum=0;

    if ($_SERVER['REQUEST_METHOD'] == 'POST' && isset($_POST['name']) && !empty($_POST['name']))
    {
        $sum++; /* strictly equivalent to: $sum += 1; */
    }

?>

<samp>sum = <?php echo $sum; ?></samp>