2974:487-3279

本文介绍了一种电话号码标准化的方法,将包含字母和特殊字符的电话号码转换为统一的数字格式,便于记忆和比较。并通过一个示例程序展示了如何检测电话簿中重复的电话号码,确保数据的准确性和唯一性。

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描述

企业喜欢用容易被记住的电话号码。让电话号码容易被记住的一个办法是将它写成一个容易记住的单词或者短语。例如,你需要给滑铁卢大学打电话时,可以拨打TUT-GLOP。有时,只将电话号码中部分数字拼写成单词。当你晚上回到酒店,可以通过拨打310-GINO来向Gino's订一份pizza。让电话号码容易被记住的另一个办法是以一种好记的方式对号码的数字进行分组。通过拨打必胜客的“三个十”号码3-10-10-10,你可以从他们那里订pizza。

电话号码的标准格式是七位十进制数,并在第三、第四位数字之间有一个连接符。电话拨号盘提供了从字母到数字的映射,映射关系如下:
A, B, 和C 映射到 2
D, E, 和F 映射到 3
G, H, 和I 映射到 4
J, K, 和L 映射到 5
M, N, 和O 映射到 6
P, R, 和S 映射到 7
T, U, 和V 映射到 8
W, X, 和Y 映射到 9

Q和Z没有映射到任何数字,连字符不需要拨号,可以任意添加和删除。 TUT-GLOP的标准格式是888-4567,310-GINO的标准格式是310-4466,3-10-10-10的标准格式是310-1010。

如果两个号码有相同的标准格式,那么他们就是等同的(相同的拨号)

你的公司正在为本地的公司编写一个电话号码薄。作为质量控制的一部分,你想要检查是否有两个和多个公司拥有相同的电话号码。

输入

输入的格式是,第一行是一个正整数,指定电话号码薄中号码的数量(最多100000)。余下的每行是一个电话号码。每个电话号码由数字,大写字母(除了Q和Z)以及连接符组成。每个电话号码中只会刚好有7个数字或者字母。

输出

对于每个出现重复的号码产生一行输出,输出是号码的标准格式紧跟一个空格然后是它的重复次数。如果存在多个重复的号码,则按照号码的字典升序输出。如果输入数据中没有重复的号码,输出一行:
No duplicates.

样例输入

12
4873279
ITS-EASY
888-4567
3-10-10-10
888-GLOP
TUT-GLOP
967-11-11
310-GINO
F101010
888-1200
-4-8-7-3-2-7-9-
487-3279

样例输出

310-1010 2
487-3279 4
888-4567 3

这是第一次写的,但是不明白怎么AC不了,希望有大佬帮我看看。

 

 

#include<stdio.h>
#include<string.h>
#include<stdlib.h>
int cmp(const void *a,const void *b)
{
	return (strcmp((char *)a,(char *)b));
}
int main()
{
	char tleNumber[10001][9],str[80];
	int n;
	char s[] = "2223334445556667777888999";
	scanf("%d",&n);
	for(int i = 0;i<n;i++)
	{
		int k = 0;
		scanf("%s",str);
		for(int j = 0;j<strlen(str);j++)
			if(str[j]>='A'&&str[j]<'Z')		
				str[j] = s[str[j] - 'A'];
	    for(int j = 0;j<strlen(str);j++)
		{
			if(k==3)
			{
				tleNumber[i][k++] = '-';
				j--;
			}	   	
			else if(k==8)
			    tleNumber[i][k] = '\0';
		    else
				if(str[j]>='0'&&str[j]<='9')
					 tleNumber[i][k++] = str[j];			        	  		       	  	 	
	    }
	}
	qsort(tleNumber,n,9,cmp);
	int count = 0;
	int sum = 0;
	for(int i = 0;i<n;i++)
	{
		count = 1;
		while(strcmp(tleNumber[i],tleNumber[i+1])==0&&i<n)
		{
			count++;
			i++;
		}
		if(count>1)
		{
			printf("%s %d\n",tleNumber[i],count);
			sum++;
		}	    
	}
	if(sum==0)
        printf("No duplicates.\n");
}

 

这是书上给的例程

#include<stdio.h>
#include<string.h>
#include<stdlib.h>
char str[100000][9],str1[80];
char map[] = "2223334445556667777888999";
int cmp(const void *a,const void *b)
{
	return (strcmp((char *)a,(char *)b));
}
void Change(int n)
{
	int j,k;
	j = k = -1;
	while(k<8)
	{
		j++;
		if(str1[j] == '-')
		continue;
		k++;
		if(k==3)
		{
			str[n][k] = '-';
			k++;
		}
		if(str1[j]>='A'&&str1[j]<='Z')
		{
			str[n][k] = map[str1[j] - 'A'];
			continue;
		}
		str[n][k] = str1[j];
	}
	str[n][k] = '\0';
		return;
}
int main()
{
	int nCases,i,j;
	bool b = true;
	scanf("%d",&nCases);
	for(i = 0;i<nCases;i++)
	{
		scanf("%s",str1);
		Change(i);
	}
	qsort(str,nCases,9,cmp);
	i = 0;
	while(i<nCases)
	{
		j = i;
		i++;
		while(strcmp(str[i],str[j]) == 0)
		i++;
		if(i - j>1)
		{
			printf("%s %d\n",str[j],i - j);
		    b = false;
		}
		
	}
	if(b)
	printf("No duplicates.\n");
}

 

def main(): t0 = time.time() ​ # 选择模型 model = build_lstm_model() ​ # 编译模型 model.compile(optimizer=tf.keras.optimizers.Adam(0.001), loss=tf.keras.losses.BinaryCrossentropy(), metrics=['accuracy']) ​ # 训练模型 checkpoint = ModelCheckpoint('model_checkpoint.h5', save_weights_only=True, verbose=1, save_freq='epoch') model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, callbacks=[checkpoint]) ​ # 评估模型 loss, accuracy = model.evaluate(x_test, y_test) print(f"Test Loss: {loss}, Test Accuracy: {accuracy}") ​ t1 = time.time() print(f"模型运行的时间为:{t1 - t0:.2f} 秒") ​ if __name__ == '__main__': main() 10秒 WARNING:tensorflow:From /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/initializers.py:118: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version. Instructions for updating: Call initializer instance with the dtype argument instead of passing it to the constructor WARNING:tensorflow:From /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1623: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version. Instructions for updating: If using Keras pass *_constraint arguments to layers. NotImplementedError: Cannot convert a symbolic Tensor (lstm/strided_slice:0) to a numpy array. --------------------------------------------------------------------------- NotImplementedError Traceback (most recent call last) Cell In[21], line 24 21 print(f"模型运行的时间为:{t1 - t0:.2f} 秒") 23 if __name__ == '__main__': ---> 24 main() Cell In[21], line 5, in main() 2 t0 = time.time() 4 # 选择模型 ----> 5 model = build_lstm_model() 7 # 编译模型 8 model.compile(optimizer=tf.keras.optimizers.Adam(0.001), 9 loss=tf.keras.losses.BinaryCrossentropy(), 10 metrics=['accuracy']) Cell In[15], line 4, in build_lstm_model() 3 def build_lstm_model(): ----> 4 model = keras.Sequential([ 5 layers.Embedding(total_words, embedding_len, input_length=max_review_len), 6 layers.LSTM(64, return_sequences=False), 7 layers.Dense(1, activation='sigmoid') 8 ]) 9 return model File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/training/tracking/base.py:457, in no_automatic_dependency_tracking.<locals>._method_wrapper(self, *args, **kwargs) 455 self._self_setattr_tracking = False # pylint: disable=protected-access 456 try: --> 457 result = method(self, *args, **kwargs) 458 finally: 459 self._self_setattr_tracking = previous_value # pylint: disable=protected-access File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/engine/sequential.py:113, in Sequential.__init__(self, layers, name) 111 tf_utils.assert_no_legacy_layers(layers) 112 for layer in layers: --> 113 self.add(layer) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/training/tracking/base.py:457, in no_automatic_dependency_tracking.<locals>._method_wrapper(self, *args, **kwargs) 455 self._self_setattr_tracking = False # pylint: disable=protected-access 456 try: --> 457 result = method(self, *args, **kwargs) 458 finally: 459 self._self_setattr_tracking = previous_value # pylint: disable=protected-access File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/engine/sequential.py:195, in Sequential.add(self, layer) 190 self.inputs = layer_utils.get_source_inputs(self.outputs[0]) 192 elif self.outputs: 193 # If the model is being built continuously on top of an input layer: 194 # refresh its output. --> 195 output_tensor = layer(self.outputs[0]) 196 if len(nest.flatten(output_tensor)) != 1: 197 raise TypeError('All layers in a Sequential model ' 198 'should have a single output tensor. ' 199 'For multi-output layers, ' 200 'use the functional API.') File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:623, in RNN.__call__(self, inputs, initial_state, constants, **kwargs) 617 inputs, initial_state, constants = _standardize_args(inputs, 618 initial_state, 619 constants, 620 self._num_constants) 622 if initial_state is None and constants is None: --> 623 return super(RNN, self).__call__(inputs, **kwargs) 625 # If any of `initial_state` or `constants` are specified and are Keras 626 # tensors, then add them to the inputs and temporarily modify the 627 # input_spec to include them. 629 additional_inputs = [] File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/engine/base_layer.py:854, in Layer.__call__(self, inputs, *args, **kwargs) 852 outputs = base_layer_utils.mark_as_return(outputs, acd) 853 else: --> 854 outputs = call_fn(cast_inputs, *args, **kwargs) 856 except errors.OperatorNotAllowedInGraphError as e: 857 raise TypeError('You are attempting to use Python control ' 858 'flow in a layer that was not declared to be ' 859 'dynamic. Pass `dynamic=True` to the class ' 860 'constructor.\nEncountered error:\n"""\n' + 861 str(e) + '\n"""') File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:2548, in LSTM.call(self, inputs, mask, training, initial_state) 2546 self.cell.reset_dropout_mask() 2547 self.cell.reset_recurrent_dropout_mask() -> 2548 return super(LSTM, self).call( 2549 inputs, mask=mask, training=training, initial_state=initial_state) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:681, in RNN.call(self, inputs, mask, training, initial_state, constants) 675 def call(self, 676 inputs, 677 mask=None, 678 training=None, 679 initial_state=None, 680 constants=None): --> 681 inputs, initial_state, constants = self._process_inputs( 682 inputs, initial_state, constants) 684 if mask is not None: 685 # Time step masks must be the same for each input. 686 # TODO(scottzhu): Should we accept multiple different masks? 687 mask = nest.flatten(mask)[0] File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:798, in RNN._process_inputs(self, inputs, initial_state, constants) 796 initial_state = self.states 797 else: --> 798 initial_state = self.get_initial_state(inputs) 800 if len(initial_state) != len(self.states): 801 raise ValueError('Layer has ' + str(len(self.states)) + 802 ' states but was passed ' + str(len(initial_state)) + 803 ' initial states.') File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:605, in RNN.get_initial_state(self, inputs) 603 dtype = inputs.dtype 604 if get_initial_state_fn: --> 605 init_state = get_initial_state_fn( 606 inputs=None, batch_size=batch_size, dtype=dtype) 607 else: 608 init_state = _generate_zero_filled_state(batch_size, self.cell.state_size, 609 dtype) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:2313, in LSTMCell.get_initial_state(self, inputs, batch_size, dtype) 2312 def get_initial_state(self, inputs=None, batch_size=None, dtype=None): -> 2313 return list(_generate_zero_filled_state_for_cell( 2314 self, inputs, batch_size, dtype)) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:2752, in _generate_zero_filled_state_for_cell(cell, inputs, batch_size, dtype) 2750 batch_size = array_ops.shape(inputs)[0] 2751 dtype = inputs.dtype -> 2752 return _generate_zero_filled_state(batch_size, cell.state_size, dtype) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:2768, in _generate_zero_filled_state(batch_size_tensor, state_size, dtype) 2765 return array_ops.zeros(init_state_size, dtype=dtype) 2767 if nest.is_sequence(state_size): -> 2768 return nest.map_structure(create_zeros, state_size) 2769 else: 2770 return create_zeros(state_size) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/util/nest.py:536, in map_structure(func, *structure, **kwargs) 532 flat_structure = [flatten(s, expand_composites) for s in structure] 533 entries = zip(*flat_structure) 535 return pack_sequence_as( --> 536 structure[0], [func(*x) for x in entries], 537 expand_composites=expand_composites) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/util/nest.py:536, in <listcomp>(.0) 532 flat_structure = [flatten(s, expand_composites) for s in structure] 533 entries = zip(*flat_structure) 535 return pack_sequence_as( --> 536 structure[0], [func(*x) for x in entries], 537 expand_composites=expand_composites) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/keras/layers/recurrent.py:2765, in _generate_zero_filled_state.<locals>.create_zeros(unnested_state_size) 2763 flat_dims = tensor_shape.as_shape(unnested_state_size).as_list() 2764 init_state_size = [batch_size_tensor] + flat_dims -> 2765 return array_ops.zeros(init_state_size, dtype=dtype) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/ops/array_ops.py:2338, in zeros(shape, dtype, name) 2334 if not isinstance(shape, ops.Tensor): 2335 try: 2336 # Create a constant if it won't be very big. Otherwise create a fill op 2337 # to prevent serialized GraphDefs from becoming too large. -> 2338 output = _constant_if_small(zero, shape, dtype, name) 2339 if output is not None: 2340 return output File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/ops/array_ops.py:2295, in _constant_if_small(value, shape, dtype, name) 2293 def _constant_if_small(value, shape, dtype, name): 2294 try: -> 2295 if np.prod(shape) < 1000: 2296 return constant(value, shape=shape, dtype=dtype, name=name) 2297 except TypeError: 2298 # Happens when shape is a Tensor, list with Tensor elements, etc. File <__array_function__ internals>:180, in prod(*args, **kwargs) File /opt/conda/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3088, in prod(a, axis, dtype, out, keepdims, initial, where) 2970 @array_function_dispatch(_prod_dispatcher) 2971 def prod(a, axis=None, dtype=None, out=None, keepdims=np._NoValue, 2972 initial=np._NoValue, where=np._NoValue): 2973 """ 2974 Return the product of array elements over a given axis. 2975 (...) 3086 10 3087 """ -> 3088 return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out, 3089 keepdims=keepdims, initial=initial, where=where) File /opt/conda/lib/python3.8/site-packages/numpy/core/fromnumeric.py:86, in _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs) 83 else: 84 return reduction(axis=axis, out=out, **passkwargs) ---> 86 return ufunc.reduce(obj, axis, dtype, out, **passkwargs) File /opt/conda/lib/python3.8/site-packages/tensorflow_core/python/framework/ops.py:735, in Tensor.__array__(self) 734 def __array__(self): --> 735 raise NotImplementedError("Cannot convert a symbolic Tensor ({}) to a numpy" 736 " array.".format(self.name)) NotImplementedError: Cannot convert a symbolic Tensor (lstm/strided_slice:0) to a numpy array.
最新发布
06-22
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