x=numpy.arry([1,2,3,4]),x.shape
时间: 2023-09-28 18:09:56 浏览: 60
这段代码存在一些错误,应该是:
```python
import numpy
x = numpy.array([1, 2, 3, 4])
print(x.shape)
```
这段代码的输出结果是:
```
(4,)
```
这是因为 `x` 是一个一维的包含 4 个元素的数组,所以它的形状是 `(4,)`。其中的逗号 `,` 表示这是一个元组(tuple)类型的数据,元组中只有一个元素。
相关问题
import numpy import scipy.special class NeuralNetwork(): def __init__(self,inputnodes,hiddennodes,outputnodes,learningrate): self.inodes=inputnodes self.hnodes=hiddennodes self.onodes=outputnodes self.lr=learningrate self.wih=numpy.random.normal(0.0,pow(self.hnodes,-0.5),(self.hnodes,self.inodes)) self.who=numpy.random.normal(0.0,pow(self.onodes,-0.5),(self.onodes,self.hnodes)) self.activation_function=lambda x:scipy.special.expit(x) pass def train(self,input_list,target_list): inputs=numpy.array(input_list,ndmin=2).T targets=numpy.array(target_list,ndmin=2).T hidden_inputs=numpy.dot(self.wih,inputs) hidden_outputs=self.activation_function(hidden_inputs) final_inputs=numpy.dot(self.who,hidden_outputs) final_outputs=self.activation_function(final_inputs) output_errors=targets-final_outputs hidden_errors=numpy.dot(self.who.T,output_errors) self.who+=self.lr*numpy.dot((output_errors*final_outputs*(1.0-final_outputs)),numpy.transpose(hidden_outputs)) self.wih+=self.lr*numpy.dot((hidden_errors*hidden_outputs*(1.0-hidden_outputs)),numpy.transpose(inputs)) pass def query(self,input_list): inputs=numpy.array(input_list,ndmin=2).T hidden_inputs=numpy.dot(self.wih,inputs) hidden_outputs=self.activation_function(hidden_inputs) final_inputs=numpy.dot(self.who,hidden_outputs) final_outputs=self.activation_function(final_inputs) return final_outputs print('n')写一下注释
以下是对代码的注释:
```python
import numpy
import scipy.special
class NeuralNetwork():
def __init__(self,inputnodes,hiddennodes,outputnodes,learningrate):
# 初始化神经网络的输入层、隐藏层、输出层的节点数以及学习率
self.inodes = inputnodes
self.hnodes = hiddennodes
self.onodes = outputnodes
self.lr = learningrate
# 初始化输入层到隐藏层和隐藏层到输出层的权重
# 对权重进行随机初始化,取值范围为均值为0,标准差为节点数的负平方根
self.wih = numpy.random.normal(0.0, pow(self.hnodes, -0.5), (self.hnodes, self.inodes))
self.who = numpy.random.normal(0.0, pow(self.onodes, -0.5), (self.onodes, self.hnodes))
# 定义激活函数为 sigmoid 函数
self.activation_function = lambda x: scipy.special.expit(x)
def train(self,input_list,target_list):
# 将输入列表和目标列表转换为二维数组
inputs = numpy.array(input_list, ndmin=2).T
targets = numpy.array(target_list, ndmin=2).T
# 计算隐藏层的输入和输出
hidden_inputs = numpy.dot(self.wih, inputs)
hidden_outputs = self.activation_function(hidden_inputs)
# 计算输出层的输入和输出
final_inputs = numpy.dot(self.who, hidden_outputs)
final_outputs = self.activation_function(final_inputs)
# 计算输出层误差和隐藏层误差
output_errors = targets - final_outputs
hidden_errors = numpy.dot(self.who.T, output_errors)
# 更新隐藏层到输出层和输入层到隐藏层的权重
self.who += self.lr * numpy.dot((output_errors * final_outputs * (1.0 - final_outputs)), numpy.transpose(hidden_outputs))
self.wih += self.lr * numpy.dot((hidden_errors * hidden_outputs * (1.0 - hidden_outputs)), numpy.transpose(inputs))
def query(self,input_list):
# 将输入列表转换为二维数组
inputs = numpy.array(input_list, ndmin=2).T
# 计算隐藏层的输入和输出
hidden_inputs = numpy.dot(self.wih, inputs)
hidden_outputs = self.activation_function(hidden_inputs)
# 计算输出层的输入和输出
final_inputs = numpy.dot(self.who, hidden_outputs)
final_outputs = self.activation_function(final_inputs)
# 返回最终输出
return final_outputs
# 创建神经网络实例
input_nodes = 3
hidden_nodes = 3
output_nodes = 3
learning_rate = 0.3
n = NeuralNetwork(input_nodes,hidden_nodes,output_nodes,learning_rate)
# 使用训练数据进行训练
training_data = [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6], [0.7, 0.8, 0.9]]
for data in training_data:
n.train(data, data)
# 使用测试数据进行测试
test_data = [0.2, 0.5, 0.8]
print(n.query(test_data))
```
这个神经网络实现了一个简单的自编码器,训练数据和测试数据都是由一些简单的数字组成。
转换成matlab:def severed_sphere(intrinsic_process, k1=5.5, k2=2): assert intrinsic_process.shape[0] == 2 intrinsic_process_temp = numpy.copy(intrinsic_process) #intrinsic_process_temp = (intrinsic_process_temp.T-numpy.mean(intrinsic_process_temp, axis=1).T).T observed_process = numpy.empty((3, intrinsic_process_temp.shape[1]), dtype=numpy.float64) observed_process[0] = numpy.sin(intrinsic_process_temp[0]*k1)*numpy.cos(intrinsic_process_temp[1]*k2) observed_process[1] = numpy.cos(intrinsic_process_temp[0]*k1)*numpy.cos(intrinsic_process_temp[1]*k2) observed_process[2] = numpy.sin(intrinsic_process_temp[1]*k2) return observed_process
在Matlab中,您可以将以下Python代码转换为对应的Matlab代码:
```matlab
function observed_process = severed_sphere(intrinsic_process, k1, k2)
assert(size(intrinsic_process, 1) == 2);
intrinsic_process_temp = intrinsic_process;
% intrinsic_process_temp = (intrinsic_process_temp.' - mean(intrinsic_process_temp, 2).').';
observed_process = zeros(3, size(intrinsic_process_temp, 2));
observed_process(1,:) = sin(intrinsic_process_temp(1,:) * k1) .* cos(intrinsic_process_temp(2,:) * k2);
observed_process(2,:) = cos(intrinsic_process_temp(1,:) * k1) .* cos(intrinsic_process_temp(2,:) * k2);
observed_process(3,:) = sin(intrinsic_process_temp(2,:) * k2);
end
```
请注意,在Matlab中没有直接相当于`empty`函数的功能,因此我们使用`zeros`函数来初始化`observed_process`数组。另外,对于行向量和列向量的转置,我们使用了`.`运算符。