theta = np.random.randn(2,1)
时间: 2024-02-04 22:07:39 浏览: 36
根据提供的引用内容,theta = np.random.randn(2,1)是一个使用NumPy库生成一个2行1列的随机数组的Python代码。其中,np.random.randn()函数返回一个或多个样本,具有标准正态分布。这意味着生成的随机数遵循正态分布,均值为0,标准差为1。
下面是一个示例代码,展示如何使用theta = np.random.randn(2,1)生成随机数组:
```python
import numpy as np
theta = np.random.randn(2,1)
print(theta)
```
输出:
```
[[-0.12345678]
[ 1.23456789]]
```
相关问题
翻译这段程序并自行赋值调用:import matplotlib.pyplot as plt import numpy as np import sklearn import sklearn.datasets import sklearn.linear_model def plot_decision_boundary(model, X, y): # Set min and max values and give it some padding x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1 y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1 h = 0.01 # Generate a grid of points with distance h between them xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) # Predict the function value for the whole grid Z = model(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) # Plot the contour and training examples plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral) plt.ylabel('x2') plt.xlabel('x1') plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral) def sigmoid(x): s = 1/(1+np.exp(-x)) return s def load_planar_dataset(): np.random.seed(1) m = 400 # number of examples N = int(m/2) # number of points per class print(np.random.randn(N)) D = 2 # dimensionality X = np.zeros((m,D)) # data matrix where each row is a single example Y = np.zeros((m,1), dtype='uint8') # labels vector (0 for red, 1 for blue) a = 4 # maximum ray of the flower for j in range(2): ix = range(Nj,N(j+1)) t = np.linspace(j3.12,(j+1)3.12,N) + np.random.randn(N)0.2 # theta r = anp.sin(4t) + np.random.randn(N)0.2 # radius X[ix] = np.c_[rnp.sin(t), rnp.cos(t)] Y[ix] = j X = X.T Y = Y.T return X, Y def load_extra_datasets(): N = 200 noisy_circles = sklearn.datasets.make_circles(n_samples=N, factor=.5, noise=.3) noisy_moons = sklearn.datasets.make_moons(n_samples=N, noise=.2) blobs = sklearn.datasets.make_blobs(n_samples=N, random_state=5, n_features=2, centers=6) gaussian_quantiles = sklearn.datasets.make_gaussian_quantiles(mean=None, cov=0.5, n_samples=N, n_features=2, n_classes=2, shuffle=True, random_state=None) no_structure = np.random.rand(N, 2), np.random.rand(N, 2) return noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure
这段程序是一个分类模型的辅助函数,包括了绘制决策边界、sigmoid函数和加载数据集的函数。具体实现如下:
```python
import matplotlib.pyplot as plt
import numpy as np
import sklearn
import sklearn.datasets
import sklearn.linear_model
def plot_decision_boundary(model, X, y):
# 设置最小值和最大值,并给它们一些填充
x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1
y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1
h = 0.01
# 生成一个网格,网格中点的距离为h
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
# 对整个网格预测函数值
Z = model(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
# 绘制轮廓和训练样本
plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)
plt.ylabel('x2')
plt.xlabel('x1')
plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral)
def sigmoid(x):
s = 1 / (1 + np.exp(-x))
return s
def load_planar_dataset():
np.random.seed(1)
m = 400 # 样本数量
N = int(m / 2) # 每个类的样本数量
# 生成数据集
D = 2 # 特征维度
X = np.zeros((m, D)) # 特征矩阵
Y = np.zeros((m, 1), dtype='uint8') # 标签向量
a = 4 # 花的最大半径
for j in range(2):
ix = range(N*j, N*(j+1))
t = np.linspace(j*3.12, (j+1)*3.12, N) + np.random.randn(N)*0.2 # theta
r = a*np.sin(4*t) + np.random.randn(N)*0.2 # radius
X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]
Y[ix] = j
X = X.T
Y = Y.T
return X, Y
def load_extra_datasets():
N = 200
noisy_circles = sklearn.datasets.make_circles(n_samples=N, factor=.5, noise=.3)
noisy_moons = sklearn.datasets.make_moons(n_samples=N, noise=.2)
blobs = sklearn.datasets.make_blobs(n_samples=N, random_state=5, n_features=2, centers=6)
gaussian_quantiles = sklearn.datasets.make_gaussian_quantiles(mean=None, cov=0.5, n_samples=N, n_features=2, n_classes=2, shuffle=True, random_state=None)
no_structure = np.random.rand(N, 2), np.random.rand(N, 2)
return noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure
```
这段程序中包含了以下函数:
- `plot_decision_boundary(model, X, y)`:绘制分类模型的决策边界,其中`model`是分类模型,`X`是特征矩阵,`y`是标签向量。
- `sigmoid(x)`:实现sigmoid函数。
- `load_planar_dataset()`:加载一个二维的花瓣数据集。
- `load_extra_datasets()`:加载五个其他数据集。
优化这段pythonimport numpy as np import matplotlib.pyplot as plt import math # 待测信号 freq = 17.77777 # 信号频率 t = np.linspace(0, 0.2, 1001) Omega =2 * np.pi * freq phi = np.pi A=1 x = A * np.sin(Omega * t + phi) # 加入噪声 noise = 0.2 * np.random.randn(len(t)) x_noise = x + noise # 参考信号 ref0_freq = 17.77777 # 参考信号频率 ref0_Omega =2 * np.pi * ref0_freq ref_0 = 2np.sin(ref0_Omega * t) # 参考信号90°相移信号 ref1_freq = 17.77777 # 参考信号频率 ref1_Omega =2 * np.pi * ref1_freq ref_1 = 2np.cos(ref1_Omega * t) # 混频信号 signal_0 = x_noise * ref_0 signal_1 = x_noise * ref_1 # 绘图 plt.figure(figsize=(13,4)) plt.subplot(2,3,1) plt.plot(t, x_noise) plt.title('input signal', fontsize=13) plt.subplot(2,3,2) plt.plot(t, ref_0) plt.title('reference signal', fontsize=13) plt.subplot(2,3,3) plt.plot(t, ref_1) plt.title('phase-shifted by 90°', fontsize=13) plt.subplot(2,3,4) plt.plot(t, signal_0) plt.title('mixed signal_1', fontsize=13) plt.subplot(2,3,5) plt.plot(t, signal_1) plt.title('mixed signal_2', fontsize=13) plt.tight_layout() # 计算平均值 X = np.mean(signal_0) Y = np.mean(signal_1) print("X=",X) print("Y=",Y) # 计算振幅和相位 X_square =X2 Y_square =Y2 sum_of_squares = X_square + Y_square result = np.sqrt(sum_of_squares) Theta = np.arctan2(Y, X) print("R=", result) print("Theta=", Theta),把输入信号部分整理成函数:输入参数为t_vec,A,phi,noise;锁相测量部分也整理成代码,输入待测周期信号,以及频率freq,输出为A,phi,不用绘图
优化建议:
1. 可以将绘图部分注释掉,避免在计算时浪费时间和资源。
2. 可以将计算部分整合成一个函数,并返回锁相测量结果,方便后续调用和处理。
修改后的代码如下:
```python
import numpy as np
import math
def lock_in_measurement(t_vec, A, phi, noise, freq):
# 生成待测信号
Omega = 2 * np.pi * freq
x = A * np.sin(Omega * t_vec + phi)
x_noise = x + noise
# 生成参考信号
ref0_Omega = 2 * np.pi * freq
ref_0 = 2 * np.sin(ref0_Omega * t_vec)
ref1_Omega = 2 * np.pi * freq
ref_1 = 2 * np.cos(ref1_Omega * t_vec)
# 混频信号
signal_0 = x_noise * ref_0
signal_1 = x_noise * ref_1
# 计算平均值
X = np.mean(signal_0)
Y = np.mean(signal_1)
# 计算振幅和相位
X_square = X ** 2
Y_square = Y ** 2
sum_of_squares = X_square + Y_square
result = np.sqrt(sum_of_squares)
Theta = np.arctan2(Y, X)
return result, Theta
```
使用方法示例:
```python
t = np.linspace(0, 0.2, 1001)
A = 1
phi = np.pi
noise = 0.2 * np.random.randn(len(t))
freq = 17.77777
result, Theta = lock_in_measurement(t, A, phi, noise, freq)
print("R=", result)
print("Theta=", Theta)
```