Python 中用 matplotlib 画散列点 (Scatter)

基本散列点绘制

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from matplotlib import pyplot as plt

import numpy as np

 

# Generating a Gaussion dataset:

# creating random vectors from the multivariate normal distribution

# given mean and covariance

mu_vec1 = np.array([0,0])

cov_mat1 = np.array([[2,0],[0,2]])

 

x1_samples = np.random.multivariate_normal(mu_vec1, cov_mat1, 100)

x2_samples = np.random.multivariate_normal(mu_vec1+0.2, cov_mat1+0.2, 100)

x3_samples = np.random.multivariate_normal(mu_vec1+0.4, cov_mat1+0.4, 100)

 

# x1_samples.shape -> (100, 2), 100 rows, 2 columns

 

plt.figure(figsize=(8,6))

 

plt.scatter(x1_samples[:,0], x1_samples[:,1], marker='x',

            color='blue', alpha=0.7, label='x1 samples')

plt.scatter(x2_samples[:,0], x1_samples[:,1], marker='o',

            color='green', alpha=0.7, label='x2 samples')

plt.scatter(x3_samples[:,0], x1_samples[:,1], marker='^',

            color='red', alpha=0.7, label='x3 samples')

plt.title('Basic scatter plot')

plt.ylabel('variable X')

plt.xlabel('Variable Y')

plt.legend(loc='upper right')

 

plt.show()

带标签

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importmatplotlib.pyplotasplt

 

x_coords=[0.13,0.22,0.39,0.59,0.68,0.74,0.93]

y_coords=[0.75,0.34,0.44,0.52,0.80,0.25,0.55]

 

fig=plt.figure(figsize=(8,5))

plt.scatter(x_coords,y_coords,marker='s',s=50)

 

forx,yinzip(x_coords,y_coords):

    plt.annotate(

        '(%s, %s)'%(x,y),

        xy=(x,y),

        xytext=(0,-10),

        textcoords='offset points',

        ha='center',

        va='top')

 

plt.xlim([0,1])

plt.ylim([0,1])

plt.show()

用曲线把样本分成两类

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# 2-category classification with random 2D-sample data

# from a multivariate normal distribution

 

import numpy as np

from matplotlib import pyplot as plt

 

def decision_boundary(x_1):

    """ Calculates the x_2 value for plotting the decision boundary."""

    return 4 - np.sqrt(-x_1**2 + 4*x_1 + 6 + np.log(16))

 

# Generating a Gaussion dataset:

# creating random vectors from the multivariate normal distribution

# given mean and covariance

mu_vec1 = np.array([0,0])

cov_mat1 = np.array([[2,0],[0,2]])

x1_samples = np.random.multivariate_normal(mu_vec1, cov_mat1, 100)

mu_vec1 = mu_vec1.reshape(1,2).T # to 1-col vector

 

mu_vec2 = np.array([1,2])

cov_mat2 = np.array([[1,0],[0,1]])

x2_samples = np.random.multivariate_normal(mu_vec2, cov_mat2, 100)

mu_vec2 = mu_vec2.reshape(1,2).T # to 1-col vector

 

# Main scatter plot and plot annotation

f, ax = plt.subplots(figsize=(7, 7))

ax.scatter(x1_samples[:,0], x1_samples[:,1], marker='o', color='green', s=40, alpha=0.5)

ax.scatter(x2_samples[:,0], x2_samples[:,1], marker='^', color='blue', s=40, alpha=0.5)

plt.legend(['Class1 (w1)', 'Class2 (w2)'], loc='upper right')

plt.title('Densities of 2 classes with 25 bivariate random patterns each')

plt.ylabel('x2')

plt.xlabel('x1')

ftext = 'p(x|w1) ~ N(mu1=(0,0)^t, cov1=I)np(x|w2) ~ N(mu2=(1,1)^t, cov2=I)'

plt.figtext(.15,.8, ftext, fontsize=11, ha='left')

 

# Adding decision boundary to plot

x_1 = np.arange(-5, 5, 0.1)

bound = decision_boundary(x_1)

plt.plot(x_1, bound, 'r--', lw=3)

 

x_vec = np.linspace(*ax.get_xlim())

x_1 = np.arange(0, 100, 0.05)

 

plt.show()

按到原点的距离增大点的大小

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importnumpyasnp

importmatplotlib.pyplotasplt

 

fig=plt.figure(figsize=(8,6))

 

# Generating a Gaussion dataset:

# creating random vectors from the multivariate normal distribution

# given mean and covariance

mu_vec1=np.array([0,0])

cov_mat1=np.array([[1,0],[0,1]])

X=np.random.multivariate_normal(mu_vec1,cov_mat1,500)

 

R=X**2

R_sum=R.sum(axis=1)

plt.scatter(X[:,0],X[:,1],

            color='gray',

            marker='o',

            s=32.*R_sum,

            edgecolor='black',

            alpha=0.5)

plt.show()