外接圆圆心坐标公式（圆的标准方程圆心坐标举例） - 原点资讯

知乎上有一个问题，内容是已知空间三个点的坐标，求三个点所构成的圆的圆心坐标（编程实现）？

外接圆圆心坐标公式,圆的标准方程圆心坐标举例(1)

根据圆的定义，这道题的核心就是找到一个点，到已知的三个点的距离相等，利用数学知识可以求解如下：

例如：给定a（x1,y1） b（x2,y2） c（x3,y3）求外接圆心坐标O（x，y）
1. 首先，外接圆的圆心是三角形三条边的垂直平分线的交点，我们根据圆心到顶点的距离相等，可以列出以下方程：
(x1-x)*(x1-x) (y1-y)*(y1-y)=(x2-x)*(x2-x) (y2-y)*(y2-y);
(x2-x)*(x2-x) (y2-y)*(y2-y)=(x3-x)*(x3-x) (y3-y)*(y3-y);
2.化简得到：
2*(x2-x1)*x 2*(y2-y1)y=x2^2 y2^2-x1^2-y1^2;
2*(x3-x2)*x 2*(y3-y2)y=x3^2 y3^2-x2^2-y2^2;
令:A1=2*(x2-x1)；
B1=2*(y2-y1)；
C1=x2^2 y2^2-x1^2-y1^2;
A2=2*(x3-x2)；
B2=2*(y3-y2)；
C2=x3^2 y3^2-x2^2-y2^2;
即:A1*x B1y=C1;
A2*x B2y=C2;
3.最后根据克拉默法则：
x=((C1*B2)-(C2*B1))/((A1*B2)-(A2*B1))；
y=((A1*C2)-(A2*C1))/((A1*B2)-(A2*B1))；

当然，我们今天不是来学习数学公式和数学推导的。Tensorflow是google开源的一款深度学习的工具，其实我们可以利用Tensoflow提供了强大的数学计算能力来求解类似的数学问题。

这道题，我们可以利用梯度下降算法，因为圆心是一个最优解，任何其它点都不满条件。（前提是这三个点不在一条直线上，否则是没有解的）

好了，我们先看代码先，然后在解释。

import tensorflow as tf import numpy # Parameters learning_rate = 0.1 training_epochs = 3000 display_step = 50 # Training Data, 3 points that form a triangel train_X = numpy.asarray([3.0,6.0,9.0]) train_Y = numpy.asarray([7.0,9.0,7.0]) # tf Graph Input X = tf.placeholder("float") Y = tf.placeholder("float") # Set vaibale for center cx = tf.Variable(3, name="cx",dtype=tf.float32) cy = tf.Variable(3, name="cy",dtype=tf.float32) # Caculate the distance to the center and make them as equal as possible distance = tf.pow(tf.add(tf.pow((X-cx),2),tf.pow((Y-cy),2)),0.5) mean = tf.reduce_mean(distance) cost = tf.reduce_sum(tf.pow((distance-mean),2)/3) # Gradient descent optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Initialize the variables (i.e. assign their default value) init = tf.global_variables_initializer() # Start training with tf.Session() as sess: sess.run(init) # Fit all training data for epoch in range(training_epochs): sess.run(optimizer, feed_dict={X: train_X, Y: train_Y}) c = sess.run(cost, feed_dict={X: train_X, Y:train_Y}) if (c - 0) < 0.0000000001: break #Display logs per epoch step if (epoch 1) % display_step == 0: c = sess.run(cost, feed_dict={X: train_X, Y:train_Y}) m = sess.run(mean, feed_dict={X: train_X, Y:train_Y}) print "Epoch:", 'd' % (epoch 1), "cost=", "{:.9f}".format(c), \ "CX=", sess.run(cx), "CY=", sess.run(cy), "Mean=", "{:.9f}".format(m) print "Optimization Finished!" training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y}) print "Training cost=", training_cost, "CX=", round(sess.run(cx),2), "CY=", round(sess.run(cy),2), "R=", round(m,2), '\n'

运行以上的python代码，结果如下：

Epoch: 0050 cost= 0.290830940 CX= 5.5859795 CY= 2.6425467 Mean= 5.657848835 Epoch: 0100 cost= 0.217094064 CX= 5.963002 CY= 3.0613017 Mean= 5.280393124 Epoch: 0150 cost= 0.173767462 CX= 5.997781 CY= 3.5245996 Mean= 4.885882378 Epoch: 0200 cost= 0.126330480 CX= 5.9999194 CY= 4.011508 Mean= 4.485837936 Epoch: 0250 cost= 0.078660280 CX= 5.9999976 CY= 4.4997787 Mean= 4.103584766 Epoch: 0300 cost= 0.038911112 CX= 5.9999976 CY= 4.945466 Mean= 3.775567770 Epoch: 0350 cost= 0.014412695 CX= 5.999998 CY= 5.2943544 Mean= 3.535865068 Epoch: 0400 cost= 0.004034557 CX= 5.999998 CY= 5.5200934 Mean= 3.390078306 Epoch: 0450 cost= 0.000921754 CX= 5.999998 CY= 5.6429324 Mean= 3.314131498 Epoch: 0500 cost= 0.000187423 CX= 5.999998 CY= 5.7023263 Mean= 3.278312683 Epoch: 0550 cost= 0.000035973 CX= 5.999998 CY= 5.7292333 Mean= 3.262284517 Epoch: 0600 cost= 0.000006724 CX= 5.999998 CY= 5.7410445 Mean= 3.255288363 Epoch: 0650 cost= 0.000001243 CX= 5.999998 CY= 5.746154 Mean= 3.252269506 Epoch: 0700 cost= 0.000000229 CX= 5.999998 CY= 5.7483506 Mean= 3.250972748 Epoch: 0750 cost= 0.000000042 CX= 5.999998 CY= 5.749294 Mean= 3.250416517 Epoch: 0800 cost= 0.000000008 CX= 5.999998 CY= 5.749697 Mean= 3.250178576 Epoch: 0850 cost= 0.000000001 CX= 5.999998 CY= 5.749871 Mean= 3.250076294 Epoch: 0900 cost= 0.000000000 CX= 5.999998 CY= 5.7499437 Mean= 3.250033140 Optimization Finished! Training cost= 9.8869656e-11 CX= 6.0 CY= 5.75 R= 3.25

经过900多次的迭代，圆心位置是（6.0，5.75），半径是3.25。

# Parameters learning_rate = 0.1 training_epochs = 3000 display_step = 50

learning_rate 是梯度下降的速率，这个值越大，收敛的越快，但也有可能会错过最优解
training_epochs是学习迭代的次数
display_step是每多少次迭代显示当前的计算结果

# Training Data, 3 points that form a triangel train_X = numpy.asarray([3.0,6.0,9.0]) train_Y = numpy.asarray([7.0,9.0,7.0]) # tf Graph Input X = tf.placeholder("float") Y = tf.placeholder("float") # Set vaibale for center cx = tf.Variable(3, name="cx",dtype=tf.float32) cy = tf.Variable(3, name="cy",dtype=tf.float32)

train_X,train_Y是三个点的x，y坐标，这里我们选了（3，7）（6，9）（9，7）三个点
X，Y是计算的输入，在计算过程中我们会使用训练数据输入X，Y
cx，cy是我们想要找的圆心点，初始值设置为（3，3），一般的学习算法会使用随机的初始值，这里我选了三角形中的一个点，这样做一般会减少迭代的次数。

# Caculate the distance to the center and make them as equal as possible distance = tf.pow(tf.add(tf.pow((X-cx),2),tf.pow((Y-cy),2)),0.5) mean = tf.reduce_mean(distance) cost = tf.reduce_sum(tf.pow((distance-mean),2)/3) # Gradient descent optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

这几行代码是算法的核心。

distance是利用两个点的距离公式算出三个点到圆心的距离
mean是三个距离的平均值
cost是三个距离的方差，我们的目标是让三个点到圆心的距离一样，也就是方差最小（cx/cy为圆心的时候，这个方差为零）
optimizer是梯度下降的训练函数，目标是使得cost（方差）最小

下面就是训练的过程了：

# Start training with tf.Session() as sess: sess.run(init) # Fit all training data for epoch in range(training_epochs): sess.run(optimizer, feed_dict={X: train_X, Y: train_Y}) c = sess.run(cost, feed_dict={X: train_X, Y:train_Y}) if (c - 0) < 0.0000000001: break #Display logs per epoch step if (epoch 1) % display_step == 0: c = sess.run(cost, feed_dict={X: train_X, Y:train_Y}) m = sess.run(mean, feed_dict={X: train_X, Y:train_Y}) print "Epoch:", 'd' % (epoch 1), "cost=", "{:.9f}".format(c), \ "CX=", sess.run(cx), "CY=", sess.run(cy), "Mean=", "{:.9f}".format(m) print "Optimization Finished!" training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y}) print "Training cost=", training_cost, "CX=", round(sess.run(cx),2), "CY=", round(sess.run(cy),2), "R=", round(m,2), '\n'