Question

我在 python3 中使用K-means进行教育。我有一个多维的聚类和质心数据集。我需要使用 t-SNE 在 2D 中可视化这些数据。

任何人都可以帮助我这样做。（关于代码的一点解释对我很有帮助。）

数据集如下：

质心：

      [0.0, 0.0, 1.125, 0.5, 0.25, 0.375, 0.125, 0.0, 0.75, 0.0, 0.0, 0.0, 0.0, 1.5, 0.5, 0.125, 0.0, 0.75, 0.25, 0.0, 1.75, 0.0, 0.0, 1.125, 0.125, 0.625, 0.25, 0.0, 0.25, 0.0, 0.625, 0.75, 0.0, 0.0, 0.625, 0.0, 0.75, 0.0, 0.625, 0.0, 0.0, 1.375, 0.625, 0.0, 1.0, 1.25, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.25, 0.625, 0.875, 0.0, 0.75, 1.25, 1.5, 0.0, 0.0]

      [1.6666666666666667, 1.5833333333333333, 0.4166666666666667, 0.16666666666666666, 0.16666666666666666, 0.08333333333333333, 0.08333333333333333, 0.0, 0.08333333333333333, 0.16666666666666666, 0.0, 0.0, 0.0, 1.3333333333333333, 1.0, 0.0, 0.0, 0.0, 0.3333333333333333, 0.0, 0.3333333333333333, 0.0, 0.08333333333333333, 0.08333333333333333, 0.16666666666666666, 0.0, 0.0, 0.25, 0.0, 0.0, 0.16666666666666666, 0.0, 0.0, 0.0, 1.1666666666666667, 1.1666666666666667, 0.0, 0.0, 0.0, 0.16666666666666666, 0.0, 0.0, 0.9166666666666666, 0.0, 0.0, 0.16666666666666666, 0.0, 0.16666666666666666, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.16666666666666666, 0.25, 0.0, 0.4166666666666667, 0.75, 0.4166666666666667, 0.0, 0.0]

      [0.0, 0.0, 0.16666666666666666, 0.6666666666666666, 0.16666666666666666, 1.6666666666666667, 1.6111111111111112, 0.2222222222222222, 0.5, 0.0, 0.05555555555555555, 1.2222222222222223, 0.5555555555555556, 0.0, 0.0, 0.1111111111111111, 0.05555555555555555, 0.0, 0.6666666666666666, 0.0, 0.6111111111111112, 0.0, 0.4444444444444444, 0.3333333333333333, 0.5, 0.0, 0.0, 0.05555555555555555, 0.0, 0.0, 0.05555555555555555, 0.16666666666666666, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.05555555555555555, 0.7222222222222222, 0.0, 0.0, 1.2777777777777777, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.9444444444444444, 0.05555555555555555, 0.3888888888888889, 0.0, 0.3888888888888889, 0.5, 0.4444444444444444, 0.3333333333333333, 0.0]

      [0.06976744186046512, 0.13953488372093023, 0.13953488372093023, 0.627906976744186, 0.11627906976744186, 0.37209302325581395, 0.18604651162790697, 0.023255813953488372, 0.0, 0.0, 0.0, 0.27906976744186046, 0.13953488372093023, 0.20930232558139536, 0.11627906976744186, 0.13953488372093023, 0.06976744186046512, 0.09302325581395349, 0.18604651162790697, 0.023255813953488372, 0.8372093023255814, 0.13953488372093023, 0.11627906976744186, 0.27906976744186046, 0.06976744186046512, 0.023255813953488372, 0.18604651162790697, 0.046511627906976744, 0.0, 0.0, 0.046511627906976744, 0.11627906976744186, 0.06976744186046512, 0.18604651162790697, 0.046511627906976744, 0.023255813953488372, 0.023255813953488372, 0.06976744186046512, 0.09302325581395349, 0.06976744186046512, 0.13953488372093023, 0.023255813953488372, 0.2558139534883721, 0.023255813953488372, 0.18604651162790697, 0.2558139534883721, 0.0, 0.23255813953488372, 0.18604651162790697, 0.023255813953488372, 0.06976744186046512, 0.11627906976744186, 0.0, 0.06976744186046512, 0.046511627906976744, 0.3023255813953488, 0.046511627906976744, 0.23255813953488372, 0.2558139534883721, 0.3023255813953488, 0.0, 0.09302325581395349]

      [0.0, 0.0, 0.5, 0.0, 1.5, 0.25, 0.0, 2.5, 1.75, 0.75, 1.0, 0.0, 0.0, 1.0, 0.0, 0.25, 1.5, 0.0, 0.5, 0.0, 0.0, 0.0, 0.0, 1.25, 0.5, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.0, 0.75, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.25, 0.0, 0.5, 0.25, 0.0, 0.0, 0.0, 1.5, 0.0, 0.0, 0.0, 0.5, 0.0, 0.0, 0.0, 0.5, 1.0, 0.0, 0.0, 0.0]

集群：

cluster_01：＆gt;

      [0 0 1 0 1 0 0 0 1 0 0 0 0 2 0 0 0 0 0 0 2 0 0 2 0 0 0 0 1 0 0 0 0 0 0 0 0 0 2 0 0 2 0 0 2 1 0 0 0 0 0 2 0 0 0 0 0 1 2 0 0 0]
      [0 0 1 0 1 0 0 0 2 0 0 0 0 2 0 0 0 0 0 0 2 0 0 2 0 0 0 0 1 0 0 0 0 0 0 0 0 0 2 0 0 2 0 0 2 1 0 0 0 0 0 2 0 0 0 0 0 1 2 0 0 0]
      [0 0 0 2 0 1 0 0 1 0 0 0 0 1 2 0 0 0 0 0 0 0 0 1 0 3 1 0 0 0 2 0 0 0 2 0 0 0 0 0 0 0 1 0 0 2 0 0 0 0 0 0 0 0 2 3 0 0 0 2 0 0]
      [0 0 0 2 0 1 0 0 1 0 0 0 0 1 2 0 0 0 0 0 0 0 0 1 1 2 1 0 0 0 2 0 0 0 2 0 0 0 0 0 0 0 1 0 0 2 0 0 0 0 0 0 0 0 2 2 0 0 0 2 0 0]
      [0 0 2 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 0 2 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 2 0 0 0 0 2 1 0 2 0 0 0 0 0 0 1 0 1 0 0 0 1 2 3 0 0]
      [0 0 2 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 0 2 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 2 0 0 0 0 2 1 0 2 0 0 0 0 0 0 2 0 1 0 0 0 1 2 2 0 0]
      [0 0 1 0 0 0 0 0 1 0 0 0 0 2 0 0 0 0 2 0 3 0 0 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 2 0 0 0 0 0 1 0 0 1 0 0 1 2 1 0 0]
      [0 0 2 0 0 1 1 0 0 0 0 0 0 0 0 1 0 2 0 0 3 0 0 1 0 0 0 0 0 0 1 2 0 0 0 0 2 0 1 0 0 2 0 0 0 2 0 0 0 0 0 0 0 0 0 2 0 1 0 2 0 0]

cluster_02：＆gt;

      [1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [2 2 0 0 1 0 0 0 1 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 2 0 0 0 0 0 0 1 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0]
      [1 2 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 2 0 0 0 1 0 2 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0]
      [2 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 2 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 2 2 0 0 0]
      [2 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 2 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 2 2 0 0 0]
      [0 3 2 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 2 0 0]
      [3 2 2 0 0 1 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 2 0 0 1 0 0 2 0 0 0 0 0 0 0 0 0 0 0 1 2 2 0 0]
      [3 3 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [0 2 0 0 0 0 0 0 0 1 0 0 0 2 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [2 1 0 0 0 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [2 1 0 1 0 0 0 0 0 0 0 0 0 2 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [2 1 0 1 0 0 0 0 0 0 0 0 0 2 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]

cluster_03：＆gt;

      [0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [0 0 0 2 0 2 1 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 2 0]
      [0 0 0 0 0 2 0 2 1 0 0 2 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 2 0 0 0 0 0 0 0 2 0 0 0 0 0 0 2 0]
      [0 0 0 2 0 1 2 0 1 0 1 0 0 0 0 1 0 0 2 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 2 0 0 0 0 0 0 0 1 0 2 0 1 0 0 0 0]
      [0 0 0 2 0 1 2 0 1 0 0 0 1 0 0 1 0 0 2 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 2 0 0 0 0 0 0 0 1 0 2 0 1 0 0 0 0]
      [0 0 0 0 0 2 1 1 0 0 0 2 1 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 1 0 1 0]
      [0 0 0 0 0 2 1 1 0 0 0 2 1 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 1 0 1 0]
      [0 0 1 0 0 2 1 0 1 0 0 2 1 0 0 0 0 0 0 0 1 0 2 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 2 0 0 0 0 0 0 0 1 0 1 0 0 1 2 0 0]
      [0 0 1 0 0 1 1 0 1 0 0 2 0 0 0 0 0 0 0 0 1 0 2 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 1 2 0 0]
      [0 0 0 2 1 2 3 0 1 0 0 1 0 0 0 0 0 0 3 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [0 0 0 2 1 2 2 0 1 0 0 1 0 0 0 0 0 0 2 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [0 0 0 0 0 2 1 0 0 0 0 2 0 0 0 0 0 0 0 0 3 0 2 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 0 0]
      [0 0 0 0 1 1 3 0 1 0 0 0 0 0 0 0 0 0 2 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 2 0 0 0 0 0 0 0 1 0 0 0 2 1 0 0 0]
      [0 0 1 0 0 2 1 0 1 0 0 3 2 0 0 0 0 0 1 0 1 0 2 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 0 0 0 0 1 1 0 0 2 2 2 0 0]
      [0 0 0 0 0 2 2 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [0 0 0 2 0 0 2 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0]
      [0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0]

cluster_04：＆gt;

      [0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0]
      [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [1 0 0 3 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2]
      [0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [0 1 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0]
      [0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [0 2 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [0 0 0 3 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [0 0 2 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0]
      [0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 2 2 0 0 0 0 0 0 1 0 0 1 2 0 0]
      [0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 2 0 0 0 0 0 0 1 0 0 1 1 0 0]
      [0 0 0 2 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 2 0 0 0 0 0 0 0 0 1 2 0 1 1 0 0 0]
      [0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 2 0 0 0 0 0 0 0 0 1 2 0 1 1 0 0 0]
      [0 0 0 2 0 0 3 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0]
      [0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [0 0 2 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0]
      [0 0 1 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 2 0 0 1 0 0 0 2 0 0 0 0 2 2 0 0 0 0 0 1 0 0 1 0 0 0 0 2 0 0 1 0 0 0 0 2 0 1 1 1 0 2]
      [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 2 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 2 0 0 0 0 0]
      [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 2 0 0 2 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 2 0 0 0 0 0 0 0 0 2 0 0]
      [0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 1 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 2 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 0]
      [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 2 0 0 0]
      [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [0 0 0 1 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 2 0 0 2 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 0 0 0 0 0 2 0 0 0 0 0 0 2 0 0 0 0 0 2 0 0 0 0]
      [0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 2 0 0 0 0 2 2 0 0 0 0 0 0 0 0 1 0 2 0 0]
      [0 0 0 0 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 2 0 0]
      [0 1 0 2 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [0 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0]
      [0 0 0 0 1 2 0 0 0 0 0 1 0 0 0 0 0 0 0 0 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [0 0 0 0 1 1 0 0 0 0 0 2 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0]
      [0 0 0 0 1 1 0 0 0 0 0 2 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0]
      [0 0 0 0 1 2 0 0 0 0 0 1 0 0 0 0 0 0 0 0 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]

cluster_05：＆gt;

      [0 0 0 0 1 0 0 2 2 1 0 0 0 0 0 0 0 0 2 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 2 1 0 0 0 1 0 0 0 0 0 0 0 2 0 0 0 0]
      [0 0 0 0 0 0 0 2 2 0 2 0 0 1 0 0 2 0 0 0 0 0 0 2 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0]
      [0 0 2 0 3 0 0 3 2 0 2 0 0 1 0 0 2 0 0 0 0 0 0 2 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 2 0 0 0 1 0 0 0 0 1 0 0 0]
      [0 0 0 0 2 1 0 3 1 2 0 0 0 2 0 1 2 0 0 0 0 0 0 1 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 2 0 0 0]

Answer 1

您可以在所有数据点上使用sklearn＆＃39; TSNE.fit_transform()并以新的缩小尺寸接收它们。

from sklearn.manifold import TSNE

all_nodes = clus1 + clus2 + clus3 + clus4 + clus5 + centroids
result = TSNE(n_components=2, learning_rate=100, early_exaggeration=50).fit_transform(all_nodes)

获得相同的情节：

import seaborn as sns
import pandas as pd

X = {
    'x': result[:,0],
    'y': result[:,1],
    'col' : ['clus1'] * len(clus1) + ['clus2'] * len(clus2) + ['clus3'] * len(clus3) + ['clus4'] * len(clus4) + ['clus5'] * len(clus5) + ['centroids'] * len(centroids),
}
data = pd.DataFrame(data=X)
sns.set(style="white", color_codes=True)
sns.lmplot( x="x", y="y", data=data, fit_reg=False, hue='col', 
       markers=['o', 'o', 'o', 'o', 'o', 'x'])

您可以自己调整TSNE的参数。您可以找到所有参数here。

<强>更新

如果你想在matplotlib中绘制图表，我很快将这段代码整理在一起：

group_sizes = [len(arr) for arr in [clus1, clus2, clus3, clus4, clus5]] 
colors = ['red', 'blue', 'green', 'purple', 'orange']
start_pos = 0
for idx, (pos, col) in enumerate(zip(group_sizes, colors)):
    plt.scatter(X['x'][start_pos:start_pos+pos], X['y'][start_pos:start_pos+pos], c=col, marker='o', label='Cluster {}'.format(idx+1))
    start_pos += pos
plt.scatter(X['x'][-5:], X['y'][-5:], c='black', marker='x', label='Centroids')
_ = plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)

输出

如何使用t-SNE可视化数据？

1 个答案: