*Anonymous Coward*
2018-03-05 13:06:08.862005 UTC

1 | #sigma |

2 | for i in range(Nclasses): |

3 | sigma_temp = np.zeros((Ndims)) |

4 | #Collect vectors from class k |

5 | for j in range(Npts): |

6 | if labels[j] == classes[i]: |

7 | x = X[j,:] |

8 | #Subtract mu |

9 | x = x - mu[i,:] |

10 | #Square each element individually |

11 | x = np.square(x) |

12 | #Sum over vectors to gain a single vector |

13 | sigma_temp += x |

14 | #Divide by number of vectors in k |

15 | sigma_temp /= N_k[i] |

16 | #Convert to a diagonal matrix |

17 | sigma[i,:,:] = np.diag(sigma_temp) |