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Python: Extract principal components


Number-to-word converter“Even Tree” Python implementationCompute the box covering on a graph using CPythonDividing a long (arbitrary-precision) number by an integerFinding connected components in PythonUsing bisect to flip coinsTwo-way data bindingCommand Line CalendarPython class for organizing images for machine learningPrint out numbers of increasing distance from center value in Python













0












$begingroup$


First, I am aware that this can be done in sklearn - I'm intentionally trying to do it myself.



I am trying to extract the eigenvectors from np.linalg.eig to form principal components. I am able to do it but I think there's a more elegant way. The part that is making it tricky is that, according to the documentation, the eigenvalues resulting from np.linalg.eig are not necessarily ordered. So to find the first principal component (and second and so on) I am sorting the eigenvalues, then finding their original indexes, then using that to extract the right eigenvectors. I am intentionally reinventing the wheel a bit up to the point where I find the eigenvalues and eigenvectors, but not afterward. If there's any easier way to get from e_vals, e_vecs = np.linalg.eig(cov_mat) to the principal components I'm interested.



import numpy as np

np.random.seed(0)
x = 10 * np.random.rand(100)
y = 0.75 * x + 2 * np.random.randn(100)

centered_x = x - np.mean(x)
centered_y = y - np.mean(y)

X = np.array(list(zip(centered_x, centered_y))).T

def covariance_matrix(X):
# I am aware of np.cov - intentionally reinventing
n = X.shape[1]
return (X @ X.T) / (n-1)

cov_mat = covariance_matrix(X)

e_vals, e_vecs = np.linalg.eig(cov_mat)

# The part below seems inelegant - looking for improvement
sorted_vals = sorted(e_vals, reverse=True)

index = [sorted_vals.index(v) for v in e_vals]

i = np.argsort(index)

sorted_vecs = e_vecs[:,i]

pc1 = sorted_vecs[:, 0]
pc2 = sorted_vecs[:, 1]









share|improve this question









$endgroup$
















    0












    $begingroup$


    First, I am aware that this can be done in sklearn - I'm intentionally trying to do it myself.



    I am trying to extract the eigenvectors from np.linalg.eig to form principal components. I am able to do it but I think there's a more elegant way. The part that is making it tricky is that, according to the documentation, the eigenvalues resulting from np.linalg.eig are not necessarily ordered. So to find the first principal component (and second and so on) I am sorting the eigenvalues, then finding their original indexes, then using that to extract the right eigenvectors. I am intentionally reinventing the wheel a bit up to the point where I find the eigenvalues and eigenvectors, but not afterward. If there's any easier way to get from e_vals, e_vecs = np.linalg.eig(cov_mat) to the principal components I'm interested.



    import numpy as np

    np.random.seed(0)
    x = 10 * np.random.rand(100)
    y = 0.75 * x + 2 * np.random.randn(100)

    centered_x = x - np.mean(x)
    centered_y = y - np.mean(y)

    X = np.array(list(zip(centered_x, centered_y))).T

    def covariance_matrix(X):
    # I am aware of np.cov - intentionally reinventing
    n = X.shape[1]
    return (X @ X.T) / (n-1)

    cov_mat = covariance_matrix(X)

    e_vals, e_vecs = np.linalg.eig(cov_mat)

    # The part below seems inelegant - looking for improvement
    sorted_vals = sorted(e_vals, reverse=True)

    index = [sorted_vals.index(v) for v in e_vals]

    i = np.argsort(index)

    sorted_vecs = e_vecs[:,i]

    pc1 = sorted_vecs[:, 0]
    pc2 = sorted_vecs[:, 1]









    share|improve this question









    $endgroup$














      0












      0








      0





      $begingroup$


      First, I am aware that this can be done in sklearn - I'm intentionally trying to do it myself.



      I am trying to extract the eigenvectors from np.linalg.eig to form principal components. I am able to do it but I think there's a more elegant way. The part that is making it tricky is that, according to the documentation, the eigenvalues resulting from np.linalg.eig are not necessarily ordered. So to find the first principal component (and second and so on) I am sorting the eigenvalues, then finding their original indexes, then using that to extract the right eigenvectors. I am intentionally reinventing the wheel a bit up to the point where I find the eigenvalues and eigenvectors, but not afterward. If there's any easier way to get from e_vals, e_vecs = np.linalg.eig(cov_mat) to the principal components I'm interested.



      import numpy as np

      np.random.seed(0)
      x = 10 * np.random.rand(100)
      y = 0.75 * x + 2 * np.random.randn(100)

      centered_x = x - np.mean(x)
      centered_y = y - np.mean(y)

      X = np.array(list(zip(centered_x, centered_y))).T

      def covariance_matrix(X):
      # I am aware of np.cov - intentionally reinventing
      n = X.shape[1]
      return (X @ X.T) / (n-1)

      cov_mat = covariance_matrix(X)

      e_vals, e_vecs = np.linalg.eig(cov_mat)

      # The part below seems inelegant - looking for improvement
      sorted_vals = sorted(e_vals, reverse=True)

      index = [sorted_vals.index(v) for v in e_vals]

      i = np.argsort(index)

      sorted_vecs = e_vecs[:,i]

      pc1 = sorted_vecs[:, 0]
      pc2 = sorted_vecs[:, 1]









      share|improve this question









      $endgroup$




      First, I am aware that this can be done in sklearn - I'm intentionally trying to do it myself.



      I am trying to extract the eigenvectors from np.linalg.eig to form principal components. I am able to do it but I think there's a more elegant way. The part that is making it tricky is that, according to the documentation, the eigenvalues resulting from np.linalg.eig are not necessarily ordered. So to find the first principal component (and second and so on) I am sorting the eigenvalues, then finding their original indexes, then using that to extract the right eigenvectors. I am intentionally reinventing the wheel a bit up to the point where I find the eigenvalues and eigenvectors, but not afterward. If there's any easier way to get from e_vals, e_vecs = np.linalg.eig(cov_mat) to the principal components I'm interested.



      import numpy as np

      np.random.seed(0)
      x = 10 * np.random.rand(100)
      y = 0.75 * x + 2 * np.random.randn(100)

      centered_x = x - np.mean(x)
      centered_y = y - np.mean(y)

      X = np.array(list(zip(centered_x, centered_y))).T

      def covariance_matrix(X):
      # I am aware of np.cov - intentionally reinventing
      n = X.shape[1]
      return (X @ X.T) / (n-1)

      cov_mat = covariance_matrix(X)

      e_vals, e_vecs = np.linalg.eig(cov_mat)

      # The part below seems inelegant - looking for improvement
      sorted_vals = sorted(e_vals, reverse=True)

      index = [sorted_vals.index(v) for v in e_vals]

      i = np.argsort(index)

      sorted_vecs = e_vecs[:,i]

      pc1 = sorted_vecs[:, 0]
      pc2 = sorted_vecs[:, 1]






      python reinventing-the-wheel






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked 56 mins ago









      jss367jss367

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