import numpy as np

# Matrix is positive definite if Cholesky Decomposition exists
def PositiveDefiniteMatrix(V):
  try:   np.linalg.cholesky(V); return True
  except np.linalg.LinAlgError: return False

def Generate_CorrMatrix(m,n):
  M = np.random.normal(0,1,(m,n))
  V = np.corrcoef(M,rowvar=False)#columns represents variables, rows contain data.
  print('\n Matrix M:             \n' + str(np.matrix(M)))
  print('\n Correlation matrix V: \n' + str(np.matrix(V)))
  print('\n Eigenvalues:          \n' + str(np.linalg.eig(V)[0]) + '\n')
  print('\n Positive definite?')
  return PositiveDefiniteMatrix(V)

Generate_CorrMatrix(m=3,n=4)

Generate_CorrMatrix(m=100,n=4)