rdisipio

from sklearn.decomposition import PCA

pca = PCA(n_components=5)
X_pca = pca.fit_transform(X)

print("Relative importance (fraction of unity):")
print(pca.explained_variance_ratio_)

rdisipio

from sklearn.datasets import make_classification

n_features = 20
feature_names = [f"x_{n}" for n in range(n_features)]
class_names = ['0', '1']

X, y = make_classification(n_samples=1000, 
                           n_features=n_features, n_informative=5, 
                           n_redundant=5, n_repeated=0, 
                           n_classes=2, n_clusters_per_class=1,

rdisipio

import networkx as nx
import dimod
from dwave.system import LeapHybridDQMSampler

...

dqm = dimod.DiscreteQuadraticModel()
num_nodes = G.number_of_nodes()

for i in G.nodes:

rdisipio

y = [0 for _ in range(n)]

for i in range(n):
  for k in range(Ap[i], Ap[i+1]):
    j = Aj[k]
    y[i] += Ax[k] * v[j]

rdisipio

def csr_to_csc(m, n, Ax, Aj, Ap):
  nnz = len(Ax)

  Bx = [0 for _ in range(nnz)]
  Bi = [0 for _ in range(nnz)]
  Bp = [0 for _ in range(m+1)]

  # Count elements in each column:
  cnt = [0 for _ in range(n)]
  for k in range(nnz):

rdisipio

#!/usr/bin/env python

m = 3
n = 4

S = [[None for _ in range(n+1)] for _ in range(m+1)]

def explore(i, j):
  if i < 0 or j < 0 or i > m or j > n:
    return 0

rdisipio

#!/usr/bin/env python

m = 3
n = 4

S = [[None for _ in range(n+1)] for _ in range(m+1)]

for i in range(m):
  for j in range(n):
    if i==0 or j==0:

rdisipio

#!/usr/bin/env python

values = [5, 10, 15]
weights = [1, 2, 3]
W = 5
n = len(values)
 
S = [[None for _ in range(W + 1)] for _ in range(n + 1)]

def knapsack(w, v, W, i):

rdisipio

#!/usr/env/python

graph = {
  'A' : ['B','C'],
  'B' : ['D', 'E'],
  'C' : ['F'],
  'D' : [],
  'E' : [],
  'F' : []
}

rdisipio

#!/usr/env/python
graph = {
  'A' : ['B','C'],
  'B' : ['D', 'E'],
  'C' : ['F'],
  'D' : [],
  'E' : [],
  'F' : []
}