Python StandardScaler.tolist方法代码示例

# 需要导入模块: from sklearn.preprocessing import StandardScaler [as 别名]
# 或者: from sklearn.preprocessing.StandardScaler import tolist [as 别名]
def check_clustering(name, Alg):
    X, y = make_blobs(n_samples=50, random_state=1)
    X, y = shuffle(X, y, random_state=7)
    X = StandardScaler().fit_transform(X)
    n_samples, n_features = X.shape
    # catch deprecation and neighbors warnings
    with warnings.catch_warnings(record=True):
        alg = Alg()
    set_fast_parameters(alg)
    if hasattr(alg, "n_clusters"):
        alg.set_params(n_clusters=3)
    set_random_state(alg)
    if name == 'AffinityPropagation':
        alg.set_params(preference=-100)
        alg.set_params(max_iter=100)

    # fit
    alg.fit(X)
    # with lists
    alg.fit(X.tolist())

    assert_equal(alg.labels_.shape, (n_samples,))
    pred = alg.labels_
    assert_greater(adjusted_rand_score(pred, y), 0.4)
    # fit another time with ``fit_predict`` and compare results
    if name is 'SpectralClustering':
        # there is no way to make Spectral clustering deterministic :(
        return
    set_random_state(alg)
    with warnings.catch_warnings(record=True):
        pred2 = alg.fit_predict(X)
    assert_array_equal(pred, pred2)

# 需要导入模块: from sklearn.preprocessing import StandardScaler [as 别名]
# 或者: from sklearn.preprocessing.StandardScaler import tolist [as 别名]
def check_transformer_general(name, Transformer):
    X, y = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]],
                      random_state=0, n_features=2, cluster_std=0.1)
    X = StandardScaler().fit_transform(X)
    X -= X.min()
    _check_transformer(name, Transformer, X, y)
    _check_transformer(name, Transformer, X.tolist(), y.tolist())

# 需要导入模块: from sklearn.preprocessing import StandardScaler [as 别名]
# 或者: from sklearn.preprocessing.StandardScaler import tolist [as 别名]
def check_transformer(name, Transformer):
    if name in ('CCA', 'LocallyLinearEmbedding', 'KernelPCA') and _is_32bit():
        # Those transformers yield non-deterministic output when executed on
        # a 32bit Python. The same transformers are stable on 64bit Python.
        # FIXME: try to isolate a minimalistic reproduction case only depending
        # on numpy & scipy and/or maybe generate a test dataset that does not
        # cause such unstable behaviors.
        msg = name + ' is non deterministic on 32bit Python'
        raise SkipTest(msg)

    X, y = make_blobs(n_samples=30, centers=[[0, 0, 0], [1, 1, 1]],
                      random_state=0, n_features=2, cluster_std=0.1)
    X = StandardScaler().fit_transform(X)
    X -= X.min()
    _check_transformer(name, Transformer, X, y)
    _check_transformer(name, Transformer, X.tolist(), y.tolist())

# 需要导入模块: from sklearn.preprocessing import StandardScaler [as 别名]
# 或者: from sklearn.preprocessing.StandardScaler import tolist [as 别名]
plt.title('Sklearn - estimated number of clusters: %d' % n_clusters_)
plt.grid()

#################################################################################
# our version
#################################################################################
import sys

sys.path.append("../db_scan")
from dbscan import DBscan
from point import Point

#################################################################################
# Compute DBSCAN
#################################################################################
xx = X.tolist()
point_array = []
for x in xx:
    pt = Point(x[0], x[1])
    point_array.append(pt)
# db = DBscan(point_array=parr, start_point_index= 0, cluster_map=clustmap, epsilon=EPS, min_neighbour=MIN_SAMPLES)
db = DBscan()
clusters = db.start(points=point_array, eps=EPS, minPts=MIN_SAMPLES)

#################################################################################
# Plot result
#################################################################################
plt.subplot(212)
colors = plt.cm.Spectral(np.linspace(0, 1, len(clusters)))

for p in point_array:

# 需要导入模块: from sklearn.preprocessing import StandardScaler [as 别名]
# 或者: from sklearn.preprocessing.StandardScaler import tolist [as 别名]
if os.path.exists("log.npy"):
    info = np.load("log.npy")
X=info



# Y = []
# for x in range(0,len(X)):
#   # print X[x][1]
#   if X[x][1]>400:
#     Y.append(X[x])
# X = Y
# print X

X = X.tolist();
# print X
mean =  np.mean(X,axis=0)
print mean
Xlength = len(X)
X_corrd = X
print Xlength
X.append([0,mean[1]])
X.append([1,mean[1]])
X.append([3,mean[1]])
X.append([4,mean[1]])
X.append([5,mean[1]])
X.append([1200,mean[1]])
X.append([1201,mean[1]])
X.append([1202,mean[1]])
X.append([1230,mean[1]])