In [79]:
%qtconsole
In [2]:
import numpy as np
import numbapro
from numbapro import *
import matplotlib.pyplot as plt
import seaborn as snb
%matplotlib inline
from sklearn.datasets import make_blobs
n_samples = 1e5
n_centers = 6
n_features = 2
n_samples = int(n_samples)
In [22]:
data, gt = make_blobs(n_samples, n_features, n_centers)
data = data.astype(np.float32)
fig1=plt.figure()
for l in np.unique(gt):
l_data = gt == l
plt.plot(data[l_data,0], data[l_data,1], '.')
plt.savefig('foo.eps', dpi=None, bbox_inches='tight')
In [23]:
type(fig1)
Out[23]:
In [32]:
import matplotlib.figure
In [34]:
isinstance(fig1,matplotlib.figure.Figure)
Out[34]:
In [10]:
ls
In [9]:
plt.savefig('foo.png', bbox_inches='tight')
In [67]:
import MyML.cluster.K_Means3 as myKM
In [80]:
reload(myKM)
Out[80]:
In [76]:
iters = 3
n_clusters = 100
init_seed = np.random.randint(0, n_samples, n_clusters)
init = data[init_seed]
estGPU = myKM.K_Means(n_clusters=n_clusters, mode='cuda', cuda_mem="manual", max_iter=iters, init=init)
estCPU = myKM.K_Means(n_clusters=n_clusters, mode='numpy', max_iter=iters, init=init)
estGPU2 = myKM.K_Means(n_clusters=n_clusters, mode='cuda', cuda_mem="manual", max_iter=iters, init=init)
estGPU2._centroid_mode="good_numba"
estCPU2 = myKM.K_Means(n_clusters=n_clusters, mode='numpy', max_iter=iters, init=init)
estCPU2._centroid_mode="good_numba"
estCPU3 = myKM.K_Means(n_clusters=n_clusters, mode='numba', max_iter=iters, init=init)
estCPU3._centroid_mode="good_numba"
estCPU4 = myKM.K_Means(n_clusters=n_clusters, mode='numba', max_iter=iters, init=init)
estCPU4._centroid_mode="good"
In [78]:
print estGPU.labels_
print estGPU2.labels_
print estCPU.labels_
print estCPU2.labels_
print estCPU3.labels_
print estCPU4.labels_
In [77]:
%time estGPU2.fit(data)
%time estGPU.fit(data)
%time estCPU.fit(data)
%time estCPU2.fit(data)
%time estCPU3.fit(data)
%time estCPU4.fit(data)
In [4]:
from numba import jit
In [91]:
@jit(nopython=True)
def test():
a=np.empty((10,10))
b = np.zeros((10,10))
return a, b