然后，我们可以定义函数来绘制直方图：import matplotlib.pyplot as pltimport seaborn as snsimport numpy as npminnpf = np.frompyfunc(lambda x, y: min(x,y), 2, 1)maxnpf = np.frompyfunc(lambda x, y: max(x,y), 2, 1)def get_custom_histogram_info(profiles, variable, n_bins):summaries ...
fromsklearn.datasetsimportfetch_mldatafromsklearn.manifoldimportTSNEfromsklearn.decompositionimportPCAimportseaborn as snsimportnumpy as npimportmatplotlib.pyplot as plt#get mnist datamnist = fetch_mldata("MNIST original") X= mnist.data / 255.0y=mnist.target#first reduce dimensionality before feeding to...
from sklearn.datasets import fetch_mldata from sklearn.manifold import TSNE from sklearn.decomposition import PCA import seaborn as sns import numpy as np import matplotlib.pyplot as plt # get mnist data mnist = fetch_mldata("MNIST original") X = mnist.data / 255.0 y = mnist.target # firs...
from utils import * import time import numpy as np from mxnet import nd, autograd, gluon from mxnet.gluon import nn, rnn import mxnet as mx import datetime import seaborn as sns import matplotlib.pyplot as plt %matplotlib inline from sklearn.decomposition import PCA import math from sklearn....
from utils import * import time import numpy as np from mxnet import nd, autograd, gluon from mxnet.gluon import nn, rnn import mxnet as mx import datetime import seaborn as sns import matplotlib.pyplot as plt %matplotlib inline from sklearn.decomposition import PCA import math from sklearn....
Consider each distribution has only 2 margins (please correct me if necessary), how to plot a marginal histograms ofHere isthe4 features?code, importmatplotlib.pyplotaspltfromsklearnimportdatasetsimportseabornassns; sns.set(style="white", color_codes=True) ...
from utils import * import time import numpy as np from mxnet import nd, autograd, gluon from mxnet.gluon import nn, rnn import mxnet as mx import datetime import seaborn as sns import matplotlib.pyplot as plt %matplotlib inline from sklearn.decomposition import PCA import math from sklearn....
from utils import * import time import numpy as np from mxnet import nd, autograd, gluon from mxnet.gluon import nn, rnn import mxnet as mx import datetime import seaborn as sns import matplotlib.pyplot as plt %matplotlib inline from sklearn.decomposition import PCA import math from sklearn....
from utils import * import time import numpy as np from mxnet import nd, autograd, gluon from mxnet.gluon import nn, rnn import mxnet as mx import datetime import seaborn as sns import matplotlib.pyplot as plt %matplotlib inline from sklearn.decomposition import PCA import math from sklearn....
from utils import * import time import numpy as np from mxnet import nd, autograd, gluon from mxnet.gluon import nn, rnn import mxnet as mx import datetime import seaborn as sns import matplotlib.pyplot as plt %matplotlib inline from sklearn.decomposition import PCA import math from sklearn....