import numpy as np
from filter import filter
from sklearn.manifold import TSNE
from sklearn import manifold
import pandas as pd
import warnings
warnings.filterwarnings("ignore")
# from tsne import bh_sne
import matplotlib.pyplot as plt
from sklearn.svm import SVC
from sklearn.neural_network import BernoulliRBM
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from filter import snn_sim_matrix
from filter import la
def Gene_Zeisel():
    data_nm="Zeisel"
    X = pd.read_csv("data/GSE76381.txt", sep='\t', low_memory=False).T
    # clrs =['#B03060', '#AEEEEE', '#68228B', 'y', 'c', 'm', '#2E2E2E', '#00008B', '#2E8B57', '#FAEBD7',
    #                  '#8B5A00', '#EEEE00', '#0000FF', '#ABABAB', '#8B8B00']
    clusters = np.array(X[7], dtype=str)[2:]
    cell_types, label = np.unique(clusters, return_inverse=True)
    data= np.array(X.iloc[2:, 11:],dtype=np.int)
    class_num = len(np.unique(label))
    # data = PCA(n_components=100).fit_transform(data)
    # data=snn_sim_matrix(data, k=class_num)
    # data= LinearDiscriminantAnalysis().fit_transform(data,label)
    # data=BernoulliRBM(n_components=50).fit_transform(data)
    data = TSNE(n_components=2,perplexity=42,angle=0.8).fit_transform(data)
    # print(data)
    # print(label)
    # print(class_num)
    # for k, clr in enumerate(clrs):
    #     cur = (label == k)
    #     plt.scatter(data[cur, 0], data[cur, 1], s=40, color=clr, edgecolors='k')
    # plt.show()

    return data,class_num,label,data_nm
456d2accab907953d91f877701d3a6f8b3f0885a
<mask token>

<mask token>


def Gene_Zeisel():
    data_nm = 'Zeisel'
    X = pd.read_csv('data/GSE76381.txt', sep='\t', low_memory=False).T
    clusters = np.array(X[7], dtype=str)[2:]
    cell_types, label = np.unique(clusters, return_inverse=True)
    data = np.array(X.iloc[2:, 11:], dtype=np.int)
    class_num = len(np.unique(label))
    data = TSNE(n_components=2, perplexity=42, angle=0.8).fit_transform(data)
    return data, class_num, label, data_nm

<mask token>
warnings.filterwarnings('ignore')
<mask token>


def Gene_Zeisel():
    data_nm = 'Zeisel'
    X = pd.read_csv('data/GSE76381.txt', sep='\t', low_memory=False).T
    clusters = np.array(X[7], dtype=str)[2:]
    cell_types, label = np.unique(clusters, return_inverse=True)
    data = np.array(X.iloc[2:, 11:], dtype=np.int)
    class_num = len(np.unique(label))
    data = TSNE(n_components=2, perplexity=42, angle=0.8).fit_transform(data)
    return data, class_num, label, data_nm

import numpy as np
from filter import filter
from sklearn.manifold import TSNE
from sklearn import manifold
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
from sklearn.svm import SVC
from sklearn.neural_network import BernoulliRBM
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from filter import snn_sim_matrix
from filter import la


def Gene_Zeisel():
    data_nm = 'Zeisel'
    X = pd.read_csv('data/GSE76381.txt', sep='\t', low_memory=False).T
    clusters = np.array(X[7], dtype=str)[2:]
    cell_types, label = np.unique(clusters, return_inverse=True)
    data = np.array(X.iloc[2:, 11:], dtype=np.int)
    class_num = len(np.unique(label))
    data = TSNE(n_components=2, perplexity=42, angle=0.8).fit_transform(data)
    return data, class_num, label, data_nm

import numpy as np
from filter import filter
from sklearn.manifold import TSNE
from sklearn import manifold
import pandas as pd
import warnings
warnings.filterwarnings("ignore")
# from tsne import bh_sne
import matplotlib.pyplot as plt
from sklearn.svm import SVC
from sklearn.neural_network import BernoulliRBM
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from filter import snn_sim_matrix
from filter import la
def Gene_Zeisel():
    data_nm="Zeisel"
    X = pd.read_csv("data/GSE76381.txt", sep='\t', low_memory=False).T
    # clrs =['#B03060', '#AEEEEE', '#68228B', 'y', 'c', 'm', '#2E2E2E', '#00008B', '#2E8B57', '#FAEBD7',
    #                  '#8B5A00', '#EEEE00', '#0000FF', '#ABABAB', '#8B8B00']
    clusters = np.array(X[7], dtype=str)[2:]
    cell_types, label = np.unique(clusters, return_inverse=True)
    data= np.array(X.iloc[2:, 11:],dtype=np.int)
    class_num = len(np.unique(label))
    # data = PCA(n_components=100).fit_transform(data)
    # data=snn_sim_matrix(data, k=class_num)
    # data= LinearDiscriminantAnalysis().fit_transform(data,label)
    # data=BernoulliRBM(n_components=50).fit_transform(data)
    data = TSNE(n_components=2,perplexity=42,angle=0.8).fit_transform(data)
    # print(data)
    # print(label)
    # print(class_num)
    # for k, clr in enumerate(clrs):
    #     cur = (label == k)
    #     plt.scatter(data[cur, 0], data[cur, 1], s=40, color=clr, edgecolors='k')
    # plt.show()

    return data,class_num,label,data_nm