puraminy

import seaborn as sn
from numpy import newaxis
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, f1_score, average_precision_score, recall_score
from sklearn.metrics import precision_recall_fscore_support as score
import matplotlib.pyplot as plt
from sklearn import preprocessing
from sklearn.model_selection import train_test_split

def plot_heat_map(ax, X, Y, Z, xlabel, ylabel, format='d', title='Heat Map'):
 

puraminy

import numpy as np

NaN = np.nan

import pandas as pd

# The ratings by users (U)
df = pd.DataFrame(np.array([[8,NaN,10,NaN,10],
                           [NaN,2,NaN,3,3],
                           [2,8,NaN,4,1],

puraminy

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import networkx as nx


df = pd.DataFrame(np.zeros((5,14)), columns = ['علی','مدرسه', 'را', 'دوست','دار',
                             'برای','او','زندان', 'است', 'کمی','در','درس','خوان', 'بازیگوش'])

s1 = 'علی مدرسه را دوست دار'

puraminy

import numpy as np
from scipy.stats import multivariate_normal
from sklearn.feature_selection import VarianceThreshold
from sklearn.metrics import accuracy_score
import seaborn as sn
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, f1_score
import matplotlib.pyplot as plt
from sklearn import preprocessing

puraminy

import numpy as np
from sklearn import preprocessing
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import itertools
import seaborn as sn
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, f1_score
import matplotlib.pyplot as plt

puraminy

import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn import preprocessing
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import RadiusNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
import matplotlib.pyplot as plt

puraminy

import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn import preprocessing
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import RadiusNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
import matplotlib.pyplot as plt

puraminy

import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn import preprocessing
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import RadiusNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
import matplotlib.pyplot as plt

puraminy

import nltk, re, pprint
from nltk import word_tokenize
import numpy as np
import pandas as pd
import heapq
import string 

from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression

puraminy

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from numpy import array
from numpy import mean
from numpy import cov
from numpy.linalg import eig, svd