def find_min_brute(f, a, b, h):
for i in np.arange(a, b, h):
low = f(i)
if (f(i + h) < low):
lowest_value = low
x_position = i
print("Minimum at x = ", x_position, "with value of:", lowest_value)
def znajdz_kolege(wyniki): # metoda ruletki
tablica = np.array(wyniki)
temp = tablica.argsort(
) # posortowane od najmniejszego do największego indexy wynikow
rank = np.empty_like(temp)
rank[temp] = np.arange(len(tablica))
dopasowanie = [len(rank) - x for x in rank]
c_wyniki = copy.deepcopy(dopasowanie)
for i in range(1, len(c_wyniki)):
c_wyniki[i] = dopasowanie[i] + c_wyniki[i - 1]
prawd = [x / c_wyniki[-1] for x in c_wyniki]
rand = random.random()
for i in range(0, len(prawd)):
if rand < prawd[i]:
return i
import pandas as pd df = pd.arange(1, 10) print(df)
df['comp_score'] = df['compound'].apply(lambda c: 'pos' if c >= 0 else 'neg')
#df.head()
c = 0 #neg
d = 0 # positive
k = df['comp_score']
p = list(k)
o = len(p)
for i in p:
if i == 'neg':
c = c + 1
else:
d = d + 1
c = (c / o) * 100
d = (d / o) * 100
import matplotlib.pyplot as plt
plt.rcdefaults()
import numpy as np
import matplotlib.pyplot as plt
objects = ('Positive', 'Negative')
y_pos = np.arange(len(objects))
performance = [d, c]
plt.bar(y_pos, performance, align='center', alpha=0.5)
plt.xticks(y_pos, objects)
plt.ylabel('% of people')
plt.title('sentiment')
plt.show()
# -*- coding: utf-8 -*- """ Created on Wed Nov 22 11:02:27 2017 @author: IFPB """ import pandas as np import numpy as np import matplotlib.pyplot as plt y = [100, 12, 44] x = np.arange(1) espacamento = 0.5 cor = 'red' plt.bar(x, y[0], width = espacamento, color = 'y', label = 'total de pessoas') plt.bar(x + 0.6, y[1], width = espacamento, color = 'r', label = '% de pessoas com mais de 5 acidentes(12%)') plt.bar(x + 1.2, y[2], width = espacamento, color = 'b', label = '% de pessoas entre 2 e 4 acidentes(44%)') plt.legend()
cls.fit(traning_x,traning_y) # traning the data set
# to we will predict the value
y_pred=cls.predict(test_x)
y_pred
#now to compare with the actual value
test_y
# now to check the number of right predictiona and wrong prediction, we will use confusion matrics
c_m=confusion_matrix(test_y,y_pred) #it fuction require actual variable and predict vaiable
c_m # to see the detail
#now to see the training plot data
x_set,y_set = traning_x,traning_y
x1,x2 = np.meshgrid(np.arange(start= x_set[:,0].min()-1,stop= x_set[:,0].min()+1,step=0.01)
np.arange(start= x_set[:,1].min()-1,stop= x_set[:,1].min()+1,step=0.01)
plt.counterf(x1,x2, cls.predict(np.array[x1.ravel(),x2.ravel()]).T).reshape(x1.shape),
alpha=0.75, cmap=ListedColormap(('red','green')))
plt.xlim(x1.min(),x1.max())
plt.ylim(x2.min(),x2.max())
for i, j in enumerate (np.unique(y_set)):
plt.scatter(x_set[y_set==j,0], x_set[y_set==j,1],
c = ListedColormap(('red','green'))(i),label = j)
plt.title('K-NN (Traningin Set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
figure = plt.figure(figsize=(27, 20))
j = 1
for i, feature1 in enumerate(features_list2[1:]):
y_name = features_list2[0]
key = feature1
bin_step = 20
all_data = df[[y_name, key]]
# Remove NaN values from Age data
all_data = all_data[~np.isnan(all_data[key])]
# Divide the range of data into bins and count survival rates
min_value = all_data[key].min()
max_value = all_data[key].max()
value_range = max_value - min_value
bins = np.arange(min_value, max_value + value_range/bin_step, value_range/bin_step )
y0 = all_data[all_data[y_name] == 0][key].reset_index(drop = True)
y1 = all_data[all_data[y_name] == 1][key].reset_index(drop = True)
ax = plt.subplot(len(features_list2)/4+1, 4, j)
ax.hist(y0, bins = bins, alpha = 0.6, color = 'red', label = 'y0')
ax.hist(y1, bins = bins, alpha = 0.6, color = 'green', label = 'y1')
ax.set_xlim(bins.min(), bins.max())
ax.set_title(key)
# ax.legend(framealpha = 0.8)
j+=1
figure.subplots_adjust(left=.02, right=.98)
plt.show()
outlier_name1 = str(df['name'][df.salary ==df['salary'].max()])
outlier_name2 = str(df['name'][df.bonus ==df['bonus'].max()])