PythonFull

# 1   Jest
# 2   Jest
# 3   Jest
# 4   Jest
# 5   xxx
# 6   xxx
# 7   Jest
# 8   Jest
# 9   50 %
# 10  Jest
# 11  Jest
# 12
# 13  Jest
# 14
# 15

#          ZADANIE 1
#import numpy as np
#
#array_1 = np.arange(0, 101)
#array_2 = np.arange(100, -1, -1)
#print(array_1)
#print(array_2)
#print(array_1 + array_2)
#print(array_1 - array_1)
#print(array_1 * array_2)
#print(array_1[:10] - array_1[-10:])
#print(array_1[array_1 % 3 == 0])


#000000000000000000000000000000000000000000000000000000000000000000000000000

#          ZADANIE 2

#import numpy as np
#import time
#import csv
#
#
#b_t = time.time()
#for item in list_1:
#	list_2.append(item*item)
#e_t = time.time()
#print("Elapsed time: ", e_t - b_t)
#
#list_1 = list(range(0, 10000000))
#b_t = time.time()
#list_2 = [item*item for item in list_1]
#e_t = time.time()
#print("Elapsed time: ", e_t - b_t)
#
#array_1 = np.arange(0, 10000000)
#b_t = time.time()
#array_2 = array_1 * array_1
#e_t = time.time()
#print("Elapsed time: ", e_t - b_t)



#000000000000000000000000000000000000000000000000000000000000000000000000000

#           ZADANIE 3
#import random
#import numpy as np
#import matplotlib.pyplot as plt
#
#
#tab = np.random.normal(50,20,(7,7))
#tab1 =np.round(tab)
#
#

#count, bins, ignored = plt.hist(tab1, 30, density=True)
#plt.plot(bins, 1/(20 * np.sqrt(2 * np.pi)) *
#               np.exp( - (bins - 50)**2 / (2 * 20**2) ),
#         linewidth=3, color='r')
#plt.show()


#print("Wyznacznik: {}".format(np.linalg.det(tab1)))
#print("Slad: {}".format(np.trace(tab1)))
#print("Wartosci i wketory wlasne {}".format(np.linalg.eig(tab)))
#print("Wektory wlasne: {}".format(np.dot(tab1)))

#Elementy tablicy razy wektor
#print("Matrix Tab przemnzona przez wektor [1,2,3,4,5,6,7]\n{}".format(np.dot(tab1,[1,2,3,4,5,6,7])))

#RevTab = np.linalg.inv(tab1)     #Macierz odwrotna

#Przkątna macierzy do kwadratu
#print("Na przekatnej macierzy mamy:\n{}".format(np.square(np.diagonal(tab1))))

#print(np.linalg.svd(tab1))    <-- SVD macierzy

#000000000000000000000000000000000000000000000000000000000000000000000000000



#Zadanie 4
#import os
#import numpy as np
#import csv
#
#
#current_dir = os.path.abspath(os.path.dirname(__file__))
#data_folder = os.path.join(current_dir, "Data")
#csv_path = os.path.join(data_folder, "iris.csv")
#
#
#with open(csv_path) as csv_file:
#	output_dict = dict()
#	reader = csv.reader(csv_file)
#	first_row = next(reader)
#	for key in first_row:
#		output_dict[key] = []
#	for row in reader:
#		for i in range(len(first_row)):
#			try:
#				output_dict[first_row[i]].append(float(row[i]))
#			except:
#				output_dict[first_row[i]].append(row[i])
#
#	for key in output_dict.keys():
#		output_dict[key] = np.array(output_dict[key])
#
#print("Septal Length mean: ", np.mean(output_dict['sepal.length']))
#print("Septal Length std: ", np.std(output_dict['sepal.length']))
#print("Septal Width mean: ", np.mean(output_dict['sepal.width']))
#print("Septal Width std: ", np.std(output_dict['sepal.width']))
#print("Petal length mean: ", np.mean(output_dict['petal.width']))
#print("Petal length std: ", np.std(output_dict['petal.length']))
#print("Petal width mean: ", np.mean(output_dict['petal.length']))
#print("Petal width std: ", np.std(output_dict['petal.length']))
#


#000000000000000000000000000000000000000000000000000000000000000000000000000

# ZADANIE 5

#current_dir = os.path.abspath(os.path.dirname(__file__))
#data_path = os.path.join(current_dir, "Data")
#csv_path = os.path.join(data_path, ".png")
##print(current_dir)
#try:
#    data_file = open(csv_path)
#except:
#    pass
#finally:
#    pass
#with open(csv_path) as csv_file:
#    reader = csv.reader(csv_file)
#    print(reader)
#    #print(dir(reader))
#    for item in reader:
#        print(item)

#000000000000000000000000000000000000000000000000000000000000000000000000000

#Zadanie 7 , blad MSE
#
#import numpy as np
#
##array_1 = np.arange(1000, dtype=np.float32)
#array_1 = np.float32(np.random.normal((1000,1)))
#array_1_export = array_1 * 1000
#
#array_1_export.tofile('zad7.txt')
#
#array_2_import = np.fromfile('zad7.txt',dtype=np.float32)
#array_2 = array_2_import /1000
#
##print(array_2[:25])
#
#ax=0
#mse = (np.square(array_1 - array_2)).mean(axis=ax)
#print("MSE: {}".format(mse))
#
#
#000000000000000000000000000000000000000000000000000000000000000000000000000
##Zadanie 8
#import numpy as np
#
#tab_1 = np.ndarray((100,100,50))
#
#for i in range(50):
#    tab_1[:,:,i] = np.asmatrix(np.random.rand(100,100))
#    print("Wyznacznik macierzy (", i,"):",np.linalg.det(tab_1[:,:,i]))
#
#wektory = np.asmatrix(np.random.rand(50,100))
#000000000000000000000000000000000000000000000000000000000000000000000000000
##Zadanie 9
#import numpy as np
#
#tab = np.random.rand(5,5)
#tab1 = np.random.rand(5,5)
#tab2=tab*tab1
#print(np.diag(tab*tab1))   #Metoda 1

#j=-1
#for i in range(0,len(tab)):
#    j = j + 1
#    print(tab2[i,j])

#000000000000000000000000000000000000000000000000000000000000000000000000000
#Zadanie 10
#import numpy as np
#
#vector = np.arange(0,10)
#for i in range(0,len(vector)):
#    if i % 2 == 0:
#        vector[i] = vector[i] * vector[i]
#    else:
#        vector[i] = 0
#
#print(vector)
#



#000000000000000000000000000000000000000000000000000000000000000000000000000
#Zadanie 11
#import numpy as np
#
#def largest_num(array,count):
#    array_out = []
#    for i in range(0,count):
#        array_out.append(np.amax(array))
#        array = np.delete(array,array.argmax(axis=0))
#    return array_out
#
#array_1 = np.arange(0,1000)
#print(largest_num(array_1,3))
#
#000000000000000000000000000000000000000000000000000000000000000000000000000

#   Zadanie 12

#N = 10
#array = np.random.rand(N)
##print(array)
#array.flags.writeable =  False
#try:
#    array[5] = 7
#except Exception as e:
#    print(e)


#Zadanie 13
#import numpy as np
#
#def rank(A, eps=1e-12):
#    u, s, vh = np.linalg.svd(A)
#    return len([x for x in s if abs(x) > eps])
#
#array_1 = ([1,2,3],[3,4,5],[5,3,3])
#print(rank(array_1))
#print(np.linalg.matrix_rank(array_1))


#000000000000000000000000000000000000000000000000000000000000000000000000000


#Zadannie 14
#Zdefiniuj ustrukturyzowana tablice zawierajaca w kazdym polu pozycje (x, y, z), a nastepnie wypełnij ja
#losowymi wartosciami.


#N=random.randint(0,9)
#print(N)
#x = np.random.rand()
#y = np.random.rand()
#z = np.random.rand()
#
#array =[(x, y, z) for i in range(N)]
#print(array)

 #000000000000000000000000000000000000000000000000000000000000000000000000000

#Zadanie 15
#Zdefiniuj losowy wektor liczb zmiennoprzecinkowych o losowej długosci. Zamien wszystkie wartosci stanowiace
# wartosc minimalna wartoscia srednia. Powtórz tak długo az nie zostania dokonana zadna zmiana.

#N = random.randrange(0,9) #losowanie dlugosci wektora
##print(N)
#vector = np.random.rand(N)
##print(vector)
#minimum = np.amin(vector)
##print(minimum)
#mean_value = np.mean(vector)
##print(mean_value)
#for i in range(0, N):
#    vector[i]= mean_value
#print(vector)