def arquitetura(tam_entrada, tam_hidden, tam_saida):
m = []
matriz_hidden = mat_aleatoria(-1,1,tam_hidden,tam_entrada) # cada linha representa um neuronio da camada escondida
matriz_saida = mat_aleatoria(-1,1, tam_saida, tam_hidden) # cada linha representa um neuronio da camada de saida
m.append(matriz_hidden)
m.append(matriz_saida)
return m
def gpa():
g = 0
civ = []
c = 0
math = []
m = 0
lang = []
l = 0
sci = []
s = 0
civn = input("how many humanitities do you take?")
for i in range(0, civn):
civ.append(f(raw_input("whats your civ grade?")))
mathn = input("how many maths do you take?")
for j in range(0, mathn):
math.append(f(raw_input("whats your math grade?")))
langn = input("how many languages do you take?")
for k in range(0, langn):
lang.append(f(raw_input("whats your language grade?")))
scin = input("how many sciences do you take?")
for n in range(0, scin):
sci.append(f(raw_input("whats your science grade?")))
for ii in range(0, len(civ)):
c = c + civ[ii]
for jj in range(0, len(math)):
m = m + math[jj]
for kk in range(0, len(lang)):
l = l + lang[kk]
for nn in range(0, len(sci)):
s = s + sci[nn]
g = (c + m + l + s) / (len(civ) + len(math) + len(lang) + len(sci))
print g
def read_data(fobj):
reader = csv.DictReader(fobj, delimiter=',')
math = []
read = []
write = []
for line in reader:
math.append(int(line['Math']))
read.append(int(line['Reading']))
write.append(int(line['Writing']))
return math, read, write
def get_M(W, X, y_list):
"""
Calculate the predict values and return missclassified data points set M
"""
M = []
for i, xt in enumerate(X):
pred_val = cal_h_W_Xt(W, xt)
if not pred_val == y_list[i]:
M.append(xt)
return M
def matriz(arq): # inicializa a matriz
start = ""
m = []
i = 0
for lin in arq:
if (i > 5): # descarta as 5 linhas iniciais
m.append([float(x) for x in lin.split()])
else:
start += str(lin).replace("b'", "").replace("\\n'", "\n")
i += 1
return m, start # retorna um array
def GP_model_test(sample_x, sample_y, sample_std, test_x, gamma, samples):
sample_x = np.array(sample_x)
cov_x = RBF_ker(sample_x, sample_x, gamma)
d = np.diag(np.array(sample_std)**2)
lower_cholesky = cholesky(cov_x + d, True)
weighted_sample_y = cho_solve((lower_cholesky, True), sample_y)
cov_te_sam = RBF_ker(test_x, sample_x, gamma)
m = []
for i in cov_te_sam:
m.append(cho_solve((lower_cholesky, True), i))
cov = RBF_ker(test_x, test_x, gamma) - np.dot(cov_te_sam, np.array(m).T)
mean = np.dot(cov_te_sam, weighted_sample_y)
N = np.random.multivariate_normal(mean, cov, samples)
return N
def math():
b, c = entry1.get(), entry2.get()
b, c = float(b), float(c)
x = float((b**2) - (4 * c))
x = s(x)
x = x / 2
new = float((b**2) - (4 * c))
new = s(new)
new = (-b) - new
new = new / 2
math = []
math.append(x)
math.append(new)
answer.delete(0, END)
answer.insert(0, math)
def continue_math(count, yt, framerate):
x = []
math = []
for i, data in enumerate(count):
x.append(data)
if (i + 1) % 2 == 0:
y1 = yt[x[0]:x[1]]
yf1 = abs(fft(y1)) / len(y1)
yf2 = yf1[range(int(len(y1) / 2))]
math_ = catch_number(yf2, framerate, x[1] - x[0])
x = []
if math_ == None:
continue
math.append(math_)
return math
def math():
b = entry1.get()
b = float(b)
c = entry2.get()
c = float(c)
x = float((b**2) - (4 * c))
x = a(x)
x = (-b) + x
x = x / 2
x1 = float((b**2) - (4 * c))
x1 = a(x1)
x1 = (-b) - x1
x1 = x1 / 2
math = []
math.append(x)
math.append(x1)
answer.delete(0, END)
answer.insert(0, math)
import math
#low=[]
#high=[]
#var=0
df = pd.read_excel('std4.xlsx')
#with pd.option_context('display.max_rows', None, 'display.max_columns', None): # more options can be specified also
#print(df)
#for i in range(len(df)) :
#print(df.loc[i, "Sess1"], df.loc[i, "Subjects"])
#print(df.)
math = []
sum_math = 0
i = 0
j = 0
while i < len(df):
math.append(df.iloc[i, 8])
sum_math = sum_math + math[j]
i = i + 5
j = j + 1
#print(math,end=" ")
#print(sum_math)
i = 1
j = 0
phy = []
sum_phy = 0
while i < len(df):
phy.append(df.iloc[i, 8])
sum_phy = sum_phy + phy[j]
i = i + 5
j = j + 1
#print(phy)
a0 = a0 + dela[0]
a1 = a1 + dela[1]
#monte carlo using computer generated random numbers
#iterations=int(input("number of iterations: "))
iterations = 1000
cir = 0
sq = 0
m = []
n = []
cirx = []
ciry = []
for i in range(iterations):
x = r.random()
y = r.random()
m.append(x)
n.append(y)
a = (x * x) + (y * y)
if a <= 1:
cirx.append(x)
ciry.append(y)
cir += 1
sq = i
pi = 4 * (cir / sq)
print("{:0.6f}".format(pi))
#monte carlo using manually generated random numbers (32 bit)
x0 = 12356789
y0 = 87238971
c = 16807
N = 2147483641 #($2^31 - 1)
