class Stack(Z):
x = X()
y = Y()
def __init__(self):
self.items = []
def is_Empty(self):
return self.items == []
def push(self, item):
self.items.append(item)
def pop(self):
return self.items.pop()
def size(self):
return len(self.items)
def stack_1(self):
self.a.a_1()
self.b.b_1()
def stack_2(self, num):
self.x_1(num)
self.x.y_2()
self.y_2()
def __init__(self, parent=None):
super(Y_info, self).__init__(parent)
label = QLabel(self)
label.setGeometry(QRect(550, 10, 90, 90))
pixmap = QPixmap('img15.jpg')
pixmap = pixmap.scaledToWidth(90)
label.setPixmap(pixmap)
self.ui = Y.Ui_Form()
self.ui.setupUi(self)
import Y y = Y.init(4) y1 = Y.value(y, 1) print y1
Statistical Learning
X(inputs) is usually denoted by a subscript to distinguish them and they
go by many different names; predictors, independent variables,
features, or sometimes just variables.
often denoted as Y(output) are more called as output, ['response and dependent variables.']
Y = ƒ(X) + ε
ƒ(X) ; a fixed but unknown function of X1,...Xp,
ε ; the random error term, which is independent of X and has a mean of 0.
∴ ƒ represents the systematic information that X provides about Y.
refer to page 17; fig 2.2
ƒ which is generally unknown (can be known when simulated)
- is the function of X and the line that shows the connection
between X and Y. ( line)
ε is represented by the vertical lines between the lines and
point, also known as the error term.
['note: some errors are positive/negative depending on where they lie in ƒ']
```Statistical Learning refers to a set of approaches for estimating ƒ```
> Why Estimate ƒ? <
two main reasons; prediction and inference
import pdb pdb.set_trace() import Y val = 0 Y.f()
