# 5 examples of 'logistic regression sklearn' in Python

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``89def logistic_regression(w, x):90    """Logistic regression classifier model.9192    w: Weights w. (n_features,) NumPy array93    x: Data point x_i. (n_features,) NumPy array94    -> float in [0, 1]95    """96    return scipy.special.expit(numpy.dot(x, w.T))``
``94def Logistic_Regression(X,Y,alpha,theta,num_iters):95	m = len(Y)96	for x in xrange(num_iters):97		new_theta = Gradient_Descent(X,Y,theta,m,alpha)98		theta = new_theta99		if x % 100 == 0:100			Cost_Function(X,Y,theta,m)101			print 'theta ', theta	102			print 'cost is ', Cost_Function(X,Y,theta,m)103	Declare_Winner(theta)``
``158def logistic_regression_3(X, y, max_iter : int = 100, learning_rate : float = 0.1):159    W = np.zeros((np.size(X, 1), np.size(y, 1)))160    for _ in range(max_iter):161        N = len(y)162        index = np.random.permutation(N)163        X = X[index]164        y = y[index]165        W_prev = np.copy(W)166        y_pred = softmax(X[0:10][:] @ W)167        grad = X[0:10][:].T @ (y_pred - y[0:10])168        W -= learning_rate * grad169        if np.allclose(W, W_prev):170            break171    return W``
``1447def predict_proba(self, X):1448    """1449    Probability estimates.14501451    The returned estimates for all classes are ordered by the1452    label of classes.14531454    For a multi_class problem, if multi_class is set to be "multinomial"1455    the softmax function is used to find the predicted probability of1456    each class.1457    Else use a one-vs-rest approach, i.e calculate the probability1458    of each class assuming it to be positive using the logistic function.1459    and normalize these values across all the classes.14601461    Parameters1462    ----------1463    X : array-like of shape (n_samples, n_features)1464        Vector to be scored, where `n_samples` is the number of samples and1465        `n_features` is the number of features.14661467    Returns1468    -------1469    T : array-like of shape (n_samples, n_classes)1470        Returns the probability of the sample for each class in the model,1471        where classes are ordered as they are in ``self.classes_``.1472    """1473    check_is_fitted(self)14741475    ovr = (self.multi_class in ["ovr", "warn"] or1476           (self.multi_class == 'auto' and (self.classes_.size <= 2 or1477                                            self.solver == 'liblinear')))1478    if ovr:1479        return super()._predict_proba_lr(X)1480    else:1481        decision = self.decision_function(X)1482        if decision.ndim == 1:1483            # Workaround for multi_class="multinomial" and binary outcomes1484            # which requires softmax prediction with only a 1D decision.1485            decision_2d = np.c_[-decision, decision]1486        else:1487            decision_2d = decision1488        return softmax(decision_2d, copy=False)``
``139def _regress(self, x, y, alpha):140    kw = self.ridge_kw or {}141    coef = ridge_regression(x, y, alpha, **kw)142    self.iters += 1143    return coef``