# 10 examples of 'linear_model.linearregression()' in Python

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``80def Linear_regression():81    # get train data82    data =np.loadtxt('data.csv',delimiter=',')8384    #define hyperparamters85    #learning_rate is used for update gradient86    #defint the number that will iteration87    # define  y =mx+b88    learning_rate = 0.00189    initial_b =0.090    initial_m = 0.091    num_iter = 10009293    #train model94    #print b m error95    print 'initial variables:\n initial_b = {0}\n intial_m = {1}\n error of begin = {2} \n'\96        .format(initial_b,initial_m,compute_error(initial_b,initial_m,data))9798    #optimizing b and m99    [b ,m] = optimizer(data,initial_b,initial_m,learning_rate,num_iter)100101    #print final b m error102    print 'final formula parmaters:\n b = {1}\n m={2}\n error of end = {3} \n'.format(num_iter,b,m,compute_error(b,m,data))103104    #plot result105    plot_data(data,b,m)``
``205def test_predict_2(self):206    X = np.array([[3.5]])207    m, n = X.shape208    intercept = np.ones((m, 1), dtype=np.int64)209    X = np.append(intercept, X, axis=1)210    theta = np.zeros((n + 1, 1), dtype=np.int64)211212    assert_allclose([[0]],213                    predict(X, theta),214                    rtol=0, atol=0.001)``
``187def linear_regression(x, y):188    """189    NOTE: Proceed linear regression190    Input191    - x: 1d timeseries (time)192    - y: time varying 2d field (time, lat, lon)193    Output194    - slope: 2d array, spatial map, linear regression slope on each grid195    - intercept: 2d array, spatial map, linear regression intercept on each grid196    """197    # get original global dimension198    lat = y.getLatitude()199    lon = y.getLongitude()200    # Convert 3d (time, lat, lon) to 2d (time, lat*lon) for polyfit applying201    im = y.shape[2]202    jm = y.shape[1]203    y_2d = y.reshape(y.shape[0], jm * im)204    # Linear regression205    slope_1d, intercept_1d = np.polyfit(x, y_2d, 1)206    # Retreive to cdms2 variabile from numpy array207    slope = MV2.array(slope_1d.reshape(jm, im))208    intercept = MV2.array(intercept_1d.reshape(jm, im))209    # Set lat/lon coordinates210    slope.setAxis(0, lat)211    slope.setAxis(1, lon)212    slope.mask = y.mask213    intercept.setAxis(0, lat)214    intercept.setAxis(1, lon)215    intercept.mask = y.mask216    # return result217    return slope, intercept``
``99def test (self) :100    for t in self.test_cases :101        h = self.__hypothesis ( t[0:-1] )102        print "H = %lf, ANS = %d" % ( h, t[self.__MAX_FEATURE_CNT])``
``59def linear_regression(x, y, init_mean=None, init_stddev=1.0):60  """Creates linear regression TensorFlow subgraph.6162  Args:63    x: tensor or placeholder for input features.64    y: tensor or placeholder for labels.65    init_mean: the mean value to use for initialization.66    init_stddev: the standard devation to use for initialization.6768  Returns:69    Predictions and loss tensors.7071  Side effects:72    The variables linear_regression.weights and linear_regression.bias are73    initialized as follows.  If init_mean is not None, then initialization74    will be done using a random normal initializer with the given init_mean75    and init_stddv.  (These may be set to 0.0 each if a zero initialization76    is desirable for convex use cases.)  If init_mean is None, then the77    uniform_unit_scaling_initialzer will be used.78  """79  with vs.variable_scope('linear_regression'):80    scope_name = vs.get_variable_scope().name81    summary.histogram('%s.x' % scope_name, x)82    summary.histogram('%s.y' % scope_name, y)83    dtype = x.dtype.base_dtype84    y_shape = y.get_shape()85    if len(y_shape) == 1:86      output_shape = 187    else:88      output_shape = y_shape[1]89    # Set up the requested initialization.90    if init_mean is None:91      weights = vs.get_variable(92          'weights', [x.get_shape()[1], output_shape], dtype=dtype)93      bias = vs.get_variable('bias', [output_shape], dtype=dtype)94    else:95      weights = vs.get_variable(96          'weights', [x.get_shape()[1], output_shape],97          initializer=init_ops.random_normal_initializer(98              init_mean, init_stddev, dtype=dtype),99          dtype=dtype)100      bias = vs.get_variable(101          'bias', [output_shape],102          initializer=init_ops.random_normal_initializer(103              init_mean, init_stddev, dtype=dtype),104          dtype=dtype)105    summary.histogram('%s.weights' % scope_name, weights)106    summary.histogram('%s.bias' % scope_name, bias)107    return losses_ops.mean_squared_error_regressor(x, y, weights, bias)``
``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)``
``64def logistic_regression(opytimizer):65    # Instanciating the model66    model = torch.nn.Sequential()6768    # Some model parameters69    n_features = 6470    n_classes = 107172    # Adding linear layer73    model.add_module("linear", torch.nn.Linear(74        n_features, n_classes, bias=False))7576    # Input variables77    batch_size = 10078    epochs = 1007980    # Gathering parameters from Opytimizer81    # Pay extremely attention to their order when declaring due to their bounds82    learning_rate = opytimizer[0][0]83    momentum = opytimizer[1][0]8485    # Declaring the loss function86    loss = torch.nn.CrossEntropyLoss(reduction='mean')8788    # Declaring the optimization algorithm89    opt = optim.SGD(model.parameters(), lr=learning_rate, momentum=momentum)9091    # Performing training loop92    for _ in range(epochs):93        # Initial cost as 0.094        cost = 0.09596        # Calculating the number of batches97        num_batches = len(X_train) // batch_size9899        # For every batch100        for k in range(num_batches):101            # Declaring initial and ending for each batch102            start, end = k * batch_size, (k + 1) * batch_size103104            # Cost will be the loss accumulated from model's fitting105            cost += fit(model, loss, opt,106                        X_train[start:end], Y_train[start:end])107108    # Predicting samples from evaluating set109    preds = predict(model, X_val)110111    # Calculating accuracy112    acc = np.mean(preds == Y_val)113114    return 1 - acc``
``8def linear_regression(feat1, feat2):9    return random.gauss(2 * feat1 + feat2 + 5, 3)``
``101def run_logistic_regression(df):102    # Logistic regression103    X = df['pageviews_cumsum']104    X = sm.add_constant(X)105    y = df['is_conversion']106    logit = sm.Logit(y, X)107    logistic_regression_results = logit.fit()108    print(logistic_regression_results.summary())109    return logistic_regression_results``
``87def calc_linear_regression(coeff, x):88    result = 089    for i in range(1, len(coeff)):90        result += x[i - 1] * coeff[i]9192    result += coeff[0]93    return result``