# 5 examples of 'python multiple linear regression' 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)``
``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``
``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)``
``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)``
``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])``