5 examples of 'python multiple linear regression' in Python

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80def Linear_regression():
81 # get train data
82 data =np.loadtxt('data.csv',delimiter=',')
83
84 #define hyperparamters
85 #learning_rate is used for update gradient
86 #defint the number that will iteration
87 # define y =mx+b
88 learning_rate = 0.001
89 initial_b =0.0
90 initial_m = 0.0
91 num_iter = 1000
92
93 #train model
94 #print b m error
95 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))
97
98 #optimizing b and m
99 [b ,m] = optimizer(data,initial_b,initial_m,learning_rate,num_iter)
100
101 #print final b m error
102 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))
103
104 #plot result
105 plot_data(data,b,m)
187def linear_regression(x, y):
188 """
189 NOTE: Proceed linear regression
190 Input
191 - x: 1d timeseries (time)
192 - y: time varying 2d field (time, lat, lon)
193 Output
194 - slope: 2d array, spatial map, linear regression slope on each grid
195 - intercept: 2d array, spatial map, linear regression intercept on each grid
196 """
197 # get original global dimension
198 lat = y.getLatitude()
199 lon = y.getLongitude()
200 # Convert 3d (time, lat, lon) to 2d (time, lat*lon) for polyfit applying
201 im = y.shape[2]
202 jm = y.shape[1]
203 y_2d = y.reshape(y.shape[0], jm * im)
204 # Linear regression
205 slope_1d, intercept_1d = np.polyfit(x, y_2d, 1)
206 # Retreive to cdms2 variabile from numpy array
207 slope = MV2.array(slope_1d.reshape(jm, im))
208 intercept = MV2.array(intercept_1d.reshape(jm, im))
209 # Set lat/lon coordinates
210 slope.setAxis(0, lat)
211 slope.setAxis(1, lon)
212 slope.mask = y.mask
213 intercept.setAxis(0, lat)
214 intercept.setAxis(1, lon)
215 intercept.mask = y.mask
216 # return result
217 return slope, intercept
59def linear_regression(x, y, init_mean=None, init_stddev=1.0):
60 """Creates linear regression TensorFlow subgraph.
61
62 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.
67
68 Returns:
69 Predictions and loss tensors.
70
71 Side effects:
72 The variables linear_regression.weights and linear_regression.bias are
73 initialized as follows. If init_mean is not None, then initialization
74 will be done using a random normal initializer with the given init_mean
75 and init_stddv. (These may be set to 0.0 each if a zero initialization
76 is desirable for convex use cases.) If init_mean is None, then the
77 uniform_unit_scaling_initialzer will be used.
78 """
79 with vs.variable_scope('linear_regression'):
80 scope_name = vs.get_variable_scope().name
81 summary.histogram('%s.x' % scope_name, x)
82 summary.histogram('%s.y' % scope_name, y)
83 dtype = x.dtype.base_dtype
84 y_shape = y.get_shape()
85 if len(y_shape) == 1:
86 output_shape = 1
87 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.shape
208 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)
211
212 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])

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