@@ -221,11 +221,11 @@ def get_parameters(self, n_batch = 10000,
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cx = np .floor (pos / self .r_stimulus )
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cy = pos % self .r_stimulus
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- results . mu_x [batch ] = cx / self .r_stimulus * max_radius * 2 - max_radius
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- results . mu_y [batch ] = - cy / self .r_stimulus * max_radius * 2 - max_radius
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- R = np .sqrt (results . mu_x [batch ]** 2 + results . mu_y [batch ]** 2 )
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+ results [ ' mu_x' ] [batch ] = cx / self .r_stimulus * max_radius * 2 - max_radius
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+ results [ ' mu_y' ] [batch ] = - cy / self .r_stimulus * max_radius * 2 - max_radius
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+ R = np .sqrt (results [ ' mu_x' ] [batch ]** 2 + results [ ' mu_y' ] [batch ]** 2 )
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P = np .hstack ((m_image , R ))
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- results . sigma [batch ] = np .matmul (P , beta )
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+ results [ ' sigma' ] [batch ] = np .matmul (P , beta )
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i = int (v / n_mask * 21 )
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sys .stdout .write ('\r ' )
@@ -248,11 +248,11 @@ def get_parameters(self, n_batch = 10000,
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cx = np .floor (pos / self .r_stimulus )
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cy = pos % self .r_stimulus
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- results . mu_x [batch ] = cx / self .r_stimulus * max_radius * 2 - max_radius
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- results . mu_y [batch ] = - cy / self .r_stimulus * max_radius * 2 - max_radius
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- R = np .sqrt (results . mu_x [batch ]** 2 + results . mu_y [batch ]** 2 )
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+ results [ ' mu_x' ] [batch ] = cx / self .r_stimulus * max_radius * 2 - max_radius
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+ results [ ' mu_y' ] [batch ] = - cy / self .r_stimulus * max_radius * 2 - max_radius
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+ R = np .sqrt (results [ ' mu_x' ] [batch ]** 2 + results [ ' mu_y' ] [batch ]** 2 )
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P = np .hstack ((m_image , R ))
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- results . sigma [batch ] = np .matmul (P , beta )
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+ results [ ' sigma' ] [batch ] = np .matmul (P , beta )
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i = int (v / n_mask * 21 )
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sys .stdout .write ('\r ' )
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