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1. 簡介
Matrix 類型繼承于 ndarray 類型，因此含有 ndarray 的所有數據屬性和方法。Matrix 類型與 ndarray 類型有六個重要的不同點，當你當 Matrix 對象當 arrays 操作時，這些不同點會導致非預期的結果。

1）Matrix 對象可以使用一個 Matlab 風格的字符串來創建，也就是一個以空格分隔列，以分號分隔行的字符串。

2）Matrix 對象總是二維的。這包含有深遠的影響，比如 m.ravel() 的返回值是二維的，成員選擇的返回值也是二維的，因此序列的行為與 array 會有本質的不同。

3）Matrix 類型的乘法覆蓋了 array 的乘法，使用的是矩陣的乘法運算。當你接收矩陣的返回值的時候，確保你已經理解這些函數的含義。特別地，事實上函數 asanyarray(m) 會返回一個 matrix，如果 m 是一個 matrix。

4）Matrix 類型的冪運算也覆蓋了之前的冪運算，使用矩陣的冪。根據這個事實，再提醒一下，如果使用一個矩陣的冪作為參數調用 asanarray(…) 跟上面的相同。

5）矩陣默認的 array_priority 是 10.0，因而 ndarray 和 matrix 對象混合的運算總是返回矩陣。

6）矩陣有幾個特有的屬性使得計算更加容易，這些屬性有：

(a) .T -- 返回自身的轉置

(b) .H -- 返回自身的共軛轉置

(c) .I -- 返回自身的逆矩陣

(d) .A -- 返回自身數據的 2 維數組的一個視圖（沒有做任何的拷貝）

Matrix 對象也可以使用其它的 Matrix 對象，字符串，或者其它的可以轉換為一個 ndarray 的參數來構造。另外，在 NumPy 里，“mat”是“matrix”的一個別名。
1）通過字符串創建矩陣

 a=np.mat(1 2 3; 4 5 3)
  print (a*a.T).I[[ 0.2924 -0.1345] [-0.1345 0.0819]]

2）通過嵌套列表創建矩陣

 mp.mat([[1,5,10],[1.0,3,4j]])
matrix([[ 1.+0.j, 5.+0.j, 10.+0.j], [ 1.+0.j, 3.+0.j, 0.+4.j]])

3）通過數組創建矩陣

 np.mat(random.rand(3,3)).T
matrix([[ 0.7699, 0.7922, 0.3294], [ 0.2792, 0.0101, 0.9219], [ 0.3398, 0.7571, 0.8197]])

2. 屬性與描述

namedescripeAReturn self as an ndarray object.A1Return self as a flattened ndarray.HReturns the (complex) conjugate transpose of self.IReturns the (multiplicative) inverse of invertible self.TReturns the transpose of the matrix.baseBase object if memory is from some other object.ctypesAn object to simplify the interaction of the array with the ctypes module.dataPython buffer object pointing to the start of the array’s data.dtypeData-type of the array’s elements.flagsInformation about the memory layout of the array.flatA 1-D iterator over the array.imagThe imaginary part of the array.itemsizeLength of one array element in bytes.nbytesTotal bytes consumed by the elements of the array.ndimNumber of array dimensions.realThe real part of the array.shapeTuple of array dimensions.sizeNumber of elements in the array.stridesTuple of bytes to step in each dimension when traversing an array.

3. 方法與描述

namedescribeall([axis, out])Test whether all matrix elements along a given axis evaluate to True.any([axis, out])Test whether any array element along a given axis evaluates to True.argmax([axis, out])Indexes of the maximum values along an axis.argmin([axis, out])Indexes of the minimum values along an axis.argpartition(kth[, axis, kind, order])Returns the indices that would partition this array.argsort([axis, kind, order])Returns the indices that would sort this array.astype(dtype[, order, casting, subok, copy])Copy of the array, cast to a specified type.byteswap(inplace)Swap the bytes of the array elementschoose(choices[, out, mode])Use an index array to construct a new array from a set of choices.clip([min, max, out])Return an array whose values are limited to [min, max].compress(condition[, axis, out])Return selected slices of this array along given axis.conj()Complex-conjugate all elements.conjugate()Return the complex conjugate, element-wise.copy([order])Return a copy of the array.cumprod([axis, dtype, out])Return the cumulative product of the elements along the given axis.cumsum([axis, dtype, out])Return the cumulative sum of the elements along the given axis.diagonal([offset, axis1, axis2])Return specified diagonals.dot(b[, out])Dot product of two arrays.dump(file)Dump a pickle of the array to the specified file.dumps()Returns the pickle of the array as a string.fill(value)Fill the array with a scalar value.flatten([order])Return a flattened copy of the matrix.getA()Return self as an ndarray object.getA1()Return self as a flattened ndarray.getH()Returns the (complex) conjugate transpose of self.getI()Returns the (multiplicative) inverse of invertible self.getT()Returns the transpose of the matrix.getfield(dtype[, offset])Returns a field of the given array as a certain type.item(*args)Copy an element of an array to a standard Python scalar and return it.itemset(*args)Insert scalar into an array (scalar is cast to array’s dtype, if possible)max([axis, out])Return the maximum value along an axis.mean([axis, dtype, out])Returns the average of the matrix elements along the given axis.min([axis, out])Return the minimum value along an axis.newbyteorder([new_order])Return the array with the same data viewed with a different byte order.nonzero()Return the indices of the elements that are non-zero.partition(kth[, axis, kind, order])Rearranges the elements in the array in such a way that value of the element in kth position prod([axis, dtype, out]) Return the product of the array elements over the given axis.ptp([axis, out])Peak-to-peak (maximum – minimum) value along the given axis.put(indices, values[, mode])Set a.flat[n] = values[n] for all n in indices.ravel([order])Return a flattened matrix.repeat(repeats[, axis])Repeat elements of an array.reshape(shape[, order])Returns an array containing the same data with a new shape.resize(new_shape[, refcheck])Change shape and size of array in-place.round([decimals, out])Return a with each element rounded to the given number of decimals.searchsorted(v[, side, sorter])Find indices where elements of v should be inserted in a to maintain order.setfield(val, dtype[, offset])Put a value into a specified place in a field defined by a data-type.setflags([write, align, uic])Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively.sort([axis, kind, order])Sort an array, in-place.squeeze([axis])Return a possibly reshaped matrix.std([axis, dtype, out, ddof])Return the standard deviation of the array elements along the given axis.sum([axis, dtype, out])Returns the sum of the matrix elements, along the given axis.swapaxes(axis1, axis2)Return a view of the array with axis1 and axis2 interchanged.take(indices[, axis, out, mode])Return an array formed from the elements of a at the given indices.tobytes([order])Construct Python bytes containing the raw data bytes in the array.tofile(fid[, sep, format])Write array to a file as text or binary (default).tolist()Return the matrix as a (possibly nested) list.tostring([order])Construct Python bytes containing the raw data bytes in the array.trace([offset, axis1, axis2, dtype, out])Return the sum along diagonals of the array.transpose(*axes)Returns a view of the array with axes transposed.var([axis, dtype, out, ddof])Returns the variance of the matrix elements, along the given axis.view([dtype, type])New view of array with the same data.

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